<clinical_study>
  <!-- This xml conforms to an XML Schema at:
    https://clinicaltrials.gov/ct2/html/images/info/public.xsd -->
  <required_header>
    <download_date>ClinicalTrials.gov processed this data on September 20, 2023</download_date>
    <link_text>Link to the current ClinicalTrials.gov record.</link_text>
    <url>https://clinicaltrials.gov/ct2/show/NCT03277508</url>
  </required_header>
  <id_info>
    <org_study_id>2017/00312</org_study_id>
    <nct_id>NCT03277508</nct_id>
  </id_info>
  <brief_title>Assistive Soft Robotic Glove Intervention Using Brain-Computer Interface for Elderly Stroke Patients</brief_title>
  <official_title>Assistive Soft Robotic Glove Intervention Using Brain-Computer Interface for Elderly Stroke Patients</official_title>
  <sponsors>
    <lead_sponsor>
      <agency>National University Hospital, Singapore</agency>
      <agency_class>Other</agency_class>
    </lead_sponsor>
    <collaborator>
      <agency>National University of Singapore</agency>
      <agency_class>Other</agency_class>
    </collaborator>
    <collaborator>
      <agency>Agency for Science, Technology and Research</agency>
      <agency_class>Other</agency_class>
    </collaborator>
  </sponsors>
  <source>National University Hospital, Singapore</source>
  <oversight_info>
    <has_dmc>No</has_dmc>
    <is_fda_regulated_drug>No</is_fda_regulated_drug>
    <is_fda_regulated_device>No</is_fda_regulated_device>
  </oversight_info>
  <brief_summary>
    <textblock>
      This research aims to integrate and develop a novel Brain-Computer Interface (BCI) controlled&#xD;
      soft robotic glove, evaluate the ability of the glove in achieving common hand grasping&#xD;
      postures and to assess the efficacy of the glove in assisting stroke patients with completing&#xD;
      functional tasks. The BCI-controlled soft robotic glove utilizes patients' user intent to&#xD;
      deliver specific electroencephalographic patterns that can trigger robot-assisted hand&#xD;
      movement to the affected hand.&#xD;
    </textblock>
  </brief_summary>
  <detailed_description>
    <textblock>
      Hand motor impairment is very common after a stroke. These impairments include difficulty&#xD;
      moving and coordinating the hands and fingers, which inhibits stroke patients from being able&#xD;
      to perform daily functional tasks independently, resulting in a reduced quality of life. More&#xD;
      than half of people with upper limb impairment after stroke will still have problems many&#xD;
      months to years after their stroke. Therefore, improving hand function is a core element of&#xD;
      rehabilitation. Many possible interventions have been developed; these may involve different&#xD;
      exercises or training, specialist equipment or techniques, or they could take the form of a&#xD;
      drug (pill or injection) given to help hand movement. There is limited evidence that suggests&#xD;
      the following interventions may be effective: constraint-induced movement therapy, mental&#xD;
      practice, mirror therapy,interventions for sensory impairment, virtual reality and a&#xD;
      relatively high dose of repetitive task practice. Current hand rehabilitation robotic devices&#xD;
      are typically driven by rigid linkages or joints, which subject the patient's fingers into a&#xD;
      single plane of motion that will feel unnatural and uncomfortable. On top of that,these&#xD;
      devices belong to the class of continuous passive motion (CPM) devices that only promote hand&#xD;
      range-of-motion, but do not require the patient to play a semi-active role in performing the&#xD;
      hand exercises. Furthermore, there is a huge demand for solutions assisting stroke patients&#xD;
