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An IoT-Enabled Stroke Rehabilitation System Based on Smart Wearable Armband and Machine Learning

التفاصيل البيبلوغرافية
العنوان: An IoT-Enabled Stroke Rehabilitation System Based on Smart Wearable Armband and Machine Learning
المؤلفون: Deng B, Pang GY, Jiang MZ, Pang ZB, Zhang H, Yang G, Liljeberg P, Xie HB, Deng J, Yang HY, Li JY, Xu J
المساهمون: PÄÄT Terveysteknologia, PÄÄT Health Technology, 2606808
بيانات النشر: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
United States
Yhdysvallat (USA)
US
سنة النشر: 2022
المجموعة: University of Turku: UTUPub / Turun yliopisto
الوصف: Surface electromyography signal plays an important role in hand function recovery training. In this paper, an IoT-enabled stroke rehabilitation system was introduced which was based on a smart wearable armband (SWA), machine learning (ML) algorithms, and a 3-D printed dexterous robot hand. User comfort is one of the key issues which should be addressed for wearable devices. The SWA was developed by integrating a low-power and tiny-sized IoT sensing device with textile electrodes, which can measure, pre-process, and wirelessly transmit bio-potential signals. By evenly distributing surface electrodes over user's forearm, drawbacks of classification accuracy poor performance can be mitigated. A new method was put forward to find the optimal feature set. ML algorithms were leveraged to analyze and discriminate features of different hand movements, and their performances were appraised by classification complexity estimating algorithms and principal components analysis. According to the verification results, all nine gestures can be successfully identified with an average accuracy up to 96.20%. In addition, a 3-D printed five-finger robot hand was implemented for hand rehabilitation training purpose. Correspondingly, user's hand movement intentions were extracted and converted into a series of commands which were used to drive motors assembled inside the dexterous robot hand. As a result, the dexterous robot hand can mimic the user's gesture in a real-time manner, which shows the proposed system can be used as a training tool to facilitate rehabilitation process for the patients after stroke.
نوع الوثيقة: other/unknown material
اللغة: English
تدمد: 2168-2372
العلاقة: ARTN 2100510; IEEE Journal of Translational Engineering in Health and Medicine; https://www.utupub.fi/handle/10024/155012Test; https://ieeexplore.ieee.org/document/8356006Test/; URN:NBN:fi-fe2021042713603
الإتاحة: https://www.utupub.fi/handle/10024/155012Test
https://ieeexplore.ieee.org/document/8356006Test/
رقم الانضمام: edsbas.81682E00
قاعدة البيانات: BASE
الوصف
تدمد:21682372