Powered ankle-foot exoskeletons can reduce the metabolic cost of human walking to below normal levels, but optimal assistance properties remain unclear. The purpose of this study was to test the effects of different assistance timing and power characteristics in an experiment with a tethered ankle-foot exoskeleton.
Ten healthy female subjects walked on a treadmill with bilateral ankle-foot exoskeletons in 10 different assistance conditions. Artificial pneumatic muscles assisted plantarflexion during ankle push-off using one of four actuation onset timings (36, 42, 48 and 54% of the stride) and three power levels (average positive exoskeleton power over a stride, summed for both legs, of 0.2, 0.4 and 0.5 W∙kg−1). We compared metabolic rate, kinematics and electromyography (EMG) between conditions.
Optimal assistance was achieved with an onset of 42% stride and average power of 0.4 W∙kg−1, leading to 21% reduction in metabolic cost compared to walking with the exoskeleton deactivated and 12% reduction compared to normal walking without the exoskeleton. With suboptimal timing or power, the exoskeleton still reduced metabolic cost, but substantially less so. The relationship between timing, power and metabolic rate was well-characterized by a two-dimensional quadratic function. The assistive mechanisms leading to these improvements included reducing muscular activity in the ankle plantarflexors and assisting leg swing initiation.
These results emphasize the importance of optimizing exoskeleton actuation properties when assisting or augmenting human locomotion. Our optimal assistance onset timing and average power levels could be used for other exoskeletons to improve assistance and resulting benefits.
Walking is the most frequent means of human locomotion . While humans use many strategies to reduce energy expenditure , walking still requires a considerable amount of metabolic energy, sometimes referred to as the 'metabolic cost' of walking. Assisting the ankle joint with an exoskeleton can reduce the metabolic cost of walking to below the cost of normal walking [3–6]. This shows that it is possible to reduce metabolic cost through robotic assistance.
Reductions in the metabolic cost of walking with ankle-foot exoskeletons result from two competing factors. A benefit can be derived from the exoskeleton when it acts to assist gait, expressed as the difference between powered exoskeleton1 walking and walking in zero-work mode1. However, wearing the exoskeleton in zero-work mode typically results in a metabolic penalty, expressed as the difference between normal walking1 without an exoskeleton and walking in zero-work mode. Some full-body exoskeletons have resulted in large metabolic penalties (e.g. ) while lightweight ankle-foot exoskeletons have resulted in penalties of less than 3% for active autonomous1 exoskeletons  and even close to zero for passive autonomous1 exoskeletons . Reducing the penalty of wearing an exoskeleton in zero-work mode is mainly a design challenge, while increasing the difference between the zero-work condition and powered exoskeleton conditions is mainly a biomechanics challenge.
In order to solve the latter human-exoskeleton interaction challenge, optimal assistance properties (e.g. actuation timing, assistance magnitude, etc.) are crucial to further reduce the metabolic energy cost of walking. Malcolm et al.  showed that the timing of exoskeleton actuation onset (referred to as actuation timing) is an important exoskeleton property that influences the metabolic cost of walking with active exoskeletons. They found a convex landscape in metabolic cost versus actuation timing with an optimum around 40% of the stride. Studies that have found the highest reductions in metabolic energy cost have also used an actuation timing around 40% of the stride [4, 6].
Of course, actuation timing is not the only determinant of metabolic cost when walking with ankle-exoskeleton assistance. Assistance magnitude also seems to have a strong effect [8, 9]. The average positive mechanical exoskeleton power per stride summed for both ankles (referred to here as exoskeleton power) can be as high as 0.38 W∙kg−1 resulting in reductions in net metabolic cost of between 10 and 22% for powered exoskeleton conditions compared to zero-work conditions [3, 4, 6, 8, 10–12]. However, comparing these studies does not result in a clear relationship between exoskeleton power magnitude and metabolic cost, likely because many factors differ between studies (e.g. design, exoskeleton mass, actuation profile, etc.), confounding comparisons. The simplest walking model  would suggest that increasing exoskeleton power will reduce the mechanical energy requirements for walking until subjects walk with zero metabolic cost. Indeed, a recent study, in which both ankle and hip joints were assisted with a soft exo-suit  indicated that metabolic energy cost reduces linearly with increasing exoskeleton assistance magnitude, similar to some findings with active prostheses . On the other hand, a study on unilateral exoskeleton assistance suggested an exponential relationship between device power and metabolic cost . Experiments and simulation studies with exoskeletons have similarly suggested that under some conditions "more is not always better" [5, 15, 16]. Interpretations have been made more difficult by the limited range of attainable levels of exoskeleton power, which has often been between 50 and 80% of biological ankle power [4, 10, 11].
In order to study if and when the reduction in the metabolic cost of walking begins to level-off with increasing exoskeleton power during bilateral exoskeleton assistance, it seems necessary to deliver more power than in current studies. To identify the influence of exoskeleton power magnitude on the metabolic cost of walking, as well as the interaction with actuation timing, there is a need for a parametric study of actuation timing and exoskeleton power over a larger range. A study of both actuation characteristics is also expected to contribute to an improved understanding of the assistive mechanisms of ankle-foot exoskeletons. Several studies have indicated that other joints besides the ankle joint are involved in the reduction in metabolic cost experienced when using an ankle-foot exoskeleton [3–5, 8, 12, 17, 18] but the exact mechanisms are unclear. Exploring different assistance parameters over a broad range would help to identify the relationship between biomechanical changes and the resulting changes in metabolic cost.
The overall goal of this study was to characterize the relationship between ankle exoskeleton power, actuation timing, and metabolic cost during walking over a broad range. We used a tethered and powered plantarflexion-assisting exoskeleton to vary actuation onset timing and average exoskeleton power independently and over a broad range and studied the influence of these characteristics on the metabolic energy cost of walking. We expected a second-order effect of actuation timing on metabolic energy cost  and explored several candidate relationships between exoskeleton power and metabolic energy cost to evaluate the interaction between timing, power and metabolic cost. A secondary goal was to use the best relationship to define optimal assistance parameters. Finally, we analyzed muscle activation, exoskeleton kinetics and walking kinematics that describe the neuromechanical interaction between the exoskeleton and the human, with the goal of explaining the reduction in metabolic cost and improving our understanding of human-exoskeleton interaction.
Fourteen female subjects participated in the experiment from which ten were retained (age 23 ± 1.2 y; weight 61.0 ± 4.5 kg; height 168.1 ± 5.2 cm; European shoe size 38.6 ± 0.8). Non-inclusions were due to technical failure of the exoskeleton (two subjects), drop-out (one subject) and errors in data synchronization (one subject). We selected female subjects of normal height and weight to be able to use one exoskeleton size for all subjects and to achieve relatively large amounts of exoskeleton power normalized to bodyweight. None of the subjects had prior experience using an exoskeleton. All participants provided written informed consent prior to participation. The experimental protocol was approved by the ethical committee of the Ghent University Hospital.
The bilateral exoskeleton (Fig. 1) consisted of an ankle-foot orthosis at each leg with a hinge at the ankle joint and pneumatic muscles [3, 17–19]. The pneumatic muscles were 0.27 m in length and were connected between the foot segment and the shank segment. They 'contracted' when inflated with compressed air. The locations of the pneumatic muscles insertions were individually adjusted such that they allowed 15° of dorsiflexion when passive. The exoskeleton fitted inside standard sport shoes. Footswitches (Multimec 5E/5G, Mec, Ballerup, Denmark) were built in to detect foot contact. Load cells (100 Hz; 210 Series, Richmond Industries Ltd., Reading, United Kingdom) were connected between the orthoses and the pneumatic muscles to measure the force of the pneumatic muscles. Linear displacement sensors (100 Hz; SLS130, Penny&Giles, Christchurch, United Kingdom) were connected between the foot and shank sections of the exoskeletons to measure ankle joint angles . The total weight of one tethered exoskeleton including all sensors was 0.890 kg (0.680 kg for the orthosis, 0.030 kg for the displacement sensor, 0.110 kg for the pneumatic muscle and 0.070 kg for the load cell). Additional hardware, including an air supply, was placed next to the treadmill.
Based on footswitch signals from the previous stride, exoskeleton actuation for the next stride was controlled using fixed percentages of stride time with a feedforward algorithm in Labview (National Instruments, Austin, TX, USA) [3, 17–19]. To control the amount of exoskeleton mechanical power, air pressure in the pneumatic muscles was adjusted using an iterative learning algorithm [9, 21]. For exoskeleton real-time control, average positive exoskeleton ankle joint mechanical power was measured in real-time using the load cell and the linear displacement sensor (Fig. 1). Based on a prior calibration with motion capture, ankle joint angle and moment arm of the pneumatic muscles were estimated based on linear displacement . A moving average of positive exoskeleton power with a window of 10 strides was then used as the input for the iterative learning algorithm. Over the course of many steps, the algorithm slowly increased or decreased air pressure to the pneumatic muscles when average power was too low or too high, respectively, in order to achieve a desired amount of average exoskeleton power during walking.
The experiment consisted of a habituation session and a data collection session with roughly one week in between. A total of 14 conditions were applied in each session: a NormalWalking condition, in which subjects walked with normal shoes without an exoskeleton; a ZeroWork condition, in which subjects walked with the exoskeleton but without assistance from the pneumatic muscles; and 12 powered exoskeleton conditions. The 12 powered conditions were based on a two dimensional parameter sweep of four actuation onset timings (actuation timings) and three average positive exoskeleton ankle joint mechanical power levels (exoskeleton powers) (Fig. 2). Four timing values where applied with actuation onset timing of 36 ± 1%, 42 ± 1%, 48 ± 1% and 54 ± 1% of the stride (referred to as Earliest, Early, Late and Latest, respectively) and with actuation always ending at 64 ± 1% of the stride. These values were chosen to be close to the optimal actuation onset timing from a previous study . Three exoskeleton power levels were applied, with values of 0.21 ± 0.02 W∙kg−1, 0.41 ± 0.03 W∙kg−1 and the maximum achievable amount with our exoskeleton and control methods, which was 0.50 ± 0.06 W∙kg−1 (referred to as Low, Medium and High, respectively). These values were chosen based on the capabilities of our exoskeleton testbed, and coincided with approximately 50, 100 and 125% of the net biological ankle work observed during normal walking [4, 10, 11]. This exceeded the largest range of prior exoskeleton studies . It was not possible to apply more than 0.2 W∙kg−1 with Latest timing due to bandwidth limitations of the pneumatic actuators. Lower amounts of power with this timing did not appear to be useful. We therefore chose to include only 10 powered exoskeleton conditions in our analysis: three actuation timings (Earliest, Early and Late) for which three power levels were applied (Low, Medium and High) and a fourth actuation timing (Latest) for which only one power level was applied (Low) (Fig. 2).
