Automatic Evaluation of Functional Movement Screening Based on Attention Mechanism and Score Distribution Prediction

Xiuchun Lin, Tao Huang, Zhiqiang Ruan, Xuechao Yang, Zhide Chen (), Guolong Zheng () and Chen Feng
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Xiuchun Lin: Fujian Institute of Education, Fuzhou 350025, China
Tao Huang: College of Computer and Control Engineering, Minjiang University, Fuzhou 350108, China
Zhiqiang Ruan: College of Computer and Control Engineering, Minjiang University, Fuzhou 350108, China
Xuechao Yang: College of Arts, Business, Law, Education & IT, Victoria University, Melbourne, VIC 8001, Australia
Zhide Chen: College of Computer and Cyber Security, Fujian Normal University, Fuzhou 350117, China
Guolong Zheng: College of Computer and Control Engineering, Minjiang University, Fuzhou 350108, China
Chen Feng: Fuzhou Polytechnic, Fuzhou 350108, China

Mathematics, 2023, vol. 11, issue 24, 1-16

Abstract: Functional movement screening (FMS) is a crucial testing method that evaluates fundamental movement patterns in the human body and identifies functional limitations. However, due to the inherent complexity of human movements, the automated assessment of FMS poses significant challenges. Prior methodologies have struggled to effectively capture and model critical human features in video data. To address this challenge, this paper introduces an automatic assessment approach for FMS by leveraging deep learning techniques. The proposed method harnesses an I3D network to extract spatiotemporal video features across various scales and levels. Additionally, an attention mechanism (AM) module is incorporated to enable the network to focus more on human movement characteristics, enhancing its sensitivity to diverse location features. Furthermore, the multilayer perceptron (MLP) module is employed to effectively discern intricate patterns and features within the input data, facilitating its classification into multiple categories. Experimental evaluations conducted on publicly available datasets demonstrate that the proposed approach achieves state-of-the-art performance levels. Notably, in comparison to existing state-of-the-art (SOTA) methods, this approach exhibits a marked improvement in accuracy. These results corroborate the efficacy of the I3D-AM-MLP framework, indicating its significance in extracting advanced human movement feature expressions and automating the assessment of functional movement screening.

Keywords: functional movement screening (FMS); human movement feature; Inflated 3D ConvNet (I3D); attention mechanism; multilayer perceptron (MLP) (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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