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Lakshminarayanan K, Ramu V, Shah R, Haque Sunny MS, Madathil D, Brahmi B, Wang I, Fareh R, Rahman MH. Developing a tablet-based brain-computer interface and robotic prototype for upper limb rehabilitation. PeerJ Comput Sci 2024; 10:e2174. [PMID: 39145233 PMCID: PMC11323104 DOI: 10.7717/peerj-cs.2174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Accepted: 06/12/2024] [Indexed: 08/16/2024]
Abstract
Background The current study explores the integration of a motor imagery (MI)-based BCI system with robotic rehabilitation designed for upper limb function recovery in stroke patients. Methods We developed a tablet deployable BCI control of the virtual iTbot for ease of use. Twelve right-handed healthy adults participated in this study, which involved a novel BCI training approach incorporating tactile vibration stimulation during MI tasks. The experiment utilized EEG signals captured via a gel-free cap, processed through various stages including signal verification, training, and testing. The training involved MI tasks with concurrent vibrotactile stimulation, utilizing common spatial pattern (CSP) training and linear discriminant analysis (LDA) for signal classification. The testing stage introduced a real-time feedback system and a virtual game environment where participants controlled a virtual iTbot robot. Results Results showed varying accuracies in motor intention detection across participants, with an average true positive rate of 63.33% in classifying MI signals. Discussion The study highlights the potential of MI-based BCI in robotic rehabilitation, particularly in terms of engagement and personalization. The findings underscore the feasibility of BCI technology in rehabilitation and its potential use for stroke survivors with upper limb dysfunctions.
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Affiliation(s)
- Kishor Lakshminarayanan
- Department of Sensors and Biomedical Tech, School of Electronics Engineering, Vellore Institute of Technology University, Vellore, Tamil Nadu, India
| | - Vadivelan Ramu
- Department of Sensors and Biomedical Tech, School of Electronics Engineering, Vellore Institute of Technology University, Vellore, Tamil Nadu, India
| | - Rakshit Shah
- Department of Orthopaedic Surgery, University of Arizona, Tucson, AZ, United States of America
| | - Md Samiul Haque Sunny
- Department of Mechanical Engineering, University of Wisconsin-Milwaukee, Milwaukee, WI, United States of America
| | - Deepa Madathil
- Jindal Institute of Behavioural Sciences, O.P. Jindal Global University, Haryana, India
| | - Brahim Brahmi
- Electrical Engineering, Collège Ahuntsic, Montreal, QC, Canada
| | - Inga Wang
- Department of Occupational Science & Technology, University of Wisconsin-Milwaukee, Milwaukee, WI, United States of America
| | - Raouf Fareh
- Department of Electrical and Computer Engineering, University of Sharjah, Sharjah, United Arab Emirates
| | - Mohammad Habibur Rahman
- Department of Mechanical Engineering, University of Wisconsin-Milwaukee, Milwaukee, WI, United States of America
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Bagged tree ensemble modelling with feature selection for isometric EMG-based force estimation. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.104012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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Kabir R, Sunny MSH, Ahmed HU, Rahman MH. Hand Rehabilitation Devices: A Comprehensive Systematic Review. MICROMACHINES 2022; 13:1033. [PMID: 35888850 PMCID: PMC9325203 DOI: 10.3390/mi13071033] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Revised: 06/23/2022] [Accepted: 06/25/2022] [Indexed: 12/20/2022]
Abstract
A cerebrovascular accident, or a stroke, can cause significant neurological damage, inflicting the patient with loss of motor function in their hands. Standard rehabilitation therapy for the hand increases demands on clinics, creating an avenue for powered hand rehabilitation devices. Hand rehabilitation devices (HRDs) are devices designed to provide the hand with passive, active, and active-assisted rehabilitation therapy; however, HRDs do not have any standards in terms of development or design. Although the categorization of an injury's severity can guide a patient into seeking proper assistance, rehabilitation devices do not have a set standard to provide a solution from the beginning to the end stages of recovery. In this paper, HRDs are defined and compared by their mechanical designs, actuation mechanisms, control systems, and therapeutic strategies. Furthermore, devices with conducted clinical trials are used to determine the future development of HRDs. After evaluating the abilities of 35 devices, it is inferred that standard characteristics for HRDs should include an exoskeleton design, the incorporation of challenge-based and coaching therapeutic strategies, and the implementation of surface electromyogram signals (sEMG) based control.