In the habituation session, subjects learned to walk with the exoskeleton and became accustomed to the experimental set-up. They walked in all powered conditions, the ZeroWork condition and the NormalWalking condition on the treadmill at 1.25 m∙s−1. All conditions lasted three minutes with two minutes of rest in between. Conditions were applied in random order, apart from the NormalWalking condition which was always first or last due to the time to don and doff the exoskeleton.
Experimental data were collected on a separate day, about a week later. Each subject performed the same walking protocol as in the habituation session, but conditions lasted for four minutes to reach steady-state metabolic rate. A standing rest condition in which subjects stood still for four minutes was also applied before the walking conditions to capture resting metabolic rate. Air pressure of the pneumatic muscles at the beginning of each powered condition was set to be the same as the air pressure from the end of each powered condition in the habituation session, such that the iterative learning algorithm did not need to change air pressure substantially during powered exoskeleton conditions on the data collection day.
Subjects wore a face mask connected to a gas analysis system that measured O2 consumption and CO2 production continuously (Cosmed, K4b2, Rome, Italy) (Fig. 1). Exoskeleton sensors (footswitches, displacement sensors and load cells) also measured continuously during the entire experiment (Fig. 1). Full body 3D kinematics were recorded with 51 reflective markers (four on each foot, two on each exoskeleton foot segment, two on each exoskeleton ankle joint, six on each exoskeleton shank segment, two on each knee joint, four on a plate connected to each thigh, six on the pelvis, and five on the torso) and 14 infrared cameras (200 Hz; Pro Reflex, Qualisys AB, Gothenburg, Sweden) with Qualisys software. Surface electromyography (EMG) of the m. (Musculus) soleus, m. gastrocnemius medialis, m. tibialis anterior, m. vastus lateralis, m. rectus femoris, m. biceps femoris and m. gluteus maximums of both legs were measured with bipolar surface electrodes and wireless transmitters (1000 Hz; ZeroWire, Noraxon, Scottsdale, AZ, USA). Electrodes were placed in accordance with SENIAM guidelines . For the m. soleus and m. gastrocnemius medialis, holes were cut in the orthosis on the approximate locations. Marker data and surface EMG data were collected for 10s during the last minute of each condition, which included around nine full strides.
Metabolic energy cost of walking was the primary study outcome. It was estimated based on O2 consumption and CO2 production during the last two minutes of each condition using a standard equation . Net metabolic cost was calculated by subtracting the metabolic cost of standing at rest from gross metabolic cost during walking and was normalized to body weight.
All time series data presented in figures were time normalized from heel contact to the next heel contact of the same leg, averaged for the left and right leg and averaged across subjects to calculate population stride averages.
Kinematics were analyzed to evaluate the effect of exoskeleton assistance on walking patterns. An eight segment model (two feet, two shanks, two thighs, one pelvis and one torso) was used to calculate sagittal plane joint angles with Visual 3D software (C-Motion, MD, USA). Dempster's regression equations  were used to define segment masses, with some alterations: the mass of the arms and head were added to the torso and the mass of the foot and shank sections of the exoskeleton were added to the foot and shank segments, respectively, of the model for the exoskeleton conditions. Heel contact and toe-off were automatically detected using foot kinematics . Toe-off timing was expressed as a percentage of the stride time. Time between consecutive heel contacts was multiplied with treadmill speed to calculate step length.
Exoskeleton kinetics were calculated to assess actuation timing and exoskeleton power. Marker positions and pneumatic muscle force data were filtered with a Butterworth low-pass filter with a cut-off frequency of 12 Hz. Ankle joint angle and moment arm of the pneumatic muscles were calculated using motion capture. Ankle joint angular velocity was calculated as the first derivative of the ankle joint angle in time. Moment arm of the pneumatic muscle force was calculated frame-by-frame as the minimum distance between the ankle joint axis and the pneumatic muscle centroid. Pneumatic muscle force, measured via a load cell, was multiplied with the moment arm to calculate exoskeleton torque and multiplied with ankle joint angular velocity to calculate instantaneous exoskeleton mechanical power. Actuation onset timing was calculated based on the maximum of the second derivative of the unfiltered exoskeleton torque, which provides a robust measure of torque onset . Exoskeleton power was calculated as the numerical integration of positive instantaneous exoskeleton power over the stride, divided by the stride time and summed for both legs. We chose to sum average exoskeleton power for both legs in order to facilitate comparisons to the reduction in body metabolic rate resulting from assistance.
EMG data were measured to describe the neuromuscular interaction between the exoskeleton and the user. Surface EMG data were band pass filtered (50–450 Hz) and rectified. A moving root mean square with a window of 100 ms was then applied. All EMG values were normalized to the peak value of the ZeroWork condition so that the peak value in the ZeroWork condition represents a value of 100. For all muscle groups, peak values were calculated for instances where a clear activation burst was present in the stride. For the m. tibialis anterior this was the peak value in the beginning of the stance phase (between 1 and 62% of the stride) and in the beginning of the swing phase (between 62 and 80% of the stride). For the m. soleus this was the peak value in the stance phase (between 1 and 62% of the stride) and in the beginning of the swing phase (between 62 and 80% of the stride). For the m. gastrocnemius medialis this was the peak value during the stance phase (between 1 and 62% of the stride). For the m. rectus femoris this was the peak value in the stance phase (between 1 and 62% of the stride) and the peak value around toe-off (between 40 and 80% of the stride). For the m. biceps femoris this was the peak value during the swing phase (between 62 and 100% of the stride). For the m. vastus lateralis and the m. gluteus maximus this was the peak value during the stance phase (between 1 and 62% of the stride).
Center-of-mass power, which is calculated by the dot product of center-of-mass velocity and ground reaction force, can be used to distinguish four functional phases of stance during walking: collision, rebound, pre-load and push-off [26, 27]. In prior studies, the rebound phase has been observed to covary with metabolic cost [5, 8, 9, 28–30]. Rebound work is calculated as the area under the positive burst of center-of-mass power during the single stance phase. It represents the positive center-of-mass work that is done by leg straightening, possible elastic rebound of the knee, and hip work that moves the swing leg . We calculated center-of-mass velocity and acceleration by taking the first and second derivative of center-of-mass position in the treadmill belt reference frame. Ground reaction force was estimated based on center-of-mass acceleration and body mass. This rough approximation of the ground reaction force can replace the direct ground reaction force measurement when no force plate is available . While data from double support could not be usefully interpreted , it was possible to retain center-of-mass power during the rebound phase (positive burst during the single stance phase) and to calculate average rebound power as the numerical integration of rebound power, divided by stride time. We expressed average rebound power in W∙kg−1.
To evaluate steady state in metabolic rate measurements, a repeated measures analysis of variance (ANOVA) was performed between sequential 30s averages from the four minute walking conditions with post-hoc paired t-tests (SPSS Statistics 21, IMB, Armonk, NY, USA). Other statistical analyses were performed with Matlab (MathWorks, Natick, MA, USA). Repeated measures ANOVA (P ≤ 0.05) were performed to establish differences in metabolic cost, step-time parameters, EMG metrics and center-of-mass metrics between all conditions. When a statistical difference was found between conditions, pairwise comparisons were performed using post-hoc paired t-tests (P ≤ 0.05) to search for differences between the powered exoskeleton conditions (n = 10) and the ZeroWork condition and between the powered exoskeleton conditions (n = 10) and the NormalWalking condition. We used a Šídák-Holm correction for multiple testing  for these 20 comparisons. We did not compare the 10 powered exoskeleton conditions with each other using paired t-tests. Instead, when significant differences were found between one or more powered exoskeleton conditions and the ZeroWork condition or between one or more powered exoskeleton conditions and the NormalWalking condition, we used a regression analysis to express changes in specific outcome parameters resulting from varying actuation timing and power magnitude levels.
Where ΔE is the change in net metabolic cost compared to the ZeroWork condition (e.g. 0.51 W∙kg−1); Ton is actuation onset (e.g. 43%); and Pavg is exoskeleton power (e.g. 0.2 W∙kg−1).
Statistical tests for the effect of actuation timing and exoskeleton power on metabolic cost, EMG metrics and center-of-mass average rebound power were performed through this regression analysis (P ≤ 0.05).
Subjects walked in 10 powered exoskeleton conditions (based on combinations of the four different actuation timings (Earliest, Early, Late, Latest) and three different exoskeleton power levels (Low, Medium, High)), a ZeroWork condition and a NormalWalking condition. The distribution of actuation timing and exoskeleton power shows that the desired parameter space was successfully covered (Fig. 2) and that the powered conditions were clearly distinguishable from each other. Earlier actuation timings resulted in an earlier onset of pneumatic muscle force (Additional file 2: Figure S2) and an earlier onset of exoskeleton torque (Fig. 3). Increased air pressure in the pneumatic muscles (Additional file 2: Figure S2) increased exoskeleton peak torque in conditions with more exoskeleton power (Fig. 3). This resulted in an earlier plantarflexion onset with earlier actuation timings and higher peak plantarflexion angles in conditions with more exoskeleton power (Fig. 3). Step length (P = 0.01) and toe-off timing (P = 0.01) showed small differences between conditions but were very similar overall. Step length was 0.68 ± 0.01 m on average across all conditions and toe-off timing occurred at 62 ± 1% of stride on average across all conditions.
