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Falkowski P, Jeznach K. Simulation of a control method for active kinesiotherapy with an upper extremity rehabilitation exoskeleton without force sensor. J Neuroeng Rehabil 2024; 21:22. [PMID: 38342919 PMCID: PMC10860295 DOI: 10.1186/s12984-024-01316-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 01/24/2024] [Indexed: 02/13/2024] Open
Abstract
Exoskeleton-aided active rehabilitation is a process that requires sensing and acting upon the motion intentions of the user. Typically, force sensors are used for this. However, they increase the weight and cost of these wearable devices. This paper presents the methodology for detecting users' intentions only with encoders integrated with the drives. It is unique compared to other algorithms, as enables active kinesiotherapy while adding no sensory systems. The method is based on comparing the measured motion with the one computed with the idealised model of the multibody system. The investigation assesses the method's performance and its robustness to model and measurement inaccuracies, as well as patients' unintended motions. Moreover, the PID parameters are selected to provide the optimal regulation based on the dynamics requirements. The research proves the presented concept of the control approach. For all the tests with the final settings, the system reacts to a change in the user's intention below one second and minimises the changes in proportion between the system's acceleration and the generated user's joint torque. The results are comparable to those obtained by EMG-based systems and significantly better than low-cost force sensors.
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Affiliation(s)
- Piotr Falkowski
- ŁUKASIEWICZ Research Network - Industrial Research Institute for Automation and Measurements PIAP, Al. Jerozolimskie 202, 02-486, Warsaw, Poland.
- Warsaw University of Technology, Pl. Politechniki 1, 00-661, Warsaw, Poland.
| | - Kajetan Jeznach
- Warsaw University of Technology, Pl. Politechniki 1, 00-661, Warsaw, Poland
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Shi L, Wang R, Zhao J, Zhang J, Kuang Z. Detection of Rehabilitation Training Effect of Upper Limb Movement Disorder Based on MPL-CNN. SENSORS (BASEL, SWITZERLAND) 2024; 24:1105. [PMID: 38400263 PMCID: PMC10892837 DOI: 10.3390/s24041105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Revised: 01/31/2024] [Accepted: 02/02/2024] [Indexed: 02/25/2024]
Abstract
Stroke represents a medical emergency and can lead to the development of movement disorders such as abnormal muscle tone, limited range of motion, or abnormalities in coordination and balance. In order to help stroke patients recover as soon as possible, rehabilitation training methods employ various movement modes such as ordinary movements and joint reactions to induce active reactions in the limbs and gradually restore normal functions. Rehabilitation effect evaluation can help physicians understand the rehabilitation needs of different patients, determine effective treatment methods and strategies, and improve treatment efficiency. In order to achieve real-time and accuracy of action detection, this article uses Mediapipe's action detection algorithm and proposes a model based on MPL-CNN. Mediapipe can be used to identify key point features of the patient's upper limbs and simultaneously identify key point features of the hand. In order to detect the effect of rehabilitation training for upper limb movement disorders, LSTM and CNN are combined to form a new LSTM-CNN model, which is used to identify the action features of upper limb rehabilitation training extracted by Medipipe. The MPL-CNN model can effectively identify the accuracy of rehabilitation movements during upper limb rehabilitation training for stroke patients. In order to ensure the scientific validity and unified standards of rehabilitation training movements, this article employs the postures in the Fugl-Meyer Upper Limb Rehabilitation Training Functional Assessment Form (FMA) and establishes an FMA upper limb rehabilitation data set for experimental verification. Experimental results show that in each stage of the Fugl-Meyer upper limb rehabilitation training evaluation effect detection, the MPL-CNN-based method's recognition accuracy of upper limb rehabilitation training actions reached 95%. At the same time, the average accuracy rate of various upper limb rehabilitation training actions reaches 97.54%. This shows that the model is highly robust across different action categories and proves that the MPL-CNN model is an effective and feasible solution. This method based on MPL-CNN can provide a high-precision detection method for the evaluation of rehabilitation effects of upper limb movement disorders after stroke, helping clinicians in evaluating the patient's rehabilitation progress and adjusting the rehabilitation plan based on the evaluation results. This will help improve the personalization and precision of rehabilitation treatment and promote patient recovery.
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Affiliation(s)
- Lijuan Shi
- College of Electronic Information Engineering, Changchun University, Changchun 130012, China; (L.S.); (R.W.); (J.Z.)