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Affiliation(s)
- Ryan Kabir
- Department of Mechanical Engineering, BioRobotics Lab, University of Wisconsin-Milwaukee, Milwaukee, WI 53211, USA; (H.U.A.); (M.H.R.)
| | - Md Samiul Haque Sunny
- Department of Computer Science, BioRobotics Lab, University of Wisconsin-Milwaukee, Milwaukee, WI 53211, USA;
| | - Helal Uddin Ahmed
- Department of Mechanical Engineering, BioRobotics Lab, University of Wisconsin-Milwaukee, Milwaukee, WI 53211, USA; (H.U.A.); (M.H.R.)
| | - Mohammad Habibur Rahman
- Department of Mechanical Engineering, BioRobotics Lab, University of Wisconsin-Milwaukee, Milwaukee, WI 53211, USA; (H.U.A.); (M.H.R.)
- Department of Computer Science, BioRobotics Lab, University of Wisconsin-Milwaukee, Milwaukee, WI 53211, USA;
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Hajian G, Morin E. Deep Multi-scale Fusion of Convolutional Neural Networks for EMG-based Movement Estimation. IEEE Trans Neural Syst Rehabil Eng 2022; 30:486-495. [PMID: 35192465 DOI: 10.1109/tnsre.2022.3153252] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
EMG-based motion estimation is required for applications such as myoelectric control, where the simultaneous estimation of kinematic information, namely joint angle and velocity, is challenging and critical. We propose a novel method for accurately modelling the generated joint angle and velocity simultaneously under isotonic, isokinetic (quasi-dynamic), and fully dynamic conditions. Our solution uses two streams of CNN, called TS-CNN to learn informative features from raw EMG data using different scales and estimate the generated motion during elbow flexion and extension. The experimental results show the robustness of our approach in comparison to conventional CNN as well as some methods used in the literature. The best obtained R2 values, are 0.81±0.06, 0.71±0.06, and 0.80±0.13 for joint angle estimation and 0.78±0.05, 0.79±0.07, and 0.71±0.13 for the velocity estimation, during isotonic, isokinetic, and dynamic contractions, respectively. Additionally, our results indicate that the experimental condition can have an impact on the model's performance for motion prediction. EMG-based velocity estimation obtains higher performance than joint angle estimation under isokinetic conditions. Under dynamic conditions, joint angle estimation is more accurate than velocity estimation, and there is no difference between joint angle and velocity estimation in the isotonic case.
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Wheelchair-Mounted Upper Limb Robotic Exoskeleton with Adaptive Controller for Activities of Daily Living. SENSORS 2021; 21:s21175738. [PMID: 34502632 PMCID: PMC8433689 DOI: 10.3390/s21175738] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/31/2021] [Revised: 08/21/2021] [Accepted: 08/23/2021] [Indexed: 11/17/2022]
Abstract
Neuro-muscular disorders and diseases such as cerebral palsy and Duchenne Muscular Dystrophy can severely limit a person's ability to perform activities of daily living (ADL). Exoskeletons can provide an active or passive support solution to assist these groups of people to perform ADL. This study presents an artificial neural network-trained adaptive controller mechanism that uses surface electromyography (sEMG) signals from the human forearm to detect hand gestures and navigate an in-house-built wheelchair-mounted upper limb robotic exoskeleton based on the user's intent while ensuring safety. To achieve the desired position of the exoskeleton based on human intent, 10 hand gestures were recorded from 8 participants without upper limb movement disabilities. Participants were tasked to perform water bottle pick and place activities while using the exoskeleton, and sEMG signals were collected from the forearm and processed through root mean square, median filter, and mean feature extractors prior to training a scaled conjugate gradient backpropagation artificial neural network. The trained network achieved an average of more than 93% accuracy, while all 8 participants who did not have any prior experience of using an exoskeleton were successfully able to perform the task in less than 20 s using the proposed artificial neural network-trained adaptive controller mechanism. These results are significant and promising thus could be tested on people with muscular dystrophy and neuro-degenerative diseases.