Subjects' metabolic cost reached steady state in the last two minutes of each walking condition (Additional file 3: Figure S3). A significant difference was found for net metabolic cost between conditions (P < 0.001). Net metabolic cost of all powered conditions was significantly lower than that in the ZeroWork condition (4.03 ± 0.74 W∙kg−1) (Fig. 4 and Additional file 4: Figure S4). The lowest net metabolic cost was found in the Early-Medium condition (3.16 ± 0.55 W∙kg−1), with a reduction of 21.4 ± 5.6% compared to ZeroWork and 12.3 ± 9.3% compared to NormalWalking (3.60 ± 0.74 W∙kg−1). In the Early-Medium condition, actuation onset was 42.3 ± 0.8% of stride and average power was 0.42 ± 0.03 W∙kg−1. The metabolic penalty of wearing the exoskeleton in ZeroWork compared to NormalWalking was 11.6 ± 9.6%. Although the metabolic cost of powered exoskeleton walking was lower in almost all conditions compared to NormalWalking, only the difference in the Early-Medium condition was statistically significant (Fig. 4 and Additional file 4: Figure S4). The ratio of the change in metabolic rate to exoskeleton mechanical power for the Early-Medium condition, was 2.1 ± 0.8. Higher ratios were found in the Early-Low and Late-Low conditions, with values of 3.1 ± 0.9 and 3.1 ± 1.4, respectively. Earlier or later timings or higher amounts of exoskeleton power resulted in lower ratios.
Regression analyses established strong relationships between actuation timing (Ton), exoskeleton power (Pavg) and the reduction in metabolic cost (ΔE). Consistent with the choice for a quadratic relationship for both actuation timing and exoskeleton power (Table 1), metabolic cost was higher in all Low and High conditions compared to the corresponding Medium conditions and metabolic cost was higher in all Earliest and Late conditions compared to the Early condition (Fig. 4 and Additional file 4: Figure S4). The resulting regression for the metabolic cost was statistically significant (P <0.001) and predicted experimental values well (R2 = 0.99) (Table 1). This 2D non-linear model combining actuation timing and average exoskeleton power therefore provided a strong estimate of the reduction in metabolic cost of walking with this exoskeleton. The regression suggests metabolic cost is minimized when actuation timing is 41% and exoskeleton power is 0.41 W∙kg−1, close to the Early-Medium condition (Fig. 4). There did not appear to be substantial interactions between optimal timing and optimal power; an onset of 41% stride minimized cost for all power levels, and a power level of 0.41 W∙kg−1 minimized cost for all timings (Additional file 4: Figure S4). Individual regressions sometimes deviated from the average profile (Additional file 1: Figure S1).
Changes in patterns of muscle activity may help illustrate the function of specific muscles during walking and how their activity changes with exoskeleton assistance (Figs. 5, 6, 7, 8, 9 and 10). The tibialis anterior muscle showed a large increase in muscle activity at the beginning of the stance phase and at the beginning of the swing phase in the powered conditions (Fig. 5a). Peak EMG during the beginning of the stance phase was significantly higher for almost all Medium and High power conditions compared to ZeroWork and NormalWalking. The regression analysis indicated that higher amounts of exoskeleton power resulted in bigger increases in m. tibialis anterior EMG (Fig. 5b). Peak EMG in the beginning of the swing phase was significantly higher for all powered conditions compared to ZeroWork and NormalWalking, with increases of more than 100% in several powered conditions. Regression analysis suggested that more exoskeleton power resulted in higher m. tibialis anterior activity (Fig. 5c).
Exoskeleton assistance reduced m. soleus activity during ankle push-off but seemed to increase EMG activity during the swing phase (Fig. 6). Peak muscle activity in the stance phase was reduced by more than 30% compared to ZeroWork and NormalWalking with early actuation onset and high exoskeleton power. The regression analysis indicated that m. soleus activity was most reduced in conditions with early actuation timings and high amounts of power. Soleus activity in the beginning of the swing phase increased slightly, possibly the result of crosstalk with the tibialis anterior muscle , as not much m. soleus activity is expected in this phase of the gait cycle. This is further suggested by the similarity between the regression analysis for both m. soleus and m. tibialis anterior peak EMG activity during the swing phase. During push-off however, tibialis anterior muscle activity was negligible and the measured signal probably originated only from the soleus muscle.
Peak gastrocnemius medialis EMG during push-off seemed to be reduced compared with both the NormalWalking and the ZeroWork condition (Fig. 7). However, differences with the ZeroWork condition were not statistically significant. Several powered conditions showed peak EMG activity that was more than 20% lower than in the NormalWalking condition. Regression analysis showed that peak m. gastrocnemius EMG was most reduced with late actuation timings and high amounts of power.
Activity in the m. rectus femoris showed a peak in the beginning of the stance phase for all conditions and in the beginning of the swing phase in the NormalWalking and ZeroWork conditions. No significant differences were found for peak EMG between conditions in the beginning of the stance phase (P = 0.23). Peak EMG activity was substantially reduced in the swing phase (Fig. 8) by as much as 40% compared to ZeroWork for all conditions. However, differences between powered conditions were rather small, which was emphasized by the shape of the regression which did not show a clear effect of actuation timing and exoskeleton power.
Activity in the m. biceps femoris showed a peak in the end of the swing phase (Fig. 9). The EMG peak activity during the swing phase seemed lower in the powered conditions compared to the NormalWalking and ZeroWork conditions. Differences in peak EMG compared to the ZeroWork condition were only statistically significant in a few conditions. Regression analysis showed a strong relationship, with the highest reductions for intermediate timing values and Medium power levels.
Peak EMG activity during the stance phase in the m. vastus lateralis and the m. gluteus maximus did not show significant differences between the powered conditions and the NormalWalking or ZeroWork conditions (Fig. 10).
Center-of-mass rebound power was significantly lower in almost all powered conditions compared to the ZeroWork condition. The regression analysis showed that average center-of-mass rebound work was reduced for high levels of exoskeleton power and early timings (Fig. 11) and had a pattern similar to that of the metabolic cost (Fig. 4).
The overall goal of this study was to determine the relationship between metabolic cost, exoskeleton actuation timing and exoskeleton power in an ankle-foot exoskeleton powered with pneumatic artificial muscles. We found strong effects of both terms, well-characterized by a two-dimensional quadratic function that suggested optimal actuation timing to be around 42% of stride and optimal average exoskeleton power to be around 0.4 W∙kg−1 summed for both legs (Fig. 4). One of our powered exoskeleton conditions, with actuation onset at 42.3 ± 0.8% of the stride and average exoskeleton power of 0.42 ± 0.03 W∙kg−1 summed for both legs, indeed resulted in a reduction in net metabolic cost of 21.4 ± 5.6% compared to walking with the exoskeleton without assistance from the pneumatic muscles. These reductions are similar to the highest reported results to date . Benefits were offset by a penalty of 11.6 ± 9.6% for wearing the exoskeleton while inactive, a higher penalty than with some other recent exoskeletons [4, 5]. Optimal timing and power thereby reduced net metabolic cost by 12.3 ± 9.3% compared to normal walking, which is the highest reported reduction for an ankle exoskeleton compared to normal walking.
Our regression analysis indicated an optimal actuation onset of around 42% of the stride: when actuation timing was independently varied, metabolic cost followed a convex pattern. In each of the Low, Medium and High power conditions, metabolic cost had a U-shaped relationship with actuation timing (Fig. 4 and Additional file 4: Figure S4). Given that there did not seem to be an interaction effect with exoskeleton power, optimal actuation onset for an ankle exoskeleton with pneumatic muscles is around 42% of the stride. The optimal actuation timing that we found resulted from a trade-off between good m. soleus assistance (with higher reductions for an earlier timing), m. gastrocnemius EMG assistance (with higher reductions for a later timing) and low m. tibialis anterior EMG resistance (with smaller increases for an intermediate timing).
The actuation timing of the condition with the lowest metabolic cost, e.g. with actuation timing onset at 42.3 ± 0.8%, is close to the actuation timing with the lowest metabolic cost of our previous study . Mooney et al. [4, 6] used a similar actuation timing for exoskeleton assistance, leading to high reductions in metabolic cost. The observed optimum is on the other hand later than the onset of torque in a passive exoskeleton with a mechanical clutch that reduced the metabolic cost of walking . However, actuation with the exoskeleton in the present study delivers mostly positive power, whereas in the passive exoskeleton study the reduction in metabolic cost is (at least partly) attributed to eccentric torque support during ankle dorsiflexion in the single stance phase. Recently, Quinlivan et al.  also found large reductions in the metabolic cost with an earlier actuation onset timing. Their exoskeleton assisted both the ankle and the hip joint, which makes it difficult to compare results. Actuation torque ramped in more slowly with that device, such that the relatively low torques at the beginning of exoskeleton assistance may have had little effect on the user. Our observed optimum is on the other hand earlier than with a unilateral prosthesis emulator, where an optimum was found at 52% of stride or later . This difference could be due to the differences between exoskeleton walking and prosthesis walking, the difference between unilateral and bilateral assistance, differences in the actuation profile or a combination of these factors.
Our regression analysis indicated an optimal amount of average exoskeleton power around 0.4 W∙kg−1 summed for both legs: when exoskeleton power was independently varied, metabolic cost followed a U-shaped pattern. The exoskeleton power of the condition with the lowest metabolic cost, e.g. with exoskeleton power of 0.42 ± 0.03 W∙kg−1 summed for both legs corresponds to around 100% of the net biological ankle joint work during walking without exoskeleton assistance [10, 11]. Higher levels of average exoskeleton power resulted in smaller reductions in metabolic cost, especially in the earlier actuation onset timing conditions. With an exo-suit that assisted both the ankle and the hip joints, an inverse linear relationship was found between exoskeleton power and the metabolic cost of walking until average net exoskeleton power was 0.19 W∙kg−1 per leg . This seems in accordance with our results, with the addition that we found that higher amounts of exoskeleton power did not result in further reductions in the metabolic cost of walking. A similar trend was observed in a unilateral exoskeleton study, in which metabolic cost seemed to level off or worsen in the highest exoskeleton work conditions . Other recent simulations and experiments [5, 15, 16] have found that 'more is not always better' for several parameters of walking (assistance), due to a trade-off between several factors that have a positive or a negative effect on the metabolic cost of walking. A similar trade-off seems present in our EMG results. For the m. soleus and the m. gastrocnemius we found that higher exoskeleton power was better but for the m. tibialis anterior high power was detrimental.