      with using the densely paralyzed hand to perform activities of daily living (ADL) in real&#xD;
      life, which is not available at present. Most of the hand rehabilitation robotic devices&#xD;
      available in the market cannot assist paralyzed hand to carry out ADL. To develop an&#xD;
      assistive device to solve this unmet need, we decided to combine BCI technology with the&#xD;
      wearable soft robotic glove, which enables actuation of paralyzed hand by motor imagery.&#xD;
    </textblock>
  </detailed_description>
  <overall_status>Completed</overall_status>
  <start_date type="Actual">March 2, 2018</start_date>
  <completion_date type="Actual">April 22, 2019</completion_date>
  <primary_completion_date type="Actual">April 22, 2019</primary_completion_date>
  <phase>N/A</phase>
  <study_type>Interventional</study_type>
  <has_expanded_access>No</has_expanded_access>
  <study_design_info>
    <allocation>Randomized</allocation>
    <intervention_model>Parallel Assignment</intervention_model>
    <intervention_model_description>A randomization stratification method, with a computer generated random sequence will be used. Subjects will be grouped in strata according to their Fugl-Meyer scores(0-28 vs 29-45) and randomized separately according to a block randomization to receive either of following treatments:(1) Treatment A: 18 sessions for 6 weeks of 60 minutes of passive robot-assisted hand therapy+ 30 minutes of standard hand therapy.(2) Treatment B: 18 sessions for 6 weeks of 60 minutes of BCI controlled robot-assisted hand therapy + 30 minutes of standard hand therapy.</intervention_model_description>
    <primary_purpose>Device Feasibility</primary_purpose>
    <masking>Single (Outcomes Assessor)</masking>
    <masking_description>The blinding will be carried out in such a way that the therapist will provide conventional therapy and assess the arm function without knowing the group that the patients belong to. The BCI Robot and Passive Robot will be administered by other study team member. A third party study team member not participating in the intervention will keep the log book which contains information regarding patients study ID and group. Randomization codes will be broken when all interventional and follow-up sessions were completed or when the patient decides to quit participation. The third party team member will unmask according to PI's request in this occasion.</masking_description>
  </study_design_info>
  <primary_outcome>
    <measure>Action Research Arm Test</measure>
    <time_frame>30 minutes</time_frame>
    <description>It assesses a client's ability to handle objects differing in size, weight and shape and therefore can be considered to be an arm-specific measure of activity limitation</description>
  </primary_outcome>
  <primary_outcome>
    <measure>Fugl-Meyer Assessment</measure>
    <time_frame>20 minutes</time_frame>
    <description>It is designed to assess motor functioning, balance, sensation and joint functioning.</description>
  </primary_outcome>
  <primary_outcome>
    <measure>Grip Strength Test</measure>
    <time_frame>5 minutes</time_frame>
    <description>The purpose of this test is to measure the maximum isometric strength of the hand and forearm muscles.</description>
  </primary_outcome>
  <number_of_arms>2</number_of_arms>
  <enrollment type="Actual">11</enrollment>
  <condition>Stroke</condition>
  <arm_group>
    <arm_group_label>BCI robot assisted hand therapy</arm_group_label>
    <arm_group_type>Experimental</arm_group_type>
    <description>Brain-computer integration robot assisted hand therapy</description>
  </arm_group>
  <arm_group>
    <arm_group_label>CPM robot assisted hand therapy</arm_group_label>
