scholarly journals A Smart Terrain Identification Technique Based on Electromyography, Ground Reaction Force, and Machine Learning for Lower Limb Rehabilitation

2020 ◽  
Vol 10 (8) ◽  
pp. 2638 ◽  
Author(s):  
Shuo Gao ◽  
Yixuan Wang ◽  
Chaoming Fang ◽  
Lijun Xu
Keyword(s):  
Machine Learning ◽  
Ground Reaction Force ◽  
Lower Limb ◽  
Confusion Matrix ◽  
Reaction Force ◽  
Simple System ◽  
Support Vector ◽  
Terrain Classification ◽  
Limb Rehabilitation ◽  
Lower Limb Rehabilitation

Automatic terrain classification in lower limb rehabilitation systems has gained worldwide attention. In this field, a simple system architecture and high classification accuracy are two desired attributes. In this article, a smart neuromuscular–mechanical fusion and machine learning-based terrain classification technique utilizing only two electromyography (EMG) sensors and two ground reaction force (GRF) sensors is reported for classifying three different terrains (downhill, level, and uphill). The EMG and GRF signals from ten healthy subjects were collected, preprocessed and segmented to obtain the EMG and GRF profiles in each stride, based on which twenty-one statistical features, including 9 GRF features and 12 EMG features, were extracted. A support vector machine (SVM) machine learning model is established and trained by the extracted EMG features, GRF features and the fusion of them, respectively. Several methods or statistical metrics were used to evaluate the goodness of the proposed technique, including a paired-t-test and Kruskal–Wallis test for correlation analysis of the selected features and ten-fold cross-validation accuracy, confusion matrix, sensitivity and specificity for the performance of the SVM model. The results show that the extracted features are highly correlated with the terrain changes and the fusion of the EMG and GRF features produces the highest accuracy of 96.8%. The presented technique allows simple system construction to achieve the precise detection of outcomes, potentially advancing the development of terrain classification techniques for rehabilitation.

Gait Prediction and Mechanism Design for 1-DOF Lower Limb Rehabilitation Devices Based on Machine Learning

2021 ◽  
Author(s):  
Wanbing Song ◽  
Yating Zhang ◽  
Zhaojie Ge ◽  
Ping Zhao
Keyword(s):  
Machine Learning ◽  
Mechanism Design ◽  
Lower Limb ◽  
Support Vector ◽  
Motion Trajectory ◽  
Trajectory Prediction ◽  
Machine Method ◽  
Specific Patient ◽  
Limb Rehabilitation ◽  
Body Parameters

Abstract A task motion trajectory usually needs to be determined for the training process and mechanism design for rehabilitation patients since they are not capable of providing a normal motion. In this paper, a machine-learning-based approach of gait trajectory prediction for lower limb rehab patients is proposed to provide the basis for the design of simple 1-degree-of-freedom (DOF) rehab mechanisms. First, a large amount of gait trajectories from various healthy volunteers are collected along with their body parameters, and a normalization method is presented to trim/expand these trajectory samples to a standard length and timing while retaining their shape and velocity information. Then, these normalized gait samples are clustered and regressed into a limited number of representative trajectories with K-means algorithm, and the cluster index is recorded as the label for each trajectory. Next, a genetic-algorithm-optimized support vector machine method is adopted to train a classifier for the trajectories, obtaining the correspondence between body parameters and cluster labels of gait trajectories. As a result, once a group of body parameters are input into the classifier, it can predict a most suitable gait trajectory for the specific patient. It shows that the accuracy of trajectory prediction reaches 96% both on training set and test set which verifies the effectiveness of the method. In the end, a 1-DOF gait rehab mechanism design example is provided to illustrate the application of the proposed method. Taking the predicted result from the classifier as the task motion trajectory for the synthesis of mechanisms, a 1-DOF six-bar mechanism is designed and the patient-mechanism matching can be realized.