- Jilin Provincial Key Laboratory of Human Health Status Identification Function & Enhancement, Changchun 130022, China;
- Key Laboratory of Intelligent Rehabilitation and Barrier-Free for the Disabled, Changchun University, Ministry of Education, Changchun 130012, China
| | - Runmin Wang
- College of Electronic Information Engineering, Changchun University, Changchun 130012, China; (L.S.); (R.W.); (J.Z.)
| | - Jian Zhao
- Jilin Provincial Key Laboratory of Human Health Status Identification Function & Enhancement, Changchun 130022, China;
- Key Laboratory of Intelligent Rehabilitation and Barrier-Free for the Disabled, Changchun University, Ministry of Education, Changchun 130012, China
- College of Computer Science and Technology, Changchun University, Changchun 130022, China
| | - Jing Zhang
- College of Electronic Information Engineering, Changchun University, Changchun 130012, China; (L.S.); (R.W.); (J.Z.)
| | - Zhejun Kuang
- Jilin Provincial Key Laboratory of Human Health Status Identification Function & Enhancement, Changchun 130022, China;
- Key Laboratory of Intelligent Rehabilitation and Barrier-Free for the Disabled, Changchun University, Ministry of Education, Changchun 130012, China
- College of Computer Science and Technology, Changchun University, Changchun 130022, China
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Iranzo S, Belda-Lois JM, Martinez-de-Juan JL, Prats-Boluda G. Assessment of Muscle Coordination Changes Caused by the Use of an Occupational Passive Lumbar Exoskeleton in Laboratory Conditions. SENSORS (BASEL, SWITZERLAND) 2023; 23:9631. [PMID: 38139478 PMCID: PMC10747114 DOI: 10.3390/s23249631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 11/29/2023] [Accepted: 12/02/2023] [Indexed: 12/24/2023]
Abstract
The introduction of exoskeletons in industry has focused on improving worker safety. Exoskeletons have the objective of decreasing the risk of injury or fatigue when performing physically demanding tasks. Exoskeletons' effect on the muscles is one of the most common focuses of their assessment. The present study aimed to analyze the muscle interactions generated during load-handling tasks in laboratory conditions with and without a passive lumbar exoskeleton. The electromyographic data of the muscles involved in the task were recorded from twelve participants performing load-handling tasks. The correlation coefficient, coherence coefficient, mutual information, and multivariate sample entropy were calculated to determine if there were significant differences in muscle interactions between the two test conditions. The results showed that muscle coordination was affected by the use of the exoskeleton. In some cases, the exoskeleton prevented changes in muscle coordination throughout the execution of the task, suggesting a more stable strategy. Additionally, according to the directed Granger causality, a trend of increasing bottom-up activation was found throughout the task when the participant was not using the exoskeleton. Among the different variables analyzed for coordination, the most sensitive to changes was the multivariate sample entropy.
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Affiliation(s)
- Sofía Iranzo
- Instituto de Biomecánica de Valencia, Universitat Politècnica de València, 46022 Valencia, Spain; (S.I.); (J.-M.B.-L.)
| | - Juan-Manuel Belda-Lois
- Instituto de Biomecánica de Valencia, Universitat Politècnica de València, 46022 Valencia, Spain; (S.I.); (J.-M.B.-L.)
| | - Jose Luis Martinez-de-Juan
- Centro de Investigación e Innovación en Bioingeniería (Ci2B), Universitat Politècnica de València, 46022 Valencia, Spain;
| | - Gema Prats-Boluda
- Centro de Investigación e Innovación en Bioingeniería (Ci2B), Universitat Politècnica de València, 46022 Valencia, Spain;
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Chen ZJ, He C, Xu J, Zheng CJ, Wu J, Xia N, Hua Q, Xia WG, Xiong CH, Huang XL. Exoskeleton-Assisted Anthropomorphic Movement Training for the Upper Limb After Stroke: The EAMT Randomized Trial. Stroke 2023; 54:1464-1473. [PMID: 37154059 DOI: 10.1161/strokeaha.122.041480] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Accepted: 04/07/2023] [Indexed: 05/10/2023]
Abstract