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Mohd Khairuddin I, Sidek SN, P.P. Abdul Majeed A, Mohd Razman MA, Ahmad Puzi A, Md Yusof H. The classification of movement intention through machine learning models: the identification of significant time-domain EMG features. PeerJ Comput Sci 2021; 7:e379. [PMID: 33817026 PMCID: PMC7959624 DOI: 10.7717/peerj-cs.379] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Accepted: 01/07/2021] [Indexed: 05/29/2023]
Abstract
Electromyography (EMG) signal is one of the extensively utilised biological signals for predicting human motor intention, which is an essential element in human-robot collaboration platforms. Studies on motion intention prediction from EMG signals have often been concentrated on either classification and regression models of muscle activity. In this study, we leverage the information from the EMG signals, to detect the subject's intentions in generating motion commands for a robot-assisted upper limb rehabilitation platform. The EMG signals are recorded from ten healthy subjects' biceps muscle, and the movements of the upper limb evaluated are voluntary elbow flexion and extension along the sagittal plane. The signals are filtered through a fifth-order Butterworth filter. A number of features were extracted from the filtered signals namely waveform length (WL), mean absolute value (MAV), root mean square (RMS), standard deviation (SD), minimum (MIN) and maximum (MAX). Several different classifiers viz. Linear Discriminant Analysis (LDA), Logistic Regression (LR), Decision Tree (DT), Support Vector Machine (SVM) and k-Nearest Neighbour (k-NN) were investigated on its efficacy to accurately classify the pre-intention and intention classes based on the significant features identified (MIN and MAX) via Extremely Randomised Tree feature selection technique. It was observed from the present investigation that the DT classifier yielded an excellent classification with a classification accuracy of 100%, 99% and 99% on training, testing and validation dataset, respectively based on the identified features. The findings of the present investigation are non-trivial towards facilitating the rehabilitation phase of patients based on their actual capability and hence, would eventually yield a more active participation from them.
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Affiliation(s)
- Ismail Mohd Khairuddin
- Faculty of Manufacturing & Mechatronics Engineering Technology, Innovative Manufacturing, Mechatronics and Sports Laboratory, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia
- Department of Mechatronics, Kulliyyah of Engineering, Biomechatronics Research Laboratory, International Islamic University, Gombak, Selangor, Malaysia
| | - Shahrul Naim Sidek
- Department of Mechatronics, Kulliyyah of Engineering, Biomechatronics Research Laboratory, International Islamic University, Gombak, Selangor, Malaysia
| | - Anwar P.P. Abdul Majeed
- Faculty of Manufacturing & Mechatronics Engineering Technology, Innovative Manufacturing, Mechatronics and Sports Laboratory, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia
| | - Mohd Azraai Mohd Razman
- Faculty of Manufacturing & Mechatronics Engineering Technology, Innovative Manufacturing, Mechatronics and Sports Laboratory, Universiti Malaysia Pahang, Pekan, Pahang, Malaysia
| | - Asmarani Ahmad Puzi
- Department of Mechatronics, Kulliyyah of Engineering, Biomechatronics Research Laboratory, International Islamic University, Gombak, Selangor, Malaysia
| | - Hazlina Md Yusof
- Department of Mechatronics, Kulliyyah of Engineering, Biomechatronics Research Laboratory, International Islamic University, Gombak, Selangor, Malaysia
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Liu C, Liang H, Ueda N, Li P, Fujimoto Y, Zhu C. Functional Evaluation of a Force Sensor-Controlled Upper-Limb Power-Assisted Exoskeleton with High Backdrivability. SENSORS (BASEL, SWITZERLAND) 2020; 20:E6379. [PMID: 33182271 PMCID: PMC7664921 DOI: 10.3390/s20216379] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Revised: 10/30/2020] [Accepted: 11/05/2020] [Indexed: 12/20/2022]
Abstract
A power-assisted exoskeleton should be capable of reducing the burden on the wearer's body or rendering his or her work improved and efficient. More specifically, the exoskeleton should be easy to wear, be simple to use, and provide power assistance without hindering the wearer's movement. Therefore, it is necessary to evaluate the backdrivability, range of motion, and power-assist capability of such an exoskeleton. This evaluation identifies the pros and cons of the exoskeleton, and it serves as the basis for its subsequent development. In this study, a lightweight upper-limb power-assisted exoskeleton with high backdrivability was developed. Moreover, a motion capture system was adopted to measure and analyze the workspace of the wearer's upper limb after the exoskeleton was worn. The results were used to evaluate the exoskeleton's ability to support the wearer's movement. Furthermore, a small and compact three-axis force sensor was used for power assistance, and the effect of the power assistance was evaluated by means of measuring the wearer's surface electromyography, force, and joint angle signals. Overall, the study showed that the exoskeleton could achieve power assistance and did not affect the wearer's movements.
