Literature DB >> 28113559

Development of an EMG-ACC-Based Upper Limb Rehabilitation Training System.

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Abstract

This paper focuses on the development of an upper limb rehabilitation training system designed for use by children with cerebral palsy (CP). It attempts to meet the requirements of in-home training by taking advantage of the combination of portable accelerometers (ACC) and surface electromyography (SEMG) sensors worn on the upper limb to capture functional movements. In the proposed system, the EMG-ACC acquisition device works essentially as wireless game controller, and three rehabilitation games were designed for improving upper limb motor function under a clinician's guidance. The games were developed on the Android platform based on a physical engine called Box2D. The results of a system performance test demonstrated that the developed games can respond to the upper limb actions within 210 ms. Positive questionnaire feedbacks from twenty CP subjects who participated in the game test verified both the feasibility and usability of the system. Results of a long-term game training conducted with three CP subjects demonstrated that CP patients could improve in their game performance through repetitive training, and persistent training was needed to improve and enhance the rehabilitation effect. According to our experimental results, the novel multi-feedback SEMG-ACC-based user interface improved the users' initiative and performance in rehabilitation training.

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Year:  2016        PMID: 28113559     DOI: 10.1109/TNSRE.2016.2560906

Source DB:  PubMed          Journal:  IEEE Trans Neural Syst Rehabil Eng        ISSN: 1534-4320            Impact factor:   3.802


  9 in total

1.  Proposal of a Wearable Multimodal Sensing-Based Serious Games Approach for Hand Movement Training After Stroke.

Authors:  Xinyu Song; Shirdi Shankara van de Ven; Shugeng Chen; Peiqi Kang; Qinghua Gao; Jie Jia; Peter B Shull
Journal:  Front Physiol       Date:  2022-06-03       Impact factor: 4.755

2.  Development of electromyographic indicators for the diagnosis of temporomandibular disorders: a protocol for an assessor-blinded cross-sectional study.

Authors:  Kwang-Ho Choi; O Sang Kwon; Ui Min Jerng; So Min Lee; Lak-Hyung Kim; Jeeyoun Jung
Journal:  Integr Med Res       Date:  2017-01-18

3.  Motor Function Evaluation of Hemiplegic Upper-Extremities Using Data Fusion from Wearable Inertial and Surface EMG Sensors.

Authors:  Yanran Li; Xu Zhang; Yanan Gong; Ying Cheng; Xiaoping Gao; Xiang Chen
Journal:  Sensors (Basel)       Date:  2017-03-13       Impact factor: 3.576

Review 4.  Games Used With Serious Purposes: A Systematic Review of Interventions in Patients With Cerebral Palsy.

Authors:  Sílvia Lopes; Paula Magalhães; Armanda Pereira; Juliana Martins; Carla Magalhães; Elisa Chaleta; Pedro Rosário
Journal:  Front Psychol       Date:  2018-09-19

Review 5.  Sensors and Systems for Physical Rehabilitation and Health Monitoring-A Review.

Authors:  Lucas Medeiros Souza do Nascimento; Lucas Vacilotto Bonfati; Melissa La Banca Freitas; José Jair Alves Mendes Junior; Hugo Valadares Siqueira; Sergio Luiz Stevan
Journal:  Sensors (Basel)       Date:  2020-07-22       Impact factor: 3.576

6.  Introduction of a sEMG Sensor System for Autonomous Use by Inexperienced Users.

Authors:  Elisa Romero Avila; Elmar Junker; Catherine Disselhorst-Klug
Journal:  Sensors (Basel)       Date:  2020-12-21       Impact factor: 3.576

7.  Electromyographic changes in masseter and sternocleidomastoid muscles can be applied to diagnose of temporomandibular disorders: An observational study.

Authors:  Kwang-Ho Choi; O Sang Kwon; Lakhyung Kim; So Min Lee; Ui Min Jerng; Jeeyoun Jung
Journal:  Integr Med Res       Date:  2021-05-16

Review 8.  Feedback Design in Targeted Exercise Digital Biofeedback Systems for Home Rehabilitation: A Scoping Review.

Authors:  Louise Brennan; Enrique Dorronzoro Zubiete; Brian Caulfield
Journal:  Sensors (Basel)       Date:  2019-12-28       Impact factor: 3.576

9.  An sEMG-Controlled 3D Game for Rehabilitation Therapies: Real-Time Time Hand Gesture Recognition Using Deep Learning Techniques.

Authors:  Nadia Nasri; Sergio Orts-Escolano; Miguel Cazorla
Journal:  Sensors (Basel)       Date:  2020-11-12       Impact factor: 3.576

  9 in total

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