    <arm_group_type>Active Comparator</arm_group_type>
    <description>Continue Passive Movement robot assisted hand therapy</description>
  </arm_group>
  <intervention>
    <intervention_type>Device</intervention_type>
    <intervention_name>BCI Robot Assisted Hand Therapy</intervention_name>
    <description>18 sessions (3 times per week for a total of 6 weeks) of 1.5 hours each training (60 minutes of BCI Robot and 30 minutes of the standard hand therapy).&#xD;
Session 1-3. Peg game: Lateral movement of gears/cups Session 4-6. Moving cups onto a shelf: Elevation process for the arm, small cups to be used.&#xD;
Session 7-9. Carrying of basket: Heavier load to be used, patient to hold the basket by the handles at the side, not by its base.&#xD;
Session 10-12. Opening bottle + pouring into a cup: Training of ADL Session 13-15. Eating: Use of a modified spoon to train ADL Session 16-18. Box and blocks: Precise index finger and thumb control</description>
    <arm_group_label>BCI robot assisted hand therapy</arm_group_label>
  </intervention>
  <intervention>
    <intervention_type>Device</intervention_type>
    <intervention_name>CPM Robot Assisted Hand Therapy</intervention_name>
    <description>18 sessions (3 times per week for a total of 6 weeks) of 1.5 hours each training (60 minutes of Passive Robot and 30 minutes of the standard hand therapy).The procedure will include the following: Session 1-18: Continuous passive motion</description>
    <arm_group_label>CPM robot assisted hand therapy</arm_group_label>
  </intervention>
  <eligibility>
    <criteria>
      <textblock>
        Inclusion Criteria:&#xD;
&#xD;
          -  Aged 55-90 years regardless of lesion size, race&#xD;
&#xD;
          -  History of stroke less than 3 months prior to participation&#xD;
&#xD;
          -  Stroke type: ischemic or haemorrhagic&#xD;
&#xD;
          -  Fugl-Meyer motor score (FM score) of upper extremity impairment of 0-45 out of a&#xD;
             maximum score of 66 on the Fugl-Meyer assessment scale&#xD;
&#xD;
          -  Ability to pay attention and maintain supported sitting for 1.5 hours continuously&#xD;
&#xD;
          -  Lack of or poor hand mobility (Medical Research Council Grade ≤ 2/5)&#xD;
&#xD;
          -  Able to give own consent&#xD;
&#xD;
          -  Able to comprehend and follow commands (Abbreviated Mental Test equal or more than 7)&#xD;
&#xD;
          -  Fulfil BCI resting brain states on initial screening.&#xD;
&#xD;
          -  Unilateral upper limb impairment&#xD;
&#xD;
        Exclusion Criteria:&#xD;
&#xD;
          -  Recurrent clinical stroke&#xD;
&#xD;
          -  Functional status: severe aphasia or inattention, unstable medical conditions which&#xD;
             may affect participation (e.g. unresolved sepsis, postural hypotension) or anticipated&#xD;
             life expectancy of &lt;1 year due to malignancy or neurodegenerative disorder)&#xD;
&#xD;
          -  Hemispatial neglect (visual or sensory) or severe visual impairment despite visual&#xD;
             aids&#xD;
&#xD;
          -  History of severe depression or active psychiatric disorder&#xD;
&#xD;
          -  Skull defect or previous cranial surgery as this would affect physical fit of EEG cap&#xD;
             interface&#xD;
&#xD;
          -  Local arm factors: severe spasticity Modified Ashworth scale &gt;1+ in any region, fixed&#xD;
             joint contractures or joint replacements, patients with poor skin conditions which&#xD;
             would contraindicate repetitive arm training, upper limb pain impeding movements with&#xD;
             visual analogue scale (VAS score) &gt;4/10, other conditions ensuing upper limb weakness,&#xD;
             skull defect, polydactyly or amputation of fingers, and allergy to electrodes or&#xD;