BACKGROUND Robot-assisted arm training is generally delivered in the robot-like manner of planar or mechanical 3-dimensional movements. It remains unclear whether integrating upper extremity (UE) natural coordinated patterns into a robotic exoskeleton can improve outcomes. The study aimed to compare conventional therapist-mediated training to the practice of human-like gross movements derived from 5 typical UE functional activities managed with exoskeletal assistance as needed for patients after stroke. METHODS In this randomized, single-blind, noninferiority trial, patients with moderate-to-severe UE motor impairment due to subacute stroke were randomly assigned (1:1) to receive 20 sessions of 45-minute exoskeleton-assisted anthropomorphic movement training or conventional therapy. Treatment allocation was masked from independent assessors, but not from patients or investigators. The primary outcome was the change in the Fugl-Meyer Assessment for Upper Extremity from baseline to 4 weeks against a prespecified noninferiority margin of 4 points. Superiority would be tested if noninferiority was demonstrated. Post hoc subgroup analyses of baseline characteristics were performed for the primary outcome. RESULTS Between June 2020 and August 2021, totally 80 inpatients (67 [83.8%] males; age, 51.9±9.9 years; days since stroke onset, 54.6±38.0) were enrolled, randomly assigned to the intervention, and included in the intention-to-treat analysis. The mean Fugl-Meyer Assessment for Upper Extremity change in exoskeleton-assisted anthropomorphic movement training (14.73 points; [95% CI, 11.43-18.02]) was higher than that of conventional therapy (9.90 points; [95% CI, 8.15-11.65]) at 4 weeks (adjusted difference, 4.51 points [95% CI, 1.13-7.90]). Moreover, post hoc analysis favored the patient subgroup (Fugl-Meyer Assessment for Upper Extremity score, 23-38 points) with moderately severe motor impairment. CONCLUSIONS Exoskeleton-assisted anthropomorphic movement training appears to be effective for patients with subacute stroke through repetitive practice of human-like movements. Although the results indicate a positive sign for exoskeleton-assisted anthropomorphic movement training, further investigations into the long-term effects and paradigm optimization are warranted. REGISTRATION URL: https://www.chictr.org.cn; Unique identifier: ChiCTR2100044078.
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Affiliation(s)
- Ze-Jian Chen
- Department of Rehabilitation Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China (Z.-J.C., J.X., N.X., X.-L.H.)
- World Health Organization Cooperative Training and Research Center in Rehabilitation, Wuhan, China (Z.-J.C., J.X., N.X., X.-L.H.)
| | - Chang He
- Institute of Medical Equipment Science and Engineering, Huazhong University of Science and Technology, Wuhan, China (C.H., C.-H.X.)
- State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China (C.H., C.-H.X.)
| | - Jiang Xu
- Department of Rehabilitation Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China (Z.-J.C., J.X., N.X., X.-L.H.)
- World Health Organization Cooperative Training and Research Center in Rehabilitation, Wuhan, China (Z.-J.C., J.X., N.X., X.-L.H.)
| | - Chan-Juan Zheng
- Department of Rehabilitation Medicine, Hubei Provincial Hospital of Integrated Traditional Chinese and Western Medicine, Wuhan, China (C.-J.Z., J.W., Q.H.)
| | - Jing Wu
- Department of Rehabilitation Medicine, Hubei Provincial Hospital of Integrated Traditional Chinese and Western Medicine, Wuhan, China (C.-J.Z., J.W., Q.H.)
| | - Nan Xia
- Department of Rehabilitation Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China (Z.-J.C., J.X., N.X., X.-L.H.)
- World Health Organization Cooperative Training and Research Center in Rehabilitation, Wuhan, China (Z.-J.C., J.X., N.X., X.-L.H.)
| | - Qiang Hua
- Department of Rehabilitation Medicine, Hubei Provincial Hospital of Integrated Traditional Chinese and Western Medicine, Wuhan, China (C.-J.Z., J.W., Q.H.)
| | - Wen-Guang Xia
- Hubei Rehabilitation Hospital, Wuhan, China (W.-G.X.)
| | - Cai-Hua Xiong
- Institute of Medical Equipment Science and Engineering, Huazhong University of Science and Technology, Wuhan, China (C.H., C.-H.X.)
- State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China (C.H., C.-H.X.)
| | - Xiao-Lin Huang
- Department of Rehabilitation Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China (Z.-J.C., J.X., N.X., X.-L.H.)
- World Health Organization Cooperative Training and Research Center in Rehabilitation, Wuhan, China (Z.-J.C., J.X., N.X., X.-L.H.)
