2013 IEEE 13th International Conference on Rehabilitation Robotics (ICORR)最新文献

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Muscle force estimation method with surface EMG for a lower extremities rehabilitation device 基于表面肌电信号的下肢康复装置肌肉力估计方法
2013 IEEE 13th International Conference on Rehabilitation Robotics (ICORR) Pub Date : 2013-06-24 DOI: 10.1109/ICORR.2013.6650419
Fengjun Bai, C. Chew, Jinfu Li, Bingquan Shen, T. M. Lubecki
{"title":"Muscle force estimation method with surface EMG for a lower extremities rehabilitation device","authors":"Fengjun Bai, C. Chew, Jinfu Li, Bingquan Shen, T. M. Lubecki","doi":"10.1109/ICORR.2013.6650419","DOIUrl":"https://doi.org/10.1109/ICORR.2013.6650419","url":null,"abstract":"This paper presents a new wearable lower extremities assistive robotic device that aims at providing assistive torque for stroke patients during rehabilitation process. The device specifically provides the assistive torque by detecting the user's intention using surface electromyography (EMG) signals with the force/torque estimation method based on continuous wavelet transform (CWT). The general hardware design of the current rehabilitation prototype was developed. Experiments were conducted to collect hamstring and quadriceps muscles EMG signals from 10 healthy subjects. Data analysis was carried out to evaluate the feasibility of the proposed human force/torque estimation algorithm. The force/torque estimation results show high implementation feasibility for the assistive device. Online tests were also carried out with the assistive device using the EMG signal to command motors. The output estimation force, hip and knee joint positions were obtained from the real-time implementation.","PeriodicalId":340643,"journal":{"name":"2013 IEEE 13th International Conference on Rehabilitation Robotics (ICORR)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122577156","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 8
Evaluating subsampling strategies for sEMG-based prediction of voluntary muscle contractions 评估基于表面肌电信号预测随意肌收缩的子采样策略
2013 IEEE 13th International Conference on Rehabilitation Robotics (ICORR) Pub Date : 2013-06-01 DOI: 10.1109/ICORR.2013.6650492
R. Kõiva, Barbara Hilsenbeck, Claudio Castellini
{"title":"Evaluating subsampling strategies for sEMG-based prediction of voluntary muscle contractions","authors":"R. Kõiva, Barbara Hilsenbeck, Claudio Castellini","doi":"10.1109/ICORR.2013.6650492","DOIUrl":"https://doi.org/10.1109/ICORR.2013.6650492","url":null,"abstract":"In previous work we showed that some human Voluntary Muscle Contractions (VMCs) of high interest to the prosthetics community, namely finger flexions/extensions and thumb rotation, can be effectively predicted using muscle activation signals coming from surface electromyography (sEMG). In this paper we study the effectiveness of various subsampling strategies to limit the size of the training data set, with the aim of extending the approach to an online VMC-prediction system whose main application will be force-controlled hand prostheses. We performed an experiment in which 10 able-bodied participants flexed and extended their fingers according to a visual stimulus, while muscle activations and VMCs (represented as synergistic fingertip forces) were gathered using sEMG electrodes and a custom-built measurement device. A Support Vector Machine (SVM) was trained on a fixed-sized subset of the collected data, obtained using seven different subsampling strategies. The SVM was then tested on subsequent new data. Our experimental results show that two subsampling strategies attain a prediction error as low as 6% to 12%, which is comparable to the error values obtained in our previous work when the entire data set was used and processed offline.","PeriodicalId":340643,"journal":{"name":"2013 IEEE 13th International Conference on Rehabilitation Robotics (ICORR)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132620044","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 9
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