It must be mentioned that we chose a second order relationship over an exponential relationship in our regression analysis (Table 1). This choice was based on the fact that average metabolic cost was higher in all High conditions compared to the corresponding Medium conditions, which indicated that metabolic cost had a U-shaped relationship with exoskeleton power in each of the Earliest, Early and Late timing conditions (Fig. 4 and Additional file 4: Figure S4). Due to the small differences in exoskeleton power between our Medium and High conditions, it is difficult to draw strong conclusions. In prosthesis research [14, 35] an exponential relationship was found for the reduction in metabolic cost and average positive exoskeleton ankle power until at least 200% of the positive work that is delivered by the biological ankle joint during normal walking. It is hard to estimate how the relationship would extend when higher amounts of power are applied, especially in late actuation timings.
While we found an optimal amount of exoskeleton power around 0.4 W∙kg−1, lower amounts of power also resulted in a reduction in metabolic cost of 14 to 16% compared to walking in the zero-work mode. In these conditions, the ratio of change in metabolic rate to mechanical power was much higher than in the metabolically optimal condition. This suggests that the assistance in conditions with lower amounts of power (around 0.2 W∙kg−1) can be more efficient, which could be important for autonomous devices where battery weight is a concern.
The extensive parameter sweep that we performed improves overall insights into the assistive mechanism of ankle exoskeletons that result in a reduction in metabolic cost. Previous work suggested that exoskeletons could reduce step-to-step transition costs , which stem from the negative work performed in the collision phase and the positive work to compensate for this energy loss, both serving to redirect the center-of-mass from one arc to another . Indeed, it has been shown that ankle exoskeletons replace some push-off work . However, while some studies have found a reduction in contralateral collision during unilateral exoskeleton walking , other studies with unilateral exoskeletons and prostheses have not found a reduction in collision work [14, 21]. On a muscular level, we did not find reductions in activity of the m. gluteus maximus, the m. biceps femoris, the m. vastus lateralis or the m. rectus femoris during the collision phase, suggesting that costs related to collision were not reduced. Several studies have reported changes in metabolic cost in combination with changes in rebound work instead of collision work [5, 8, 9, 14, 28–30]. In this study we found a strong similarity between the optimal parameters for the reduction in metabolic cost and for reduction in rebound work. The rebound phase is often attributed to positive work of the stance leg and to contralateral leg swing [26, 36]. We found indications of assistance in leg swing initiation during exoskeleton assistance with a reduction in m. rectus femoris activity. One of the roles of the m. rectus femoris during the end of stance and the initiation of swing is to serve as a hip flexor to bring the swing leg forward . This suggests less involvement of hip and knee musculature in leg swing during powered exoskeleton assistance. Several other studies have reported the influence of ankle assistance on the knee and hip joints [4, 12, 17, 18]. Similar findings were found in a prosthesis study , where ankle push-off work also implicated in assisting leg swing initiation. While we only assisted the ankle joint, it is likely that the increased plantarflexion and the higher plantarflexion velocity during push-off with exoskeleton assistance could influence the following leg swing. This seems to coincide with findings of Lipfert et al. , that linked the impulsive ankle push-off in walking to initiation of leg swing. As such, it seems that increased plantarflexion is an essential proviso for assisting leg swing initiation with an ankle exoskeleton. Other studies with exoskeletons that reduced metabolic cost and reported reduced knee and hip work also showed increases in plantarflexion [4, 12, 18]. This could also explain why other exoskeletons that imposed similar torque profiles but maintained normal joint angles did not result in a reduction in the metabolic cost of walking . It may therefore not be optimal to emulate the biological ankle kinematics.
Recent findings from simulations and experiments [5, 40] suggest that the metabolic cost of the plantarflexors is not only related to the push-off but also to isometric activity earlier in the stance phase. For example, reductions in metabolic cost of up to 7% are possible from torque support during the beginning of the stance phase . A combination of replacing ankle joint work during push-off, assisting leg swing initiation (as a direct result of ankle assistance or by assisting the hip joint itself [41, 42]) like we did in our study and torque support during the beginning of stance, as was done in the passive exoskeleton study , could potentially lead to reductions in the metabolic cost of more than 25%, especially because midstance torque support can lead to inefficient, rapid shortening of plantarflexor muscles during push-off . Exoskeletons that can vary torque and power in a more controlled way  could be used to manipulate the exoskeleton power profile and further focus on the combined assistance of torque support during stance and positive power assistance during push-off.
Because of the complexity of human locomotion, reducing the metabolic cost of walking with an exoskeleton is not straightforward. Due to the effect of exoskeleton assistance on walking pattern (e.g. ), on balance , on muscle activity (e.g. ), on elastic stretch and recoil (e.g. ), on fascicle power (e.g. ) and many other factors, it is difficult to predict human behavior resulting from exoskeleton assistance. One way to explore this space is to perform parameter sweep experiments in combination with supporting measures to understand why a specific condition is beneficial (e.g. [3, 8, 9, 14, 21]). These parameter sweep studies will remain necessary in the future to establish optimal assistance as the optimum will likely result from a combination of factors. Another promising approach is direct optimization of metabolic rate using human-in-the-loop optimization techniques [46–48]. At the moment experimental approaches are still preferred, as intuition is often deceiving and simulations are not yet very reliable  because of the complex human-exoskeleton interface. However, experimental results from studies like this one can be used to improve musculoskeletal models.
Our exoskeleton testbed was tethered to off-board hardware and power sources, while other exoskeletons have shown reductions in metabolic cost for autonomous devices where all the hardware and power sources were carried by the user [5, 6]. The large difference that we found between powered exoskeleton walking and exoskeleton walking in zero-work mode suggests that if we could apply our actuation profile with a lightweight autonomous exoskeleton [4–6, 8], reductions of more than 15% versus normal walking are possible. One possibility might be to rely on the human joints to serve as hinges, which shows promise [4, 6, 8].
Despite the recent introduction of autonomous exoskeletons [5, 6, 8] our exoskeleton testbed is still useful as it allows manipulation of exoskeleton assistance parameters in a broad range, which is important to improve the understanding of human-exoskeleton interaction. There is still a need for studies combining metabolic measurements, EMG, kinematics and kinetics during exoskeleton walking in a broad range of conditions to elaborate on these findings. Similar studies have improved insight into exoskeleton work and torque support [8, 9], unilateral prosthesis walking  and bilateral prosthesis walking .
One of the limitations of this study is that individual subjects exhibited differences in their responses to exoskeleton assistance. We searched for one optimal actuation timing and optimal exoskeleton power, while this sometimes seemed to differ between subjects. By using individual optimizations, it may be possible to further reduce metabolic cost. We found reductions of 26 ± 4% for powered versus ZeroWork walking and of 17 ± 10% for powered walking versus normal walking if we averaged the highest reductions from each subject.
We were unable to fully control the shape of the exoskeleton power curve and it is possible that other actuation parameters affect metabolic cost more strongly. For example, our exoskeleton testbed was not able to deliver sufficient power to elaborate the latest onset timing thoroughly. It would be useful to explore higher amounts of exoskeleton power with late timing. A future approach could be to evaluate the effect of the shape of the actuation profile on the metabolic cost of exoskeleton walking.
This study examined the independent effects of ankle exoskeleton actuation timing and average exoskeleton power on the metabolic cost of walking. We showed that reductions in metabolic cost of 21% versus walking with the exoskeleton in zero-work mode and 12% versus normal walking without the exoskeleton are possible with optimal actuation timing and power. Actuation timing showed an optimum at 42% of the stride and average exoskeleton power was optimal at around 0.42 W∙kg−1. The assistive mechanisms leading to these reductions include reducing muscular activity of the plantarflexors during push-off and more proximal muscles during leg swing initiation.
Normal walking is used to refer to walking without an exoskeleton. Active exoskeletons refer to exoskeletons which use external energy to assist walking while passive exoskeletons store and re-use energy which is generated by the user and thus do not need external energy input. Powered walking refers to walking with exoskeleton assistance (both for passive and active exoskeleton assistance). Walking with the exoskeleton without assistance of the device (e.g. with the assistive spring disengaged for passive devices or without active assistance for active devices) is referred to as walking with zero-work instead of 'unpowered' exoskeleton walking (e.g. [3, 10]) to avoid confusion when concerning passive exoskeletons . Tethered exoskeletons refer to a connection with off-board hardware or power source. Autonomous exoskeletons refer to devices were all hardware and power sources are carried by the user.
The authors would like to thank Prof. Wim Derave for his advice in initiating the manuscript, Ing. Davy Spiessens for the technical support, Ms. Pieter Van den Berghe for his assistance in the data collection, Ms. Jozefien Speeckaert for her assistance in data processing and editing the manuscript and the company Aqtor! (Ghent, Belgium) for their assistance in manufacturing the exoskeleton.
This study was funded by the Special Research Fund of Ghent University (BOF10/DOC/288).
The data collected and analyzed in the current study are available in the manuscript and supplementary data. More details are available from the corresponding author upon request.
SG, PM, SC and DDC designed the study; SG and PM performed data collection and data processing; PM and SG performed data analysis; SG, PM, SC and DDC interpreted results of the study; SG drafted the manuscript and PM, SC and DDC edited and revised the manuscript. All authors gave final approval for publication.
All participants of the study proved written informed consent prior to participation. The experimental protocol was approved by the ethical committee of the Ghent University Hospital.