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Zhao Y, Wu H, Zhang M, Mao J, Todoh M. Design methodology of portable upper limb exoskeletons for people with strokes. Front Neurosci 2023; 17:1128332. [PMID: 37008203 PMCID: PMC10060802 DOI: 10.3389/fnins.2023.1128332] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 02/27/2023] [Indexed: 03/18/2023] Open
Affiliation(s)
- Yongkun Zhao
- Division of Human Mechanical Systems and Design, Graduate School of Engineering, Hokkaido University, Sapporo, Japan
- Department of Bioengineering, Faculty of Engineering, Imperial College London, London, United Kingdom
| | - Haijun Wu
- Division of Mechanical and Aerospace Engineering, Faculty of Engineering, Hokkaido University, Sapporo, Japan
| | - Mingquan Zhang
- State Key Laboratory of Bioelectronics, Jiangsu Provincial Key Laboratory of Remote Measurement and Control, School of Instrument Science and Engineering, Southeast University, Nanjing, China
| | - Juzheng Mao
- State Key Laboratory of Bioelectronics, Jiangsu Provincial Key Laboratory of Remote Measurement and Control, School of Instrument Science and Engineering, Southeast University, Nanjing, China
- *Correspondence: Juzheng Mao
| | - Masahiro Todoh
- Division of Mechanical and Aerospace Engineering, Faculty of Engineering, Hokkaido University, Sapporo, Japan
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Meng L, Jiang X, Qin H, Fan J, Zeng Z, Chen C, Zhang A, Dai C, Wu X, Akay YM, Akay M, Chen W. Automatic Upper-Limb Brunnstrom Recovery Stage Evaluation via Daily Activity Monitoring. IEEE Trans Neural Syst Rehabil Eng 2022; 30:2589-2599. [PMID: 36067100 DOI: 10.1109/tnsre.2022.3204781] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Motor function assessment is crucial for post-stroke rehabilitation. Conventional evaluation methods are subjective, heavily depending on the experience of therapists. In light of the strong correlation between the stroke severity level and the performance of activities of daily living (ADLs), we explored the possibility of automatically evaluating the upper-limb Brunnstrom Recovery Stage (BRS) via three typical ADLs (tooth brushing, face washing and drinking). Multimodal data (acceleration, angular velocity, surface electromyography) were synchronously collected from 5 upper-limb-worn sensor modules. The performance of BRS evaluation system is known to be variable with different system parameters (e.g., number of sensor modules, feature types and classifiers). We systematically searched for the optimal parameters from different data segmentation strategies (five window lengths and four overlaps), 42 types of features, 12 feature optimization techniques and 9 classifiers with the leave-one-subject-out cross-validation. To achieve reliable and low-cost monitoring, we further explored whether it was possible to obtain a satisfactory result using a relatively small number of sensor modules. As a result, the proposed approach can correctly recognize the stages of all 27 participants using only three sensor modules with the optimized data segmentation parameters (window length: 7s, overlap: 50%), extracted features (simple square integral, slope sign change, modified mean absolute value 1 and modified mean absolute value 2), the feature optimization method (principal component analysis) and the logistic regression classifier. According to the literature, this is the first study to comprehensively optimize sensor configuration and parameters in each stage of the BRS classification framework. The proposed approach can serve as a factor-screening tool towards the automatic BRS classification and is promising to be further used at home.
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Jonna P, Rao M. Design of a 6-DoF Cost-effective Differential-drive based Robotic system for Upper-Limb Stroke Rehabilitation. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:1423-1427. [PMID: 36085923 DOI: 10.1109/embc48229.2022.9871426] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
This paper discusses the design, construction, and characteristics of a six degree of freedom (6-DoF) robotic upper limb stroke rehabilitation device. The device is primarily designed to be used by stroke survivors suffering from hemi-paresis, post stroke therapy. The proposed device is aimed to substitute upper limb exoskeletons or end-effector robotic arm based rehabilitation systems, which are generally bulky, expensive due to customized design, and are used only in clinical settings. The device is capable of aiding patients perform rehabilitation exercises involving abduction, adduction, flexion, and extension movements for the wrist, shoulder joints and flexion, extension, pronation, supination for the elbow joint. The device has a mobile base and uses differential drive principles for movement. The device provides an end-effector plate having force-sensitive resistors (FSRs) on which the patient can rest their hand. The mobile base of the rehabilitation system enables it to aid for a greater range of movements when compared to other end-effector-based upper limb rehabilitation devices. Additionally, a camera is mounted on top of the 6-DoF robotic system to enable finger tracking from a remote system using the MediaPipe framework, and measure the hand instability metric over time to assess patient's performance. Clinical relevance - The 6-DoF rehabilitation system is capable of aiding different range of motions for upper limb. The low-cost generic system is applicable for different physical personalities, enabling quick adoption for large patient populations who are in need of rehabilitation systems. The 6-DoF system is aimed towards clinical, and domestic usage by patients.
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