Gait Phase Estimation for FES Based on Pelvic Movement of a Novel Gait Rehabilitation Robot

Author(s):  
Jing Ye ◽  
Gong Chen ◽  
Quanquan Liu ◽  
Lihong Duan ◽  
Xifan Yao ◽  
...  
2014 ◽  
Vol 494-495 ◽  
pp. 1084-1087
Author(s):  
Fu Cheng Cao ◽  
Hai Xin Sun ◽  
Li Rong Wang

An iterative learning impedance control algorithm is presented to control a gait rehabilitation robot. According to the circumstances of the patient, the appropriate rehabilitation target impedance parameters are set. With the adoption of iterative learning control law, the impedance error in the closed loop is guaranteed to converge to zero and the iterative trajectories follow the desired trajectories over the entire operation interval. The effectiveness of the proposed method is shown through numerical simulation results.


2021 ◽  
Vol 11 (19) ◽  
pp. 8940
Author(s):  
Wonseok Choi ◽  
Wonseok Yang ◽  
Jaeyoung Na ◽  
Giuk Lee ◽  
Woochul Nam

For gait phase estimation, time-series data of lower-limb motion can be segmented according to time windows. Time-domain features can then be calculated from the signal enclosed in a time window. A set of time-domain features is used for gait phase estimation. In this approach, the components of the feature set and the length of the time window are influential parameters for gait phase estimation. However, optimal parameter values, which determine a feature set and its values, can vary across subjects. Previously, these parameters were determined empirically, which led to a degraded estimation performance. To address this problem, this paper proposes a new feature extraction approach. Specifically, the components of the feature set are selected using a binary genetic algorithm, and the length of the time window is determined through Bayesian optimization. In this approach, the two optimization techniques are integrated to conduct a dual optimization task. The proposed method is validated using data from five walking and five running motions. For walking, the proposed approach reduced the gait phase estimation error from 1.284% to 0.910%, while for running, the error decreased from 1.997% to 1.484%.


2021 ◽  
Author(s):  
Choonghyun Son ◽  
Anna Lee ◽  
Junkyung Lee ◽  
DaeEun Kim ◽  
Seung-Jong Kim ◽  
...  

Abstract Background: Aging societies lead to higher demand for gait rehabilitation as age-related neurological disorders such as stroke increase. Since conventional methods for gait rehabilitation are physically and economically burdensome, robotic gait training systems have been studied and commercialized, many of which provided movements confined in the sagittal plane. For better outcomes of gait rehabilitation with more natural gait patterns, however, it is desirable to provide pelvic movements in the transverse plane. In this study, a robotic gait training system capable of pelvic motions in the transverse plane was used to evaluated the effect of the pelvic motions on stroke patients. Method: Healbot T, which is a robotic gait training system and capable of providing pelvic movements in the transverse plane as well as flexion/extension of the hip and knee joints and adduction/abduction of the hip joints, is introduced and used to evaluate the effect of the pelvic movement on gait training of stroke patients.Experiment: 23 stroke patients with hemiparesis participated in this study and were assigned into two groups. Pelvis-on group was provided with pelvic motions whereas no pelvic movement was allowed for pelvis-off group during 10 sessions of gait trainings in Healbot T. EMG signals and interaction forces as well as the joint angles of the robot were measured. Gait parameters such as stride length, gait period, cadence, and walking speed were measured after gait training. Result: 37.5 % lower interaction forces of pelvis were observed in the pelvis-on group than the pelvis-off group. Furthermore, the interaction forces at the thighs and calves of both groups showed significant decrease. The EMG signals of gluteus medius of the pelvis-on group increased by 77.2 %. Furthermore, statistically significant increases in various muscles were measured in the pelvis-on group during the stance phase. Conclusion: Gait training using a robotic gait training system with pelvic movements was conducted to study the effects of lateral and rotational pelvic movements in gait training of stroke patients. The pelvic movements made gait training less interfered by the exoskeleton while stimulating the voluntary muscle activation during the stance phase. Clinical trial registration: KCT0003762, 2018-1254, Registered 28 October 2018, https://cris.nih.go.kr/cris/search/search_result_st01_kren.jsp?seq=14310


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