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text:
BritainMar 1st 2014 edition
Rail ambition
Cities used to think modern art could save them. Now it's railway stations
IN JANUARY commuters voted Birmingham New Street one of Britain's worst railway stations. Each day nearly 150,000 people move through a structure built for half as many. But by next year it will be transformed, with 400 tonnes of undulating steel cladding and a vaguely eyeball appearance. The station will have "the wow factor", boasts Sir Albert Bore, the leader of Birmingham city council. It will also show how much attitudes to railway stations have changed.
Railway stations are the chief exception to the rule that Britain invests too little in infrastructure. Of the 17 big termini managed by Network Rail, the owner of Britain's tracks, 11 are being redeveloped or have recently been completed. Five other stations, including Reading and Northampton, are being spruced up by local councils and Network Rail.
Some simply need to be expanded: the number of train journeys has risen by 35% since 2005. But the design of New Street suggests aspirations well beyond more easeful travel. The building would not look out of place in Dubai and is striking, if slightly incongruous, in the grey West Midlands. City planners wanted something monumental, like Grand Central station in New York, says Sir Bernard Zissman, chairman of the independent design panel.
"Twenty or thirty years ago business people were more likely to arrive in a city by car," explains Jon Neale of Jones Lang LaSalle, a property specialist. Town planners duly carved out motorways and roundabouts to entice them. In 1962 a local politician claimed that a new design for Birmingham, involving an inner ring road, would make it "one of the finest city centres in Europe".
Cities now measure their appeal by their stations. Businesses cluster around them: at King's Cross, a once-grimy part of north London, a postcode has been created for all the new buildings around the station, which was redeveloped in 2013. John Lewis, an upmarket department store, will open in the mall above New Street (which is indeed called "Grand Central") along with 60 other shops. The council hopes it will pull in visitors to the city.
Such ambition recalls the stations of the 19th century. Those structures "spoke to the corporate sensibility of a city," says Tristram Hunt, an MP and historian, by combining commerce with the sheen of civic pride. The first New Street station, built in 1851, had the largest single-span roof in the country at the time. It was torn down by enthusiastic 1960s town planners. Now some of its original lustre may return.
This article appeared in the Britain section of the print edition under the headline "Rail ambition"

