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Article

Frailty Level Classification of the Community Elderly Using Microsoft Kinect-Based Skeleton Pose: A Machine Learning Approach

1
Department of Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin 341851416, Iran
2
Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran 1477893855, Iran
3
Bone and Joint Research Center, Chang Gung Memorial Hospital, Taoyuan 33333, Taiwan
4
School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
5
Department of Physical Medicine & Rehabilitation, Chang Gung Memorial Hospital at Linkou and College of Medicine, Chang Gung University, Taoyuan 33302, Taiwan
6
Department of Physical Medicine and Rehabilitation, Bei-Hu Branch, National Taiwan University Hospital, Taipei 10845, Taiwan
7
School of Physical Therapy and Graduate Institute of Rehabilitation Science, College of Medicine, Chang Gung University, Taoyuan 33302, Taiwan
*
Author to whom correspondence should be addressed.
Ghasem Akbari and Mohammad Nikkhoo contributed equally to this work and share first authorship.
Academic Editor: Zdeněk Svoboda
Sensors 2021, 21(12), 4017; https://doi.org/10.3390/s21124017
Received: 2 May 2021 / Revised: 28 May 2021 / Accepted: 7 June 2021 / Published: 10 June 2021
(This article belongs to the Special Issue Sensors for Human Movement Applications)
Frailty is one of the most important geriatric syndromes, which can be associated with increased risk for incident disability and hospitalization. Developing a real-time classification model of elderly frailty level could be beneficial for designing a clinical predictive assessment tool. Hence, the objective of this study was to predict the elderly frailty level utilizing the machine learning approach on skeleton data acquired from a Kinect sensor. Seven hundred and eighty-seven community elderly were recruited in this study. The Kinect data were acquired from the elderly performing different functional assessment exercises including: (1) 30-s arm curl; (2) 30-s chair sit-to-stand; (3) 2-min step; and (4) gait analysis tests. The proposed methodology was successfully validated by gender classification with accuracies up to 84 percent. Regarding frailty level evaluation and prediction, the results indicated that support vector classifier (SVC) and multi-layer perceptron (MLP) are the most successful estimators in prediction of the Fried’s frailty level with median accuracies up to 97.5 percent. The high level of accuracy achieved with the proposed methodology indicates that ML modeling can identify the risk of frailty in elderly individuals based on evaluating the real-time skeletal movements using the Kinect sensor. View Full-Text
Keywords: frailty level; Kinect data; machine learning; feature extraction; classification frailty level; Kinect data; machine learning; feature extraction; classification
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MDPI and ACS Style

Akbari, G.; Nikkhoo, M.; Wang, L.; Chen, C.P.C.; Han, D.-S.; Lin, Y.-H.; Chen, H.-B.; Cheng, C.-H. Frailty Level Classification of the Community Elderly Using Microsoft Kinect-Based Skeleton Pose: A Machine Learning Approach. Sensors 2021, 21, 4017. https://doi.org/10.3390/s21124017

AMA Style

Akbari G, Nikkhoo M, Wang L, Chen CPC, Han D-S, Lin Y-H, Chen H-B, Cheng C-H. Frailty Level Classification of the Community Elderly Using Microsoft Kinect-Based Skeleton Pose: A Machine Learning Approach. Sensors. 2021; 21(12):4017. https://doi.org/10.3390/s21124017

Chicago/Turabian Style

Akbari, Ghasem; Nikkhoo, Mohammad; Wang, Lizhen; Chen, Carl P.C.; Han, Der-Sheng; Lin, Yang-Hua; Chen, Hung-Bin; Cheng, Chih-Hsiu. 2021. "Frailty Level Classification of the Community Elderly Using Microsoft Kinect-Based Skeleton Pose: A Machine Learning Approach" Sensors 21, no. 12: 4017. https://doi.org/10.3390/s21124017

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