The design of a rehabilitation training system with EMG feedback for stroke patients

2015 ◽  
pp. 277-282
Author(s):  
C Yu ◽  
J Guo ◽  
Z Yu ◽  
H Niu ◽  
Y Fan ◽  
...  
Keyword(s):  
Training System ◽  
Stroke Patients ◽  
Rehabilitation Training ◽  
Emg Feedback

Deep learning analysis based on multi-sensor fusion data for hemiplegia rehabilitation training system for stoke patients

Robotica ◽  
2021 ◽  
pp. 1-18
Author(s):  
Peng Zhang ◽  
Junxia Zhang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Short Term Memory ◽  
Training System ◽  
Sensor Data ◽  
Hybrid Network ◽  
Stroke Patients ◽  
Rehabilitation Training ◽  
Lower Limb Exoskeleton ◽  
Active Rehabilitation

Abstract By recognizing the motion of the healthy side, the lower limb exoskeleton robot can provide therapy to the affected side of stroke patients. To improve the accuracy of motion intention recognition based on sensor data, the research based on deep learning was carried out. Eighty healthy subjects performed gait experiments under five different gait environments (flat ground, 10 ${}^\circ$ upslope and downslope, and upstairs and downstairs) by simulating stroke patients. To facilitate the training and classification of the neural network, this paper presents template processing schemes to adapt to different data formats. The novel algorithm model of a hybrid network model based on convolutional neural network (CNN) and Long–short-term memory (LSTM) model is constructed. To mitigate the data-sparse problem, a spatial–temporal-embedded LSTM model (SQLSTM) combining spatial–temporal influence with the LSTM model is proposed. The proposed CNN-SQLSTM model is evaluated on a real trajectory dataset, and the results demonstrate the effectiveness of the proposed model. The proposed method will be used to guide the control strategy design of robot system for active rehabilitation training.

scholarly journals Clinical Efficacy of a New Robot-assisted Gait Training System for Acute Stroke Patients

2021 ◽  
Author(s):  
Andy Chien ◽  
Fei-Chun Chang ◽  
Nai-Hsin Meng ◽  
Pei-Yu Yang ◽  
Ching Huang ◽  
...  
Keyword(s):  
Sleep Quality ◽  
Acute Stroke ◽  
Quality Index ◽  
Pittsburgh Sleep Quality Index ◽  
Gait Training ◽  
Training System ◽  
Control Group ◽  
Stroke Patients ◽  
Robot Assisted ◽  
Significant Difference

Abstract Purpose Robot-assisted gait rehabilitation has been proposed as a plausible supplementary rehabilitation strategy in stroke rehabilitation in the last decade. However, its exact benefit over traditional rehabilitation remain sparse and unclear. It is therefore the purpose of the current study to comparatively investigate the clinical benefits of the additional robot-assisted training in acute stroke patients compared to standard hospital rehabilitation alone. Methods Ninety acute stroke patients (< 3 month) were recruited. All participants received the standard hospital neurorehabilitation comprises 45–60 min sessions daily for 3 weeks. Sixty patients also received an additional 30 min of robot-assisted gait training with the HIWIN MRG-P100 gait training system after each of the standard neurorehabilitation session. Outcome measures included: 1. Berg Balance Scale (BBS); 2. Brunnstrom Stage; 3. Pittsburgh Sleep Quality Index and 4. Taiwanese Depression Questionnaire (TDQ) which were assessed pre-treatment and then after every five training sessions. Results Both groups demonstrated significant improvement pre- and post-treatment for the BBS (robotic group p = 0.023; control group p = 0.033) but no significant difference (p > 0.1) between the groups were found. However, the robotic training group had more participants demonstrating larger BBS points of improvement as well as greater Brunnstrom stage of improvement, when compared to the control group. No significant within and between group statistical differences (p > 0.3) were found for Pittsburgh Sleep Quality Index and Taiwanese Depression Questionnaire. Conclusion The addition of robotic gait training on top of standard hospital neurorehabilitation for acute stroke patients appear to produce a slightly greater improvement in clinical functional outcomes, which is not transferred to psychological status.

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