Recent Advances on Horizontal Lower Limb Rehabilitation Robot

2017 ◽  
Vol 10 (2) ◽  
Author(s):  
Jingang Jiang ◽  
Xuefeng Ma ◽  
Biao Huo ◽  
Xiaoyang Yu ◽  
Xiaowei Guo ◽  
...  
Keyword(s):  
Lower Limb ◽  
Rehabilitation Robot ◽  
Recent Advances ◽  
Limb Rehabilitation ◽  
Lower Limb Rehabilitation

scholarly journals Predictive Modelling of Employee Turnover in Indian IT Industry Using Machine Learning Techniques

2019 ◽  
Vol 23 (1) ◽  
pp. 12-21 ◽  
Author(s):  
Shikha N. Khera ◽  
Divya
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Confusion Matrix ◽  
Predictive Modelling ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
It Industry ◽  
Knowledge Based ◽  
Employee Attrition

Information technology (IT) industry in India has been facing a systemic issue of high attrition in the past few years, resulting in monetary and knowledge-based loses to the companies. The aim of this research is to develop a model to predict employee attrition and provide the organizations opportunities to address any issue and improve retention. Predictive model was developed based on supervised machine learning algorithm, support vector machine (SVM). Archival employee data (consisting of 22 input features) were collected from Human Resource databases of three IT companies in India, including their employment status (response variable) at the time of collection. Accuracy results from the confusion matrix for the SVM model showed that the model has an accuracy of 85 per cent. Also, results show that the model performs better in predicting who will leave the firm as compared to predicting who will not leave the company.

scholarly journals Running ground reaction forces across footwear conditions are predicted from the motion of two body mass components

2019 ◽  
Vol 126 (5) ◽  
pp. 1315-1325 ◽  
Author(s):  
Andrew B. Udofa ◽  
Kenneth P. Clark ◽  
Laurence J. Ryan ◽  
Peter G. Weyand
Keyword(s):  
Ground Reaction Force ◽  
Lower Limb ◽  
Reaction Force ◽  
Ground Reaction Forces ◽  
Vertical Ground Reaction Force ◽  
Time Patterns ◽  
Foot Strike ◽  
Reaction Forces ◽  
Vertical Ground ◽  
Force Time

Although running shoes alter foot-ground reaction forces, particularly during impact, how they do so is incompletely understood. Here, we hypothesized that footwear effects on running ground reaction force-time patterns can be accurately predicted from the motion of two components of the body’s mass (mb): the contacting lower-limb (m1 = 0.08mb) and the remainder (m2 = 0.92mb). Simultaneous motion and vertical ground reaction force-time data were acquired at 1,000 Hz from eight uninstructed subjects running on a force-instrumented treadmill at 4.0 and 7.0 m/s under four footwear conditions: barefoot, minimal sole, thin sole, and thick sole. Vertical ground reaction force-time patterns were generated from the two-mass model using body mass and footfall-specific measures of contact time, aerial time, and lower-limb impact deceleration. Model force-time patterns generated using the empirical inputs acquired for each footfall matched the measured patterns closely across the four footwear conditions at both protocol speeds ( r2 = 0.96 ± 0.004; root mean squared error  = 0.17 ± 0.01 body-weight units; n = 275 total footfalls). Foot landing angles (θF) were inversely related to footwear thickness; more positive or plantar-flexed landing angles coincided with longer-impact durations and force-time patterns lacking distinct rising-edge force peaks. Our results support three conclusions: 1) running ground reaction force-time patterns across footwear conditions can be accurately predicted using our two-mass, two-impulse model, 2) impact forces, regardless of foot strike mechanics, can be accurately quantified from lower-limb motion and a fixed anatomical mass (0.08mb), and 3) runners maintain similar loading rates (ΔFvertical/Δtime) across footwear conditions by altering foot strike angle to regulate the duration of impact. NEW & NOTEWORTHY Here, we validate a two-mass, two-impulse model of running vertical ground reaction forces across four footwear thickness conditions (barefoot, minimal, thin, thick). Our model allows the impact portion of the impulse to be extracted from measured total ground reaction force-time patterns using motion data from the ankle. The gait adjustments observed across footwear conditions revealed that runners maintained similar loading rates across footwear conditions by altering foot strike angles to regulate the duration of impact.

Impedance Control Based Sliding Mode for Lower Limb Rehabilitation Robot

2014 ◽  
Vol 672-674 ◽  
pp. 1770-1773 ◽  
Author(s):  
Fu Cheng Cao ◽  
Li Min Du
Keyword(s):  
Dynamic Response ◽  
Sliding Mode Control ◽  
Lower Limb ◽  
Control Method ◽  
Sliding Mode ◽  
Impedance Control ◽  
Rehabilitation Robot ◽  
Active Rehabilitation ◽  
Limb Rehabilitation ◽  
Lower Limb Rehabilitation

Aimed at improving the dynamic response of the lower limb for patients, an impedance control method based on sliding mode was presented to implement an active rehabilitation. Impedance control can achieve a target-reaching training without the help of a therapist and sliding mode control has a robustness to system uncertainty and vary limb strength. Simulations demonstrate the efficacy of the proposed method for lower limb rehabilitation.

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