Pub Date : 2013-06-24DOI: 10.1109/ICORR.2013.6650419
Fengjun Bai, C. Chew, Jinfu Li, Bingquan Shen, T. M. Lubecki
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.
{"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}
Pub Date : 2013-06-24DOI: 10.1109/ICORR.2013.6650445
Morgan K. Boes, Mazharul Islam, Y. Li, E. Hsiao-Wecksler
A Portable Powered Ankle-Foot Orthosis (PPAFO) has been designed for gait assistance. The PPAFO can supply assistive torque at the ankle joint in plantarflexion and dorsiflexion using a bidirectional pneumatic actuator. Two control schemes have been developed to regulate timings of the assistive torques during different phases in the gait cycle. The Direct Event (DE) controller uses heel and toe force sensors to detect the start and end of key phases using specific events (e.g., heel strike and toe-off). The State Estimation (SE) controller finds the least-square-error between real-time sensor data and a reference model from training data to estimate the gait state and to detect phases based on this estimate. A pneumatic recycling scheme for improved fuel efficiency was also implemented. This scheme regenerates energy from plantarflexion exhaust gas to power dorsiflexion actuation. The objective of this study was to assess the fuel efficiency of these two controllers and pneumatic recycling scheme, as measured by fuel consumption and work output. Data were collected from 3 minute walking trials with the PPAFO by five healthy young control subjects. The SE with recycling (SER) scheme had an average fuel savings of 25% compared to the SE control scheme, and 24% compared to the DE controller. The SER controller allowed for comparable net work output to the SE controller which both did more net work than the DE controller. These observations can be applicable to other portable fluid-powered orthotics, prosthetics, and robotics in terms of potential impact of controller choice and energy regeneration on fuel consumption.
{"title":"Fuel efficiency of a Portable Powered Ankle-Foot Orthosis","authors":"Morgan K. Boes, Mazharul Islam, Y. Li, E. Hsiao-Wecksler","doi":"10.1109/ICORR.2013.6650445","DOIUrl":"https://doi.org/10.1109/ICORR.2013.6650445","url":null,"abstract":"A Portable Powered Ankle-Foot Orthosis (PPAFO) has been designed for gait assistance. The PPAFO can supply assistive torque at the ankle joint in plantarflexion and dorsiflexion using a bidirectional pneumatic actuator. Two control schemes have been developed to regulate timings of the assistive torques during different phases in the gait cycle. The Direct Event (DE) controller uses heel and toe force sensors to detect the start and end of key phases using specific events (e.g., heel strike and toe-off). The State Estimation (SE) controller finds the least-square-error between real-time sensor data and a reference model from training data to estimate the gait state and to detect phases based on this estimate. A pneumatic recycling scheme for improved fuel efficiency was also implemented. This scheme regenerates energy from plantarflexion exhaust gas to power dorsiflexion actuation. The objective of this study was to assess the fuel efficiency of these two controllers and pneumatic recycling scheme, as measured by fuel consumption and work output. Data were collected from 3 minute walking trials with the PPAFO by five healthy young control subjects. The SE with recycling (SER) scheme had an average fuel savings of 25% compared to the SE control scheme, and 24% compared to the DE controller. The SER controller allowed for comparable net work output to the SE controller which both did more net work than the DE controller. These observations can be applicable to other portable fluid-powered orthotics, prosthetics, and robotics in terms of potential impact of controller choice and energy regeneration on fuel consumption.","PeriodicalId":340643,"journal":{"name":"2013 IEEE 13th International Conference on Rehabilitation Robotics (ICORR)","volume":"51 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":"126586246","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}
Pub Date : 2013-06-24DOI: 10.1109/ICORR.2013.6650493
T. Exell, C. Freeman, K. Meadmore, M. Kutlu, E. Rogers, A. Hughes, E. Hallewell, J. Burridge
An upper-limb stroke rehabilitation system is developed that assists patients in performing real world functionally relevant reaching tasks. The system provides de-weighting of the arm via a simple spring support whilst functional electrical stimulation is applied to the anterior deltoid and triceps via surface electrodes, and to the wrist and hand extensors via a 40 element surface electrode array. Iterative learning control (ILC) is used to mediate the electrical stimulation, and updates the stimulation signal applied to each muscle group based on the error between the ideal and actual movement in the previous attempt. The control system applies the minimum amount of stimulation required, maximising voluntary effort. Low-cost, markerless motion tracking is provided via a Microsoft Kinect, with hand and wrist data provided by an electrogoniometer or data glove. The system is described and initial experimental results are presented for a stroke patient starting treatment.
{"title":"Goal orientated stroke rehabilitation utilising electrical stimulation, iterative learning and Microsoft Kinect","authors":"T. Exell, C. Freeman, K. Meadmore, M. Kutlu, E. Rogers, A. Hughes, E. Hallewell, J. Burridge","doi":"10.1109/ICORR.2013.6650493","DOIUrl":"https://doi.org/10.1109/ICORR.2013.6650493","url":null,"abstract":"An upper-limb stroke rehabilitation system is developed that assists patients in performing real world functionally relevant reaching tasks. The system provides de-weighting of the arm via a simple spring support whilst functional electrical stimulation is applied to the anterior deltoid and triceps via surface electrodes, and to the wrist and hand extensors via a 40 element surface electrode array. Iterative learning control (ILC) is used to mediate the electrical stimulation, and updates the stimulation signal applied to each muscle group based on the error between the ideal and actual movement in the previous attempt. The control system applies the minimum amount of stimulation required, maximising voluntary effort. Low-cost, markerless motion tracking is provided via a Microsoft Kinect, with hand and wrist data provided by an electrogoniometer or data glove. The system is described and initial experimental results are presented for a stroke patient starting treatment.","PeriodicalId":340643,"journal":{"name":"2013 IEEE 13th International Conference on Rehabilitation Robotics (ICORR)","volume":"42 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":"133673660","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}
Pub Date : 2013-06-01DOI: 10.1109/ICORR.2013.6650492
R. Kõiva, Barbara Hilsenbeck, Claudio Castellini
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.
{"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}
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