1. Field
The present disclosure relates to a method which recognizes a walking speed intention of a walking trainee wearing a walk assist robot and controls a walking speed of the walk assist robot using the same.
Specifically, the present disclosure relates to a method which recognizes a walking speed intention of a walking trainee from surface electromyogram (EMG) of muscles related to ankle joint extension in the plantar flexor of the walking trainee, and a method which controls a walking speed of a walk assist robot using the same.
Also, the present disclosure relates to a method which selects a linear or non-linear function in proportion to a plantar flexor EMG waveform length maximum value in the stance phase during the gait cycle of a walking trainee, sets coefficients of the function, and recognizes a walking speed intention, and a method which observes changes in plantar pressure or knee joint angle of a walking trainee and determines the end of stance phase, and a method which controls a walking speed of a walk assist robot to conform to a walking speed intention of a walking trainee.
[Description about National Research and Development Support]
This study was supported by the Robot Industry Convergence Technology Development program of Ministry of Trade, Industry and Energy, Republic of Korea (Project No. 1415135300) under the Korea Evaluation Institute of Industrial Technology.
2. Description of the Related Art
Recently, walk assist robots such as exoskeleton type gait rehabilitation training robots and robots for increasing muscular strength are being actively developed. Also, many studies have been made on human-robot interfaces to intuitively operate these robots according to the intention of walking trainees. These studies are about making new intuitive human-robot interface protocols related to robot manoeuvre and applying them to robots, rather than a direct intention related to gait of walking trainees.
A conventional example of this walk assist robot is disclosed in FIG. 10.
The conventional walk assist robot 100 includes a chair 101 and a body weight support system 108 to support a body of a walking trainee, and the body weight support system 108 is supported on a supporting member 103.
Also, through a height adjustment device 102, the height of the body weight support system 108 can be adjusted based on a body size of the walking trainee, and a speed controller 107 disposed at the lower part of the walk assist robot 100 controls the speed of a conveyor 106, and controls the speed of a treadmill 104 through a hinge mechanism 105 connected to the conveyor 106.
However, the conventional walk assist robot 100 can control the walking speed, but simply controls the speed of the treadmill 104 only by a control method of the walk assist robot 100 itself, and does not consider a walking intention of the walking trainee at all.
Thus, there is a need for technology that finds a direct walking intention from bio-signals of walking trainees and applies it to robots, and particularly, in the case of gait rehabilitation training robots, such technology is essential.
Gait rehabilitation training robots are being clinically used mainly for hemiplegic patients after stroke, and their goal is to restore damaged brain functions related to gait through gait training. Thus, what is needed to increase a rehabilitation effect of gait rehabilitation training is not new intuitive human-robot interface protocols, but identifying instructions related to gait carried from the brain to lower limb muscles and controlling robots in accordance with the instructions.
Also, through this, there is a need for development of walk assist robots that contribute to the reduction in social costs for taking care of walking trainees by enabling them to walk almost like normal people independently without wearing robots when they finish rehabilitation training.