redpajama_set_name: RedPajamaCommonCrawl


When I started to learn agile approaches to projects, OODA was all the rage, but it seems to have disappeared from view as modern agilistas move from Scrum to lean to Kanban to ultra-velocitus development.
I guess I am still a bit old school, because I still think the OODA loop is the essence of the agile approach.
So what is an OODA loop?
OODA loops began as an approach for fighter pilots to avoid dying. It is a way to train fighter pilots and also a way to design fighter aircraft, develop tactics for air combat and win wars in the air. It turns out that it is also really useful for agile projects, production support teams and anyone rolling out a product in a competitive market.
But what is an OODA loop?
I am only producing a quick summary of the idea here because I want to adopt the approach when I am doing some regression testing. But you can get more information by going to Wikipedia and exploring the links it has there.
In a rapidly changing world, you can come up with a plan but it will be out of date before you can text your friends to tell them that you plan is ready to share.
So rather than just executing your plan you need to get really good at observing, interpreting and reacting to what is happening around you.
But as you react, you also impact the world, which then reacts to what you have done and is immediately different to what it was when you started to react to it.
So now you need to observe what is going on again, interpret it and react again faster than your competitors because otherwise you are always playing catch-up.
What is worse though is that it is not only about how fast you react, but how effectively you do so. If you are acting on the wrong information or you are interpreting it incorrectly then that is often worse than not reacting. So you need to be really, really good at observing, interpreting and reacting to things.
But things get even worse, because you cannot just react to what others do, you need to also take the initiative and come up with new moves and new ideas before the other guy does.
But of course while you are getting good at observing what happens and thinking up cunning new ideas, the world is moving so rapidly that you already need to be reacting again.
And so the loop repeats (unless you are a fighter pilot; in which case you will be shot down before you react).
I was asked by an online university whether I could put a link to their degree on my site somewhere. I actually think it is relevant here because my first degree was done face to face and had a slow response time in learning – I would attend a tutorial and have a short discussion where one person spoke at a time. But when I did my masters online, I would read comments from other students, then see a response next time I logged on … and then see further discussions. So rather having one go at a topic (and then studying the night before the exam) I would have several "learning cycles". I would see a comment (observe), orient myself to what they meant, decide whether to respond and then type my response. Then the cycle would begin again.
The reason I did my second degree online was for convenience, but I do think I gained a further benefit from the ongoing learning cycles rather than a one off hit at each topic. If you want to see how this works, then check this link for a good example of an online degree program – this one is relevant both because it is online and because the courses are relevant to the concepts of OODA (business intelligence and modern organizational leadership): SJU Online.
But what is the OODA loop?
For a project team this means observing the impact that your previous decisions and actions have had, as well as how the evolving system is performing. And in some cases it also means observing what your competitors, suppliers and business customers are experiencing.
For a fighter pilot this means making sense of huge amounts of chaotic, complex and often contradictory information as you observe it. The problem is made worse because the pilot already has his or her own assumptions and expectations and these alter the way the pilot will interpret what he or she observes, sometimes for the better and often to the pilot's detriment.
For a project team, orient means exactly the same thing.
For both doomed fighter pilots and project teams, this is the piece that we often don't have time to do, or that we do incorrectly without even realising it. But that means either we cannot make a decision or that we make the wrong one. Either way it means that things become overwhelming and you end up crashing and burning.
Apparently a lot of people who do well in the early stages of pilot training bomb out when they get to aerial combat training because they simply don't trust their instincts enough to make a decision when they are in practice dogfights … or they ignore their analysis and make poor decisions in the heat of battle.
This is also where a lot of clever people bomb out when they become project leaders, senior BA's or testers. They can analyse information but they do not seem to be able to make effective decisions based on that analysis.
The situation gets even worse for many teams where different people are making logical decisions on their own "OODA" loop, but this is not aligned to the analysis and decisions that others are making.
Obviously for a fighter pilot there is no point in a decision that is not acted on quickly. But just as importantly, if the pilot lacks flying skills then he or she may attempt to do something and fail – which ends with crashing and burning.
Once again things are similar in the project team, if a decision is not acted on quickly then it is out of date and the team have to redo their analysis and if the team are poor at executing their decisions then they quickly become muddled as events overtake their planning.
Sadly, no matter how fast you act in a rapidly evolving situation, you still need to adjust to events that are transpiring as you act. And each time you act, you alter the complex system you are part of, both for better and for worse. So you need to rapidly return to the Observe step, observing the impact of your action and the other changes that are occurring at the same time.
Most of the good project managers I work with can relate to the OODA loop even if they have not heard it explained in quite those terms. In fact this is how I predict which project managers who will be lucky in their projects and which will seem to work hard and do all the right things but then crash and burn.
More than that though, I think that this is what separates a brilliant BA from a mediocre one. Rather than simply regurgitating what people say and therefore passing the already out of date information back to the decision makers, a good business analyst is the one who actually drives the OODA loop in the team.
So when you have good BA's the decision makers feel like they are undder pressure, but there is a feeling that they are fine tuning old decisions and making the right new decisions as the project evolves. But with a bad BA, decision makers have no idea what is going on after only a few days on the project, or they skip the analysis phase in pursuit of timely (and often ill-informed) decisions and then pay the price later as the project seems to get harder and harder.
But what does all that have to do with regression testing? I hope to make that clear in the next enthralling episode of my series on regressions testing, day by day.


text:
Oínes (llamada oficialmente San Cosme de Oíns) es una parroquia española del municipio de Arzúa, en la provincia de La Coruña, Galicia.

Otras denominaciones

La parroquia también es conocida por el nombre de San Cosme de Oís.

Entidades de población

Entidades de población que forman parte de la parroquia:
 Beis
 Ferradal
 Filgueira
 Fraga (A Fraga)
 Fruzo
 León de Abaixo
 León de Arriba
 Manedo
 Pumar (O Pumar)
 Seixas
 Vilamoura (A Vilamoura)

Despoblado

Despoblado que forma parte de la parroquia:
 Iglesia (A Igrexa)

Demografía

Referencias

Enlaces externos

Parroquias de Arzúa

redpajama_set_name: RedPajamaWikipedia


text:
Die HSG Hanau (Handballspielgemeinschaft Hanau) ist eine Handballspielgemeinschaft der Handballabteilungen der TS Steinheim und des TV Kesselstadt. Der Zusammenschluss erfolgte im Jahr 2009. Momentan spielt die HSG Hanau in der 3. Liga Mitte. Außerdem spielen drei Jugendmannschaften der HSG Hanau zurzeit in der für ihre Altersklasse höchsten Liga, darunter die A-Jugend, die in der Staffel Mitte der Jugend-Handball-Bundesliga (JHBL) aufläuft.

Mannschaften

Männer 1
Die 1. Männermannschaft der HSG Hanau ging nach der Vereinsgründung im Jahr 2009 in der hessischen Landesliga Süd an den Start. Sie beendete die Saison 2009/2010 auf dem 4. Platz der Landesliga Süd.

In der darauffolgenden Saison 2010/2011 konnte sich die Mannschaft auf dem dritten Platz der Landesliga Süd platzieren und steigerte sich im Vergleich zur Vorsaison um einen Rang.

Eine Saison später (2011/2012) wurde auf dem ersten Tabellenplatz mit nur zwei Niederlagen der Aufstieg in die Oberliga Hessen erreicht.

In der Saison 2012/2013 schaffte man als Neuling direkt den Sprung auf den 5. Platz.

In den beiden darauffolgenden Jahren konnte die HSG Hanau den 7. Platz (2013/2014) und den 3. Platz (2014/2015) erreichen.

Ein Jahr später (2015/2016) konnte sich die HSG Hanau mit einem Punkt Vorsprung auf die SG Bruchköbel den Titel Hessenmeister sichern und stieg somit in die 3. Liga Ost auf. Den Meistertitel in der Oberliga Hessen konnte sich die HSG Hanau in derselben Saison erst am letzten Spieltag in einem Heimspiel gegen die 2. Mannschaft des TV Hüttenberg sichern.

Am Ende der Saison 2016/2017 konnte die HSG Hanau den Klassenerhalt bereits im vorletzten Heimspiel der Saison gegen die SG LVB Leipzig sichern und steht in der 3. Liga Ost auf dem 9. Platz.

Im letzten Spiel der Saison 2017/2018 wurde die bislang beste Platzierung in der jungen Vereinsgeschichte erzielt, als man mit einem 3. Platz in der 3. Liga Staffel Ost auf sich aufmerksam machte und in allen 15 Spielen in der heimischen Main-Kinzig-Halle ungeschlagen blieb (2 Remis). Durch diese Platzierung haben sich die Grimmstädter erstmals in der Vereinsgeschichte für den DHB-Pokal qualifiziert.

Als Ausrichter dieses First-Four-Turniers am 18.+19. August 2018 in der Main-Kinzig-Halle darf die HSG Hanau die Bundesligisten HC Erlangen und SG BBM Bietigheim sowie dem Ligakonkurrenten MSG Groß-Bieberau/Modau begrüßen.

Die Saison 2018/2019 war die erfolgreichste für die HSG Hanau mit einer Punkteausbeute von 37:23.

Nach der vorzeitigen Beendigung des regulären Spielbetriebs in der Spielzeit 2020/2021 der 3. Liga aufgrund der COVID-19-Pandemie in Deutschland nutzte der Verein die Möglichkeit, sich für die außerordentliche Aufstiegsrunde zur 2. Handball-Bundesliga anzumelden; zum Aufstieg reichte es allerdings nicht.

A-Jugend
Die A-Jugend der HSG Hanau startete 2009/2010 in der Bezirksoberliga in den Spielbetrieb und konnte in der ersten Saison nur den 9. Platz der Bezirksoberliga erreichen und wurde somit Vorletzter. In der Saison 2010/2011 spielte die Hanauer A-Jugend auch in der Bezirksoberliga und erreichte den 5. Platz.

In der Saison 2011/2012 konnte sich die A-Jugend für die neugegründete Jugend-Handball-Bundesliga (JHBL), die höchste Liga der Altersklasse, qualifizieren. Die Mannschaft ging in der Staffel West an den Start und konnte sich am Ende der Saison auf dem 7. Tabellenrang platzieren. Ein Jahr danach 2012/2013 qualifizierte man sich erneut für die JHBL, erreichte jedoch nur den 11. Tabellenrang und wurde damit Vorletzter in der Staffel West der A-Jugend Bundesliga.

