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

Quantitative evaluation of motor function is of great demand for monitoring clinical outcome of applied interventions and further guiding the establishment of therapeutic protocol. This study proposes a novel framework for evaluating upper limb motor function based on data fusion from inertial measu...

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Main Authors: Yanran Li, Xu Zhang, Yanan Gong, Ying Cheng, Xiaoping Gao, Xiang Chen
Format: Article
Language:English
Published: MDPI AG 2017-03-01
Series:Sensors
Subjects:
Online Access:http://www.mdpi.com/1424-8220/17/3/582
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spelling doaj-7561f765a90844b3bba071b5e8c490d12020-11-24T21:06:14ZengMDPI AGSensors1424-82202017-03-0117358210.3390/s17030582s17030582Motor Function Evaluation of Hemiplegic Upper-Extremities Using Data Fusion from Wearable Inertial and Surface EMG SensorsYanran Li0Xu Zhang1Yanan Gong2Ying Cheng3Xiaoping Gao4Xiang Chen5Department of Electronic Science and Technology, University of Science and Technology of China, Hefei 230027, ChinaDepartment of Electronic Science and Technology, University of Science and Technology of China, Hefei 230027, ChinaDepartment of Electronic Science and Technology, University of Science and Technology of China, Hefei 230027, ChinaDepartment of Rehabilitation Medicine at the First Affiliated Hospital of Anhui Medical University, Hefei 230022, ChinaDepartment of Rehabilitation Medicine at the First Affiliated Hospital of Anhui Medical University, Hefei 230022, ChinaDepartment of Electronic Science and Technology, University of Science and Technology of China, Hefei 230027, ChinaQuantitative evaluation of motor function is of great demand for monitoring clinical outcome of applied interventions and further guiding the establishment of therapeutic protocol. This study proposes a novel framework for evaluating upper limb motor function based on data fusion from inertial measurement units (IMUs) and surface electromyography (EMG) sensors. With wearable sensors worn on the tested upper limbs, subjects were asked to perform eleven straightforward, specifically designed canonical upper-limb functional tasks. A series of machine learning algorithms were applied to the recorded motion data to produce evaluation indicators, which is able to reflect the level of upper-limb motor function abnormality. Sixteen healthy subjects and eighteen stroke subjects with substantial hemiparesis were recruited in the experiment. The combined IMU and EMG data yielded superior performance over the IMU data alone and the EMG data alone, in terms of decreased normal data variation rate (NDVR) and improved determination coefficient (DC) from a regression analysis between the derived indicator and routine clinical assessment score. Three common unsupervised learning algorithms achieved comparable performance with NDVR around 10% and strong DC around 0.85. By contrast, the use of a supervised algorithm was able to dramatically decrease the NDVR to 6.55%. With the proposed framework, all the produced indicators demonstrated high agreement with the routine clinical assessment scale, indicating their capability of assessing upper-limb motor functions. This study offers a feasible solution to motor function assessment in an objective and quantitative manner, especially suitable for home and community use.http://www.mdpi.com/1424-8220/17/3/582electromyographyinertial measurement unitmotor function evaluationstroke
collection DOAJ
language English
format Article
sources DOAJ
author Yanran Li
Xu Zhang
Yanan Gong
Ying Cheng
Xiaoping Gao
Xiang Chen
spellingShingle Yanran Li
Xu Zhang
Yanan Gong
Ying Cheng
Xiaoping Gao
Xiang Chen
Motor Function Evaluation of Hemiplegic Upper-Extremities Using Data Fusion from Wearable Inertial and Surface EMG Sensors
Sensors
electromyography
inertial measurement unit
motor function evaluation
stroke
author_facet Yanran Li
Xu Zhang
Yanan Gong
Ying Cheng
Xiaoping Gao
Xiang Chen
author_sort Yanran Li
title Motor Function Evaluation of Hemiplegic Upper-Extremities Using Data Fusion from Wearable Inertial and Surface EMG Sensors
title_short Motor Function Evaluation of Hemiplegic Upper-Extremities Using Data Fusion from Wearable Inertial and Surface EMG Sensors
title_full Motor Function Evaluation of Hemiplegic Upper-Extremities Using Data Fusion from Wearable Inertial and Surface EMG Sensors
title_fullStr Motor Function Evaluation of Hemiplegic Upper-Extremities Using Data Fusion from Wearable Inertial and Surface EMG Sensors
title_full_unstemmed Motor Function Evaluation of Hemiplegic Upper-Extremities Using Data Fusion from Wearable Inertial and Surface EMG Sensors
title_sort motor function evaluation of hemiplegic upper-extremities using data fusion from wearable inertial and surface emg sensors
publisher MDPI AG
series Sensors
issn 1424-8220
publishDate 2017-03-01
description Quantitative evaluation of motor function is of great demand for monitoring clinical outcome of applied interventions and further guiding the establishment of therapeutic protocol. This study proposes a novel framework for evaluating upper limb motor function based on data fusion from inertial measurement units (IMUs) and surface electromyography (EMG) sensors. With wearable sensors worn on the tested upper limbs, subjects were asked to perform eleven straightforward, specifically designed canonical upper-limb functional tasks. A series of machine learning algorithms were applied to the recorded motion data to produce evaluation indicators, which is able to reflect the level of upper-limb motor function abnormality. Sixteen healthy subjects and eighteen stroke subjects with substantial hemiparesis were recruited in the experiment. The combined IMU and EMG data yielded superior performance over the IMU data alone and the EMG data alone, in terms of decreased normal data variation rate (NDVR) and improved determination coefficient (DC) from a regression analysis between the derived indicator and routine clinical assessment score. Three common unsupervised learning algorithms achieved comparable performance with NDVR around 10% and strong DC around 0.85. By contrast, the use of a supervised algorithm was able to dramatically decrease the NDVR to 6.55%. With the proposed framework, all the produced indicators demonstrated high agreement with the routine clinical assessment scale, indicating their capability of assessing upper-limb motor functions. This study offers a feasible solution to motor function assessment in an objective and quantitative manner, especially suitable for home and community use.
topic electromyography
inertial measurement unit
motor function evaluation
stroke
url http://www.mdpi.com/1424-8220/17/3/582
work_keys_str_mv AT yanranli motorfunctionevaluationofhemiplegicupperextremitiesusingdatafusionfromwearableinertialandsurfaceemgsensors
AT xuzhang motorfunctionevaluationofhemiplegicupperextremitiesusingdatafusionfromwearableinertialandsurfaceemgsensors
AT yanangong motorfunctionevaluationofhemiplegicupperextremitiesusingdatafusionfromwearableinertialandsurfaceemgsensors
AT yingcheng motorfunctionevaluationofhemiplegicupperextremitiesusingdatafusionfromwearableinertialandsurfaceemgsensors
AT xiaopinggao motorfunctionevaluationofhemiplegicupperextremitiesusingdatafusionfromwearableinertialandsurfaceemgsensors
AT xiangchen motorfunctionevaluationofhemiplegicupperextremitiesusingdatafusionfromwearableinertialandsurfaceemgsensors
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