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Affiliation(s)
- Chang Liu
- Department of Environment and Life Engineering, Maebashi Institute of Technology, 460-1 Kamisadori, Maebashi, Gunma 371-0816, Japan; (C.L.); (H.L.); (N.U.); (P.L.)
| | - Hongbo Liang
- Department of Environment and Life Engineering, Maebashi Institute of Technology, 460-1 Kamisadori, Maebashi, Gunma 371-0816, Japan; (C.L.); (H.L.); (N.U.); (P.L.)
| | - Naoya Ueda
- Department of Environment and Life Engineering, Maebashi Institute of Technology, 460-1 Kamisadori, Maebashi, Gunma 371-0816, Japan; (C.L.); (H.L.); (N.U.); (P.L.)
| | - Peirang Li
- Department of Environment and Life Engineering, Maebashi Institute of Technology, 460-1 Kamisadori, Maebashi, Gunma 371-0816, Japan; (C.L.); (H.L.); (N.U.); (P.L.)
| | - Yasutaka Fujimoto
- Department of Electrical and Computer Engineering, Faculty of Engineering, Yokohama National University, 79-5 Tokiwadai, Hodogaya-ku, Yokohama 240-8501, Japan;
| | - Chi Zhu
- Department of Systems Life Engineering, Maebashi Institute of Technology, 460-1 Kamisadori, Maebashi, Gunma 371-0816, Japan
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Huang Y, Song R, Argha A, Savkin AV, Celler BG, Su SW. Continuous Description of Human 3D Motion Intent Through Switching Mechanism. IEEE Trans Neural Syst Rehabil Eng 2019; 28:277-286. [PMID: 31647440 DOI: 10.1109/tnsre.2019.2949203] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Post-stroke motor recovery highly relies on voluntarily participating in active rehabilitation as early as possible for promoting the reorganization of the patient's brain. In this paper, a new method is proposed which manipulates cable-based rehabilitation robots to assist multi-joint body motions. This uses an electromyography (EMG) decoder for continuous estimation of voluntary motion intention to establish a cooperative human-machine interface for promoting the participation in rehabilitation exercises. In particular, for multi-joint complex tasks in three-dimensional space, a switching mechanism has been developed which can carve up tasks into separate simple motions. For each simple motion, a linear six-inputs and three-outputs time-invariant model is established respectively. The inputs are the processed muscle activations of six arm muscles, and the outputs are voluntary forces of participants when executing a multi-directional tracking task with visual feedback. The experiments for examining the decoder model and EMG-based controller include model training, testing and controller application phases with seven healthy participants. Experimental results demonstrate that the decoder model with the switching mechanism could effectively recognize arm movement intention and provide appropriate assistance to the participants. This study finds that the switching mechanism can improve both the model estimation accuracy and the completeness for executing complex tasks.
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Shen Y, Ma J, Dobkin B, Rosen J. Asymmetric Dual Arm Approach For Post Stroke Recovery Of Motor Functions Utilizing The EXO-UL8 Exoskeleton System: A Pilot Study. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2019; 2018:1701-1707. [PMID: 30440723 DOI: 10.1109/embc.2018.8512665] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
In a dual arm therapeutic regime aiming to rehabilitate motor functions post stroke, both the affected arm (paretic) and the unaffected (non-paretic) arm are involved. In this context, the leading idea is that motor functions of the affected arm during a reaching task may be improved if the unaffected arm has already reached the target. As part of this pilot study, one chronic post-stroke patient with weakness and spasticity on his right arm conducted reaching tasks to virtual targets arranged in a $5\times 3$ matrix located parallel to his frontal plane, in two different configurations: (1) affected arm only (without assistance from the exoskeleton); (2) unaffected arm first followed by the affected arm (2a) without, and (2b) with assistance. A force field attracting the wrist of the affected arm to the target was used in the assistive mode. The data post-processing and analysis included task completion time, reachable task space, joint range of motion, human-robot interaction force/torque and power exchange at multiple sensors along the arm - visualized in a series of interaction maps. The data validated the robotic system's basic functionality in facilitating post-stroke unilateral and asymmetric bilateral training. Future work would be expanded to clinical trials with more subjects to be recruited and additional features to be implemented.