             adhesive gel&#xD;
      </textblock>
    </criteria>
    <gender>All</gender>
    <minimum_age>55 Years</minimum_age>
    <maximum_age>90 Years</maximum_age>
    <healthy_volunteers>No</healthy_volunteers>
  </eligibility>
  <overall_official>
    <last_name>Jeong Hoon Lim</last_name>
    <role>Principal Investigator</role>
    <affiliation>National University Hospital, Singapore</affiliation>
  </overall_official>
  <location>
    <facility>
      <name>National University Hospital</name>
      <address>
        <city>Singapore</city>
        <zip>119074</zip>
        <country>Singapore</country>
      </address>
    </facility>
  </location>
  <location_countries>
    <country>Singapore</country>
  </location_countries>
  <reference>
    <citation>F.Z. Low, H.K. Yap, J.H. Lim, F. Nasrallah, J.C.H. Goh, C.H. Yeow. Wearable Soft Robotics for Rehabilitation. 8th Asian Pacific Conference on Biomechanics 2015 (Sapporo).</citation>
  </reference>
  <reference>
    <citation>H.K. Yap, F. Nasrahllah, J.H. Lim, F.Z. Low, J.C.H. Goh, C.H. Yeow. MRC-Glove: A fMRI Compatible Soft Robotic Glove for Hand Rehabilitation Application, 14th IEEE/RAS-EMBS International Conference on Rehabilitation Robotics (ICORR 2015).</citation>
  </reference>
  <reference>
    <citation>H.K. Yap, J.H. Lim, F. Nasrahllah, J.C.H. Goh, C.H. Yeow. A Soft Exoskeleton for Hand Assistive and Rehabilitation Application Page 8 using Pneumatic Actuators with Variable Stiffness, in IEEE Int. Conf. Robotics and Automation 2015.</citation>
  </reference>
  <reference>
    <citation>H.K. Yap, J.C.H. Goh, C.H. Yeow. Design and Characterization of Soft Actuator for Hand Rehabilitation Application. 6th European Conference of the International Federation for Medical and Biological Engineering (MBEC 2014)</citation>
  </reference>
  <reference>
    <citation>H.K. Yap, J.H. Lim, F. Nasrallah, J.C.H. Goh, C.H. Yeow. Customizable Pneumatic Bending Actuator for Finger Rehabilitation. Design of Medical Devices Conference Europe 2014 (Delft)</citation>
  </reference>
  <reference>
    <citation>C.H. Yeow, A.T. Baisch, S.G. Talbot, C.J. Walsh. Differential spring stiffness design for finger therapy exercise device: bio-inspired from stiff pathological finger joints. ASME Journal of Medical Devices 2012</citation>
  </reference>
  <reference>
    <citation>Ang KK, Chin ZY, Wang C, Guan C, Zhang H. Filter Bank Common Spatial Pattern Algorithm on BCI Competition IV Datasets 2a and 2b. Front Neurosci. 2012 Mar 29;6:39. doi: 10.3389/fnins.2012.00039. eCollection 2012.</citation>
    <PMID>22479236</PMID>
  </reference>
  <reference>
    <citation>Ang KK, Guan C, Phua KS, Wang C, Zhao L, Teo WP, Chen C, Ng YS, Chew E. Facilitating effects of transcranial direct current stimulation on motor imagery brain-computer interface with robotic feedback for stroke rehabilitation. Arch Phys Med Rehabil. 2015 Mar;96(3 Suppl):S79-87. doi: 10.1016/j.apmr.2014.08.008.</citation>
    <PMID>25721551</PMID>
  </reference>
  <reference>
    <citation>Ang KK, Chua KS, Phua KS, Wang C, Chin ZY, Kuah CW, Low W, Guan C. A Randomized Controlled Trial of EEG-Based Motor Imagery Brain-Computer Interface Robotic Rehabilitation for Stroke. Clin EEG Neurosci. 2015 Oct;46(4):310-20. doi: 10.1177/1550059414522229. Epub 2014 Apr 21.</citation>
    <PMID>24756025</PMID>
  </reference>
  <reference>
    <citation>Ang KK, Guan C, Phua KS, Wang C, Zhou L, Tang KY, Ephraim Joseph GJ, Kuah CW, Chua KS. Brain-computer interface-based robotic end effector system for wrist and hand rehabilitation: results of a three-armed randomized controlled trial for chronic stroke. Front Neuroeng. 2014 Jul 29;7:30. doi: 10.3389/fneng.2014.00030. eCollection 2014.</citation>