In der Saison 2013/2014 erreichte der Verein den 3. Platz in der JHBL-Staffel West und errang damit die Direktqualifikation für ein weiteres Jahr in der A-Jugend Bundesliga.

Seit der Saison 2014/2015 gehen die A-Jugendhandballer der HSG Hanau in der Staffel Ost der JHBL an den Start. In ihrer ersten Saison in der Staffel Ost endete die Saison für den Hanauer Nachwuchs auf dem 4. Platz.

Mit dem erreichen des 4. Platzes sicherte man die direkte Qualifikation für ein weiteres Jahr in der JHBL. Ein Jahr später 2015/2016 verpasste man dann die Direktqualifikation da nur der 8. Platz und nicht der für die Direktqualifikation nötige 6. Platz erreicht werden konnte. Jedoch qualifizierten sich die Hanauer A-Jugendlichen auch im nächsten Jahr wieder für die A-Jugend Bundesliga.

In der vorangegangenen Saison 2016/2017 konnte man sich mit erreichen des 4. Platzes erneut die Direktqualifikation sichern und beendete die Saison damit vor den 5. platzierten Nachwuchsspielern vom TV Gelnhausen und hinter der Junioren Akademie des TV Großwallstadt aus Großwallstadt, welche den 3. Platz erreichte.

Mit dem 7. Platz in der Saison 2017/2018 verpasste die HSG Hanau die direkte Qualifikation für das folgende Bundesligajahr.

In einem hessenweiten Qualifikationsturnier (3. Platz) und einem weiteren mit Teilnehmern aus Hessen, Rheinland-Pfalz und dem Saarland konnten sich die Grimmstädter am 27. Mai 2018 mit einem 2. Platz in diesem Qualifikationsturnier für die nunmehr 8. Saison in Folge für die höchste Deutsche Spielklasse qualifizieren. Ab der Saison 2018/2019 wurden die Staffeln Nord & Ost verschmolzen und die Staffel Mitte neu eingeführt.

In der Spielzeit 2018/2019 sicherte sich die Hanauer A-Jugend den 3. Platz mit einer Punkteausbeute von 31:13 und verpasste nur knapp die erstmalige Qualifikation zur Deutschen Meisterschaft.

B-Jugend
Die B-Jugend der HSG Hanau spielte in der Saison 2009/2010 in der Regionalliga in der Staffel Ost, die damals höchste Spielklasse der B-Jugend, und konnte sich am Ende der Saison auf dem 7. Tabellenrang platzieren.

In der Saison 2010/2011 ging man in der Oberliga Hessen an den Start, die in dieser Saison ebenfalls höchstmögliche Spielklasse. Der Hanauer Nachwuchs gewann in diesem Jahr die Oberliga Hessen und nahm daher an der Südwestdeutschen Meisterschaft teil. Im Halbfinale der Südwestdeutschen Meisterschaft bezwang man die SG Wallau mit 27:26 in Wallau und eine Woche später 40:31 in Hanau. Im Finale der Südwestdeutschen Meisterschaft konnte man den TV Hüttenberg nach einer 36:23-Auswärtsniederlage im Rückspiel in Hanau mit 30:17 bezwingen und wurde Südwestdeutscher Meister. Daraufhin spielte man nun im Viertelfinale der Deutschen Meisterschaft. Gegner hier war die HSG Menden-Lendringsen. Das Hinspiel des Viertelfinales in Hanau ging 21:21 aus, jedoch verloren die Hanauer B-Jugendhandballer das Rückspiel mit 31:26 und schieden aus dem Wettbewerb aus.

Ein Jahr später, 2011/2012, ging man auch in der Oberliga Hessen an den Start und konnte erneut den Titel gewinnen. Somit war man für die Vorrunde der deutschen Meisterschaft qualifiziert. Gegner waren hier in der Gruppe D: die SG Kronau Östringen und der TSV GWD Minden. Die Hanauer konnten mit nur einem Sieg über Minden im Hinspiel den zweiten Platz erreichen, der jedoch nicht für eine Teilnahme am Halbfinale der Deutschen Meisterschaft ausreichte.

Auch 2012/2013 spielte der Hanauer Nachwuchs wieder in der Oberliga Hessen, konnte jedoch aufgrund einer 20:21-Niederlage im Rückspiel am vorletzten Spieltag gegen die HSG Dudenhofen Münchholzhausen nicht den 1. Platz erreichen, die Mannschaft wurde zweiter und qualifizierte sich somit nicht für die deutsche Meisterschaft.

In der Spielzeit 2013/2014 konnte sich der Hanauer Nachwuchs nur für die Landesliga Hessen, die zweithöchste Spielklasse, qualifizieren und beendete die Saison auf dem 4. Platz. Auch 2014/2015 spielte man in der Landesliga und erreichte den 3. Platz, punktgleich mit den viertplatzierten Handballern der HSG Dudenhofen Münchholzhausen.

In der Saison 2015/2016 schafften die B-Jugendhandballer der HSG Hanau nach zwei Jahren erstmals wieder den Einzug in die Oberliga und konnten diese gewinnen. Somit war man als Hessenmeister am Ende der Saison für das Viertelfinale der deutschen Meisterschaft qualifiziert. Im Viertelfinale traf man in Gruppe B auf den Nachwuchs der Eintracht Hildesheim und auf die Nachwuchsspieler des SV 64 Zweibrücken. Zweibrücken konnte sich als Erster durchsetzen und zog ins Halbfinale gegen die Füchse Berlin ein. Die HSG Hanau wurde in Gruppe B des Viertelfinales aufgrund eines 27:21-Hinspielsieges gegen die Eintracht aus Hildesheim und eines 26:25-Sieges gegen Zweibrücken im Rückspiel Zweiter vor dem Nachwuchs der Eintracht Hildesheim.

In der Saison 2016/2017 musste sich die B-Jugend der HSG Hanau über ein Entscheidungsspiel für die Oberliga qualifizieren. Gegner war die mJSG Melsungen/Körle/Guxhagen. Obwohl man das Hinspiel in Melsungen mit 25:24 verlor, qualifizierte man sich dank eines 31:22-Sieges im Rückspiel für die Oberliga. Die Hanauer B-Jugendhandballer konnten jedoch mit 17:11 Punkten nur den 5. Platz der Oberliga erreichen, punktgleich mit der SG Wallau/Massenheim und 10 Punkte vor den Sechstplatzierten von der HSG VfR/Eintracht Wiesbaden.

Nach einem guten Start in die Saison und dem zweiten Platz in der Hinrunde, reichte es nach einer schwächeren Rückrunde nur für den 5. Platz in der Saison 2017/2018.

C-Jugend
Die C-Jugend der HSG Hanau beendete ihre erste Saison 2009/2010 auf dem zweiten Platz der Oberliga Hessen Staffel Süd (höchste Spielklasse der Altersklasse) mit 25:3 Punkte hinter der JSG Wallstadt 27:1 Punkte und verpasste somit die Hessenmeisterschaft. Hanau verlor das Hinspiel gegen Wallstadt mit 30:31 und kam im Rückspiel nicht über ein 30:30-Unentschieden hinaus.

Auch im darauffolgenden Jahr (2010/2011) ging die C-Jugend der HSG Hanau in der Oberliga Hessen Staffel Süd an den Start. Die HSG Hanau Jugendhandballer gewannen die Oberliga mit 26:2 Punkten souverän vor der HSG Bensheim/Auerbach 22:6 Punkte. Die Hanauer C-Jugend verlor nur ein Spiel gegen JSG Crumstadt/Goddelau mit 20:19 alle weiteren Spiele konnten gewonnen werden. Auch bei der nachfolgenden Hessenmeisterschaft konnte sich die C-Jugend der HSG Hanau durchsetzen. Sie gewann das erste Spiel der Hessenmeisterschaft gegen die TSG Münster mit 36:24 und das zweite Spiel gegen die HSG Zwehren/Kassel mit 41:27 und wurde Hessenmeister. Als Hessenmeister war man für die Südwestdeutsche Meisterschaft qualifiziert, bei der man den zweiten Platz hinter dem SV 64 Zweibrücken erringen konnte. Die C-Jugend der HSG Hanau gewann bei der Südwestdeutschen Meisterschaft vier Spiele und verlor nur gegen den erstplatzierten aus Zweibrücken mit 35:23.

In der Saison 2011/2012 spielte die Hanauer C-Jugend wieder in der Oberliga Süd, konnte jedoch nur den 5. Platz erreichen. Die Bilanz: Sechs Siege und acht Niederlagen bei 12:16 Punkten und einem Torverhältnis von −33 Toren.