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A review on EMG-based motor intention prediction of continuous human upper limb motion for human-robot collaboration. Biomed Signal Process Control 2019. [DOI: 10.1016/j.bspc.2019.02.011] [Citation(s) in RCA: 143] [Impact Index Per Article: 28.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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Kukker A, Sharma R. Neural reinforcement learning classifier for elbow, finger and hand movements. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2018. [DOI: 10.3233/jifs-169795] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Affiliation(s)
- Amit Kukker
- Instrumentation and Control Engineering Division, Netaji Subhas Institute of Technology, Delhi, India
| | - Rajneesh Sharma
- Instrumentation and Control Engineering Division, Netaji Subhas Institute of Technology, Delhi, India
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Wu H, Wang D, Huang Q, Gao L. Real-time continuous recognition of knee motion using multi-channel mechanomyography signals detected on clothes. J Electromyogr Kinesiol 2017; 38:94-102. [PMID: 29182965 DOI: 10.1016/j.jelekin.2017.10.010] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2017] [Revised: 10/20/2017] [Accepted: 10/20/2017] [Indexed: 11/29/2022] Open
Abstract
Mechanomyography (MMG) signal has been recently investigated for pattern recognition of human motion. In theory, it is no need of direct skin contact to be detected and unaffected by changes in skin impedance. So, it is hopeful for developing wearable sensing device with clothes. However, there have been no studies so far to detect MMG signal on clothes and verify the feasibility of pattern recognition. For this study, 4-channel MMG signals were detected on clothes from the thigh muscles of 8 able-bodied participants. The support vector machines (SVM) classifier with 4 common features was used to recognize 6 knee motions and the average accuracy of nearly 88% was achieved. The accuracy can be further improved up to 91% by introducing a new proposed feature of the difference of mean absolute value (DMAV), but not by root mean square (RMS) or mean absolute value (MAV). Furthermore, the first-order Markov chain model was combined with the SVM classifier and it can avoid the misclassifications in some cases. For application to wearable power-assisted devices, this study would promote the developments of more flexible, more comfortable, and minimally obtrusive wearable sensing devices with clothes and recognition techniques of human motion intention.
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Affiliation(s)
- Haifeng Wu
- Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei 230031, China; University of Science and Technology of China, Hefei 230026, China; High Magnetic Field Laboratory, Chinese Academy of Sciences, Hefei 230031, China.
| | - Daqing Wang
- Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei 230031, China; University of Science and Technology of China, Hefei 230026, China
| | - Qing Huang
- Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei 230031, China
| | - Lifu Gao
- Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei 230031, China.
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Kim YJ, Park CK, Kim KG. An EMG-based variable impedance control for elbow exercise: preliminary study. Adv Robot 2017. [DOI: 10.1080/01691864.2017.1353440] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Affiliation(s)
- Yeoun-Jae Kim
- Biomedical Engineering Branch, Division of Precision Medicine and Cancer Informatics, National Cancer Center, Goyang-si, Korea
| | - Chang-Kyu Park
- Department of Ship and Ocean, Vision University of Jeonju, Jeonju, Korea
| | - Kwang Gi Kim
- Biomedical Engineering Branch, Division of Precision Medicine and Cancer Informatics, National Cancer Center, Goyang-si, Korea
- Department of Biomedical Engineering, School of Medicine, Gil Medical Center, Gachon University, Incheon, Korea
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Ambar R, Yusof Y, Jamil MMA, Sharif JM, Ngadi MA. Design of accelerometer based wrist rehabilitation device. 2017 6TH ICT INTERNATIONAL STUDENT PROJECT CONFERENCE (ICT-ISPC) 2017. [DOI: 10.1109/ict-ispc.2017.8075342] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
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Proietti T, Crocher V, Roby-Brami A, Jarrasse N. Upper-Limb Robotic Exoskeletons for Neurorehabilitation: A Review on Control Strategies. IEEE Rev Biomed Eng 2016; 9:4-14. [PMID: 27071194 DOI: 10.1109/rbme.2016.2552201] [Citation(s) in RCA: 108] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Since the late 1990s, there has been a burst of research on robotic devices for poststroke rehabilitation. Robot-mediated therapy produced improvements on recovery of motor capacity; however, so far, the use of robots has not shown qualitative benefit over classical therapist-led training sessions, performed on the same quantity of movements. Multidegree-of-freedom robots, like the modern upper-limb exoskeletons, enable a distributed interaction on the whole assisted limb and can exploit a large amount of sensory feedback data, potentially providing new capabilities within standard rehabilitation sessions. Surprisingly, most publications in the field of exoskeletons focused only on mechatronic design of the devices, while little details were given to the control aspects. On the contrary, we believe a paramount aspect for robots potentiality lies on the control side. Therefore, the aim of this review is to provide a taxonomy of currently available control strategies for exoskeletons for neurorehabilitation, in order to formulate appropriate questions toward the development of innovative and improved control strategies.
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