    <PMID>25120465</PMID>
  </reference>
  <reference>
    <citation>Rojas JI, Zurru MC, Romano M, Patrucco L, Cristiano E. Acute ischemic stroke and transient ischemic attack in the very old--risk factor profile and stroke subtype between patients older than 80 years and patients aged less than 80 years. Eur J Neurol. 2007 Aug;14(8):895-9. doi: 10.1111/j.1468-1331.2007.01841.x.</citation>
    <PMID>17662011</PMID>
  </reference>
  <reference>
    <citation>Rosamond W, Flegal K, Furie K, Go A, Greenlund K, Haase N, Hailpern SM, Ho M, Howard V, Kissela B, Kittner S, Lloyd-Jones D, McDermott M, Meigs J, Moy C, Nichol G, O'Donnell C, Roger V, Sorlie P, Steinberger J, Thom T, Wilson M, Hong Y; American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Heart disease and stroke statistics--2008 update: a report from the American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Circulation. 2008 Jan 29;117(4):e25-146. doi: 10.1161/CIRCULATIONAHA.107.187998. Epub 2007 Dec 17. No abstract available. Erratum In: Circulation. 2010 Jul 6;122(1):e10. Kissela, Bret [corrected to Kissela, Brett].</citation>
    <PMID>18086926</PMID>
  </reference>
  <reference>
    <citation>Mozaffarian D, Benjamin EJ, Go AS, Arnett DK, Blaha MJ, Cushman M, de Ferranti S, Despres JP, Fullerton HJ, Howard VJ, Huffman MD, Judd SE, Kissela BM, Lackland DT, Lichtman JH, Lisabeth LD, Liu S, Mackey RH, Matchar DB, McGuire DK, Mohler ER 3rd, Moy CS, Muntner P, Mussolino ME, Nasir K, Neumar RW, Nichol G, Palaniappan L, Pandey DK, Reeves MJ, Rodriguez CJ, Sorlie PD, Stein J, Towfighi A, Turan TN, Virani SS, Willey JZ, Woo D, Yeh RW, Turner MB; American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Heart disease and stroke statistics--2015 update: a report from the American Heart Association. Circulation. 2015 Jan 27;131(4):e29-322. doi: 10.1161/CIR.0000000000000152. Epub 2014 Dec 17. No abstract available. Erratum In: Circulation. 2015 Jun 16;131(24):e535. Circulation. 2016 Feb 23;133(8):e417.</citation>
    <PMID>25520374</PMID>
  </reference>
  <reference>
    <citation>Venketasubramanian N, Chen CL. Burden of stroke in Singapore. Int J Stroke. 2008 Feb;3(1):51-4. doi: 10.1111/j.1747-4949.2008.00181.x.</citation>
    <PMID>18705915</PMID>
  </reference>
  <results_reference>
    <citation>Hong Kai Yap, Kamaldin N, Jeong Hoon Lim, Nasrallah FA, Goh JCH, Chen-Hua Yeow. A Magnetic Resonance Compatible Soft Wearable Robotic Glove for Hand Rehabilitation and Brain Imaging. IEEE Trans Neural Syst Rehabil Eng. 2017 Jun;25(6):782-793. doi: 10.1109/TNSRE.2016.2602941. Epub 2016 Aug 25.</citation>
    <PMID>28113591</PMID>
  </results_reference>
  <verification_date>April 2019</verification_date>
  <study_first_submitted>April 21, 2017</study_first_submitted>
  <study_first_submitted_qc>September 6, 2017</study_first_submitted_qc>
  <study_first_posted type="Actual">September 11, 2017</study_first_posted>
  <last_update_submitted>April 29, 2019</last_update_submitted>
  <last_update_submitted_qc>April 29, 2019</last_update_submitted_qc>
  <last_update_posted type="Actual">April 30, 2019</last_update_posted>
  <responsible_party>
    <responsible_party_type>Sponsor</responsible_party_type>
  </responsible_party>
  <keyword>Brain-computer integration</keyword>
  <condition_browse>
    <!-- CAUTION:  The following MeSH terms are assigned with an imperfect algorithm            -->
    <mesh_term>Stroke</mesh_term>
  </condition_browse>
  <patient_data>
    <sharing_ipd>No</sharing_ipd>
  </patient_data>
  <!-- Results have not yet been posted for this study                                          -->
</clinical_study>


download date: ClinicalTrials.gov processed this data on September 20, 2023
link text: Link to the current ClinicalTrials.gov record.