Auch ein Jahr später 2012/2013 erreichte die C-Jugend HSG Hanau die Hessenmeisterschaft nicht und steht am Ende der Saison mit 18:10 Punkten auf dem 4. Platz.

Eine Saison danach (2013/2014) steht man im Vergleich zur Vorsaison wieder einen Platz besser da. Die C-Jugendhandballer erreichen den 3. Platz der Oberliga Süd mit 22:6 Punkten, elf Siege und drei Niederlagen sind die Ausbeute der Saison.

In der Saison 2014/2015 verpasst die Hanauer C-Jugend die Qualifikation zur Oberliga und spielt daher nur in der Bezirksoberliga. Die C-Jugendlichen der HSG Hanau werden mit 25:3 Punkten zweiter in der Bezirksoberliga hinter dem TV Langenselbold.

Ein Jahr später (2015/2016) kam die HSG Hanau wieder zurück in der Oberliga Süd und gewann sie. Die Hanauer C-Jugendlichen sind mit 28:0 Punkten klar auf dem 1. Platz vor den Nachwuchshandballern des TV Gelnhausen (23:5 Punkte) und denen der SG Bruchköbel (21:7 Punkte). Auch ist Hanau mit nur 365 Gegentoren die defensiv stärkste Mannschaft der Liga. Die beiden Topspiele gegen die C-Jugendhandballer des TV Gelnhausen konnten mit 37:34 und 45:39 gewonnen werden. Bei der folgenden Hessenmeisterschaft konnten die Hanauer vor über 300 Zuschauern jedoch nur das erste der beiden Spiele gewinnen. Nach einem 30:20-Sieg gegen die HSG Zwehren/Kassel folgte im zweiten Spiel eine 37:30-Niederlage gegen die HSG Dudenhofen/Münchholzhausen. Das Ergebnis war die Vize-Hessenmeisterschaft für die C-Jugendlichen der HSG Hanau.

Ein ähnliches Bild ergab sich in der Saison 2016/2017. Erneut konnte man die Staffel Süd der Oberliga ohne Niederlage gewinnen. 27:1 Punkte waren die Bilanz am Saisonende. Somit konnte man sich vor der HSG Dreieich platzieren, die mit 25:3 Punkten zweiter wurde. Das erste Spitzenspiel gegen Dreieich endete 21:21, das Rückspiel konnte in heimischer Halle 28:27 gewonnen werden. Bei der Hessenmeisterschaft konnten die Hanauer C-Jugendhandballer jedoch kein Spiel für sich entscheiden. Die C-Jugend verlor 21:31 gegen die mJSG Melsungen/Körle/Guxhagen und 29:17 gegen die TSG Münster. Somit war man am Saisonende Hessens drittbeste C-Jugend Handballmannschaft.

Hanau wurde 2017/2018 zweiter in der Staffel Süd der C-Jugend Oberliga und qualifizierte sich somit für die Hessenmeisterschaft. Die Hanauer C-Jugend gewann das Halbfinale gegen die HSG Dutenhofen/Münchholzhausen mit 23:17, verlor jedoch das Finale mit 20:25 gegen die HSG Hochheim/Wicker.

Weblinks
 Staffel Ost – Deutscher Handballbund
 Aktuell 3. Liga Männer Staffel Ost – Deutscher Handballbund
 SIS-Handball

Einzelnachweise

Sportverein (Hanau)
Hanau, Hsg
Gegründet 2009


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Introducing the Speake-Marin Dong Son Tourbillon
December 2, 2015 JaseNewsIndependent watchmaker, Speake-Marin, Tourbillon, Unique piece
The ancient Dong Son people of Vietnam and more specifically, their bronze drums, served as the inspiration for Speake-Marin's newest unique piece, the Dong Son Tourbillon. The Dong Son drums were bronze drums created by Dong Son culture that existed around the Red River Delta of northern Vietnam between 600BC and 300AD. The drums were said to represent an artistic level that very few cultures of the time could match, and the independent watchmaker is paying tribute to this ancient culture with a unique creation of its own.
The Dong Son Tourbillon features a 38mm Piccadilly red gold case and is presented on an alligator strap with a red gold pin buckle.
As you can see from the picture above, the Dong Son Tourbillon features the brand's calling card, the Piccadilly case, which frames a beautifully decorated 18k solid red gold dial. Speake-Marin says the dial gets its design cues from some of the most revered Dong Son drum creations such as the Hoàng Ha drum, Co Loa drum, as well as the famous Ngoc Lu drum. The top surface of these drums are said to be adorned with a central starburst and concentric circles, within which are a set of repetitive images of armed warriors and flying egrets, as well as geometric hatching.
Following this, the Dong Son Tourbillon's dial features an outer ring of egrets, along with an inner ring decorated with plumed warriors holding their weapons between the 12-points of the central starburst pattern. These warriors, as you may have guessed, function as hour markers for the smaller of the two Speake-Marin "Foundation" style heat-blued steel hands.
Although it features a highly detailed dial, Speake-Marin has ensured it's easy for the wearer to read the time.
To make it easy for the wearer to quickly figure out the time, the dial features a prominent roman numeral at the 12 o'clock position, just above one of the inner ring's plumed warriors. Likewise, on the periphery of the outer egret ring, sits a railway-style minute track, which the minute hand fully reaches (see picture above).
The intricate 18k red gold dial measures 1mm thick, and the incredible details are the result of an interesting chemical etching process. This process creates motifs that are just 0.3mm deep – a result that is impossible to achieve by hand engraving says Peter Speake Marin.
A diamond is set within the fluted crown.
The Dong Son Tourbillon features a 60-second tourbillon, visible at the traditional six o'clock position. It is framed by a steel guard, and is secured in position by a polished bridge produced from Durnico steel, which is noted for its strength. Like all of Speake-Marin's tourbillon movements, the tourbillon cage is shaped like a topping tool wheel, which is also the brand's logo.
As you'd expect, the watch features an exhibition caseback, which gives the wearer the opportunity to enjoy, and examine, the beautifully finished SM/CDH Calibre SM3 automatic movement. The movement features a platinum micro-rotor, and offers a power reserve of 72-hours when fully wound. The calibre is the work of specialist movement developer Création et Développement Horlogers (CDH), a Swiss company that Speake-Marin collaborates with (hence the 'SM/CDH' movement label).
The movement is meticulously finished though most of the gear train is hidden under the German-silver movement plates. The plates themselves have a circular grain finish.
Speake-Marin is yet to announce price of the Dong Son Tourbillon.
To learn more about Speake-Marin, click here.


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Home/World/7 killed, 11 injured in Hong Kong apartment building fire
7 killed, 11 injured in Hong Kong apartment building fire
November 16, 2020 World Leave a comment
Hong Kong (AP) – Authorities in Hong Kong said a fire in an apartment building killed at least seven people and injured 11, which probably began when people from the city's Nepali community gathered.
The explosion occurred on Sunday night in Yao Ma Tei, a bustling area in Kowloon, usually with old apartment blocks, shops and businesses.
According to the area's district council member, Leslie Chan, the apartment that caught fire was operating as an unlicensed restaurant.
The Hong Kong government said that four men and three women were among those who died, the ages of those killed and injured were stated to be between 8 and 48. Chan said the dead and injured belonged to the Nepali community.
Fire officials said at a press conference early Monday that the cause of the explosion was still unknown. According to fire department official Cheung Kwong-un, the building lacked a sprinkler system and people were trapped behind the kitchen.
Local media reports said that people were celebrating Diwali at the restaurant, a Hindu festival, as well as a birthday when the explosion occurred. According to media reports, lit candles set fire to soundproof materials.
Residents in the neighborhood said those caught in the fire were from Nepal. Many live in the Nepalese region and work in Hong Kong's finance, retail and security businesses.
In the low-rise building, the apartment where the fire was set, some of its windows were blown and the outer walls were soothed. The building was cordoned off on Monday while police were on watch.
Chan said the district council is providing assistance to those evacuated from the building and some need to stay in hostels temporarily while the fire is investigated.
Hong Kong leader Carrie Lam issued a statement shortly after midnight, saying she was "very sad that the fire in Yao Ma Ti last night resulted in many deaths and injuries." He also visited the site of the fire on Monday morning.
Local media said the fire was the deadliest after the 2011 blasts in which nine people were killed.
The incident highlights fire safety issues in the city's old buildings, home to some of the world's most expensive real estate. Fatal fires used to be a regular occurrence in Hong Kong, one of the world's most densely populated cities, but has declined in recent years as the government has implemented strict fire safety measures.
The story has been corrected to show that the victims were aged between 8 and 48, not 8 and 38. And to show that 11 people were injured in the fire, not 12.
Tags AP Top News Asia / Pacific Hogcog The fire
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