A Masked Multi-Task Learning Approach for Otago Micro Labels Recognition.

IF 5.2 2区 医学 Q2 ENGINEERING, BIOMEDICAL
Meng Shang, Lenore Dedeyne, Jolan Dupont, Laura Vercauteren, Nadjia Amini, Laurence Lapauw, Evelien Gielen, Sabine Verschueren, Carolina Varon, Walter De Raedt, Bart Vanrumste
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引用次数: 0

Abstract

The Otago Exercise Program (OEP) serves as a vital rehabilitation initiative for older adults, aiming to enhance their strength and balance, and consequently prevent falls. While Human Activity Recognition (HAR) systems have been widely employed in recognizing the activities of individuals, existing systems focus on the duration of macro activities (i.e. a sequence of repetitions of the same exercise), neglecting the ability to discern micro activities (i.e. the individual repetitions of the exercises), in the case of OEP. This study presents a novel multi-task machine learning approach aimed at bridging this gap in recognizing the micro activities of OEP. To manage the limited dataset size, our model utilizes a Transformer encoder for feature extraction, subsequently classified by a Temporal Convolutional Network (TCN). Simultaneously, the Transformer encoder is employed for masked self-supervised learning to reconstruct input signals. Results indicate that the masked unsupervised learning task enhances the performance of the supervised learning (classification task), as evidenced by f1-scores surpassing the clinically applicable threshold of 0.8. From the micro activities, two clinically relevant outcomes emerge: counting the number of repetitions of each exercise and calculating the velocity during chair rising. These outcomes enable the automatic monitoring of exercise intensity and difficulty in the daily lives of older adults.

奥塔哥微标签识别的掩码多任务学习方法。
奥塔哥运动计划(OEP)是老年人的重要康复计划,旨在增强他们的力量和平衡,从而防止跌倒。虽然人类活动识别(HAR)系统已被广泛用于识别个体的活动,但现有的系统侧重于宏观活动的持续时间(即同一练习的一系列重复),而忽略了识别微观活动的能力(即练习的个体重复),在OEP的情况下。本研究提出了一种新的多任务机器学习方法,旨在弥合这一差距,以识别OEP的微观活动。为了管理有限的数据集大小,我们的模型使用Transformer编码器进行特征提取,随后通过时序卷积网络(TCN)进行分类。同时,采用变压器编码器进行掩模自监督学习,重构输入信号。结果表明,掩蔽无监督学习任务提高了监督学习(分类任务)的性能,f1得分超过了临床适用的阈值0.8。从微观活动中,出现了两个临床相关的结果:计算每次运动的重复次数和计算椅子上升的速度。这些结果可以自动监测老年人日常生活中的运动强度和难度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
8.60
自引率
8.20%
发文量
479
审稿时长
6-12 weeks
期刊介绍: Rehabilitative and neural aspects of biomedical engineering, including functional electrical stimulation, acoustic dynamics, human performance measurement and analysis, nerve stimulation, electromyography, motor control and stimulation; and hardware and software applications for rehabilitation engineering and assistive devices.
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