url: https://clinicaltrials.gov/ct2/show/NCT03277508
org study id: 2017/00312
nct id: NCT03277508
lead sponsor: 
collaborator: 
collaborator: 
has dmc: No
is fda regulated drug: No
is fda regulated device: No
This research aims to integrate and develop a novel Brain-Computer Interface (BCI) controlled
      soft robotic glove, evaluate the ability of the glove in achieving common hand grasping
      postures and to assess the efficacy of the glove in assisting stroke patients with completing
      functional tasks. The BCI-controlled soft robotic glove utilizes patients' user intent to
      deliver specific electroencephalographic patterns that can trigger robot-assisted hand
      movement to the affected hand.
Hand motor impairment is very common after a stroke. These impairments include difficulty
      moving and coordinating the hands and fingers, which inhibits stroke patients from being able
      to perform daily functional tasks independently, resulting in a reduced quality of life. More
      than half of people with upper limb impairment after stroke will still have problems many
      months to years after their stroke. Therefore, improving hand function is a core element of
      rehabilitation. Many possible interventions have been developed; these may involve different
      exercises or training, specialist equipment or techniques, or they could take the form of a
      drug (pill or injection) given to help hand movement. There is limited evidence that suggests
      the following interventions may be effective: constraint-induced movement therapy, mental
      practice, mirror therapy,interventions for sensory impairment, virtual reality and a
      relatively high dose of repetitive task practice. Current hand rehabilitation robotic devices
      are typically driven by rigid linkages or joints, which subject the patient's fingers into a
      single plane of motion that will feel unnatural and uncomfortable. On top of that,these
      devices belong to the class of continuous passive motion (CPM) devices that only promote hand
      range-of-motion, but do not require the patient to play a semi-active role in performing the
      hand exercises. Furthermore, there is a huge demand for solutions assisting stroke patients
      with using the densely paralyzed hand to perform activities of daily living (ADL) in real
      life, which is not available at present. Most of the hand rehabilitation robotic devices
      available in the market cannot assist paralyzed hand to carry out ADL. To develop an
      assistive device to solve this unmet need, we decided to combine BCI technology with the
      wearable soft robotic glove, which enables actuation of paralyzed hand by motor imagery.
allocation: Randomized
intervention model: Parallel Assignment
intervention model description: A randomization stratification method, with a computer generated random sequence will be used. Subjects will be grouped in strata according to their Fugl-Meyer scores(0-28 vs 29-45) and randomized separately according to a block randomization to receive either of following treatments:(1) Treatment A: 18 sessions for 6 weeks of 60 minutes of passive robot-assisted hand therapy+ 30 minutes of standard hand therapy.(2) Treatment B: 18 sessions for 6 weeks of 60 minutes of BCI controlled robot-assisted hand therapy + 30 minutes of standard hand therapy.
primary purpose: Device Feasibility
masking: Single (Outcomes Assessor)
masking description: The blinding will be carried out in such a way that the therapist will provide conventional therapy and assess the arm function without knowing the group that the patients belong to. The BCI Robot and Passive Robot will be administered by other study team member. A third party study team member not participating in the intervention will keep the log book which contains information regarding patients study ID and group. Randomization codes will be broken when all interventional and follow-up sessions were completed or when the patient decides to quit participation. The third party team member will unmask according to PI's request in this occasion.
measure: Action Research Arm Test
time frame: 30 minutes
description: It assesses a client's ability to handle objects differing in size, weight and shape and therefore can be considered to be an arm-specific measure of activity limitation
measure: Fugl-Meyer Assessment
time frame: 20 minutes
description: It is designed to assess motor functioning, balance, sensation and joint functioning.
measure: Grip Strength Test
time frame: 5 minutes
description: The purpose of this test is to measure the maximum isometric strength of the hand and forearm muscles.
arm group label: BCI robot assisted hand therapy
arm group type: Experimental
description: Brain-computer integration robot assisted hand therapy
arm group label: CPM robot assisted hand therapy
arm group type: Active Comparator
description: Continue Passive Movement robot assisted hand therapy
intervention type: Device
intervention name: BCI Robot Assisted Hand Therapy
description: 18 sessions (3 times per week for a total of 6 weeks) of 1.5 hours each training (60 minutes of BCI Robot and 30 minutes of the standard hand therapy).
Session 1-3. Peg game: Lateral movement of gears/cups Session 4-6. Moving cups onto a shelf: Elevation process for the arm, small cups to be used.
Session 7-9. Carrying of basket: Heavier load to be used, patient to hold the basket by the handles at the side, not by its base.
Session 10-12. Opening bottle + pouring into a cup: Training of ADL Session 13-15. Eating: Use of a modified spoon to train ADL Session 16-18. Box and blocks: Precise index finger and thumb control
arm group label: BCI robot assisted hand therapy
intervention type: Device
intervention name: CPM Robot Assisted Hand Therapy
description: 18 sessions (3 times per week for a total of 6 weeks) of 1.5 hours each training (60 minutes of Passive Robot and 30 minutes of the standard hand therapy).The procedure will include the following: Session 1-18: Continuous passive motion
arm group label: CPM robot assisted hand therapy
criteria: 
gender: All
minimum age: 55 Years
maximum age: 90 Years
healthy volunteers: No
last name: Jeong Hoon Lim
role: Principal Investigator
affiliation: National University Hospital, Singapore
facility: 
country: Singapore
citation: F.Z. Low, H.K. Yap, J.H. Lim, F. Nasrallah, J.C.H. Goh, C.H. Yeow. Wearable Soft Robotics for Rehabilitation. 8th Asian Pacific Conference on Biomechanics 2015 (Sapporo).
citation: H.K. Yap, F. Nasrahllah, J.H. Lim, F.Z. Low, J.C.H. Goh, C.H. Yeow. MRC-Glove: A fMRI Compatible Soft Robotic Glove for Hand Rehabilitation Application, 14th IEEE/RAS-EMBS International Conference on Rehabilitation Robotics (ICORR 2015).
citation: H.K. Yap, J.H. Lim, F. Nasrahllah, J.C.H. Goh, C.H. Yeow. A Soft Exoskeleton for Hand Assistive and Rehabilitation Application Page 8 using Pneumatic Actuators with Variable Stiffness, in IEEE Int. Conf. Robotics and Automation 2015.
citation: H.K. Yap, J.C.H. Goh, C.H. Yeow. Design and Characterization of Soft Actuator for Hand Rehabilitation Application. 6th European Conference of the International Federation for Medical and Biological Engineering (MBEC 2014)
citation: H.K. Yap, J.H. Lim, F. Nasrallah, J.C.H. Goh, C.H. Yeow. Customizable Pneumatic Bending Actuator for Finger Rehabilitation. Design of Medical Devices Conference Europe 2014 (Delft)
citation: C.H. Yeow, A.T. Baisch, S.G. Talbot, C.J. Walsh. Differential spring stiffness design for finger therapy exercise device: bio-inspired from stiff pathological finger joints. ASME Journal of Medical Devices 2012
citation: Ang KK, Chin ZY, Wang C, Guan C, Zhang H. Filter Bank Common Spatial Pattern Algorithm on BCI Competition IV Datasets 2a and 2b. Front Neurosci. 2012 Mar 29;6:39. doi: 10.3389/fnins.2012.00039. eCollection 2012.
PMID: 22479236
citation: Ang KK, Guan C, Phua KS, Wang C, Zhao L, Teo WP, Chen C, Ng YS, Chew E. Facilitating effects of transcranial direct current stimulation on motor imagery brain-computer interface with robotic feedback for stroke rehabilitation. Arch Phys Med Rehabil. 2015 Mar;96(3 Suppl):S79-87. doi: 10.1016/j.apmr.2014.08.008.
PMID: 25721551
citation: Ang KK, Chua KS, Phua KS, Wang C, Chin ZY, Kuah CW, Low W, Guan C. A Randomized Controlled Trial of EEG-Based Motor Imagery Brain-Computer Interface Robotic Rehabilitation for Stroke. Clin EEG Neurosci. 2015 Oct;46(4):310-20. doi: 10.1177/1550059414522229. Epub 2014 Apr 21.
PMID: 24756025
citation: Ang KK, Guan C, Phua KS, Wang C, Zhou L, Tang KY, Ephraim Joseph GJ, Kuah CW, Chua KS. Brain-computer interface-based robotic end effector system for wrist and hand rehabilitation: results of a three-armed randomized controlled trial for chronic stroke. Front Neuroeng. 2014 Jul 29;7:30. doi: 10.3389/fneng.2014.00030. eCollection 2014.
PMID: 25120465
citation: Rojas JI, Zurru MC, Romano M, Patrucco L, Cristiano E. Acute ischemic stroke and transient ischemic attack in the very old--risk factor profile and stroke subtype between patients older than 80 years and patients aged less than 80 years. Eur J Neurol. 2007 Aug;14(8):895-9. doi: 10.1111/j.1468-1331.2007.01841.x.
PMID: 17662011
citation: Rosamond W, Flegal K, Furie K, Go A, Greenlund K, Haase N, Hailpern SM, Ho M, Howard V, Kissela B, Kittner S, Lloyd-Jones D, McDermott M, Meigs J, Moy C, Nichol G, O'Donnell C, Roger V, Sorlie P, Steinberger J, Thom T, Wilson M, Hong Y; American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Heart disease and stroke statistics--2008 update: a report from the American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Circulation. 2008 Jan 29;117(4):e25-146. doi: 10.1161/CIRCULATIONAHA.107.187998. Epub 2007 Dec 17. No abstract available. Erratum In: Circulation. 2010 Jul 6;122(1):e10. Kissela, Bret [corrected to Kissela, Brett].
PMID: 18086926
citation: Mozaffarian D, Benjamin EJ, Go AS, Arnett DK, Blaha MJ, Cushman M, de Ferranti S, Despres JP, Fullerton HJ, Howard VJ, Huffman MD, Judd SE, Kissela BM, Lackland DT, Lichtman JH, Lisabeth LD, Liu S, Mackey RH, Matchar DB, McGuire DK, Mohler ER 3rd, Moy CS, Muntner P, Mussolino ME, Nasir K, Neumar RW, Nichol G, Palaniappan L, Pandey DK, Reeves MJ, Rodriguez CJ, Sorlie PD, Stein J, Towfighi A, Turan TN, Virani SS, Willey JZ, Woo D, Yeh RW, Turner MB; American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Heart disease and stroke statistics--2015 update: a report from the American Heart Association. Circulation. 2015 Jan 27;131(4):e29-322. doi: 10.1161/CIR.0000000000000152. Epub 2014 Dec 17. No abstract available. Erratum In: Circulation. 2015 Jun 16;131(24):e535. Circulation. 2016 Feb 23;133(8):e417.
PMID: 25520374
citation: Venketasubramanian N, Chen CL. Burden of stroke in Singapore. Int J Stroke. 2008 Feb;3(1):51-4. doi: 10.1111/j.1747-4949.2008.00181.x.
PMID: 18705915
citation: Hong Kai Yap, Kamaldin N, Jeong Hoon Lim, Nasrallah FA, Goh JCH, Chen-Hua Yeow. A Magnetic Resonance Compatible Soft Wearable Robotic Glove for Hand Rehabilitation and Brain Imaging. IEEE Trans Neural Syst Rehabil Eng. 2017 Jun;25(6):782-793. doi: 10.1109/TNSRE.2016.2602941. Epub 2016 Aug 25.
PMID: 28113591
responsible party type: Sponsor
mesh term: Stroke
sharing ipd: No