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 semi-supervised 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 unsupervised 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.

翻译：奥塔哥运动计划（OEP）是一项针对老年人的重要康复计划，旨在增强其力量与平衡能力，从而预防跌倒。尽管人体活动识别（HAR）系统已广泛用于识别个体活动，但在OEP场景中，现有系统主要关注宏观活动（即同一练习的重复序列）的持续时间，而缺乏识别微观活动（即练习的单个重复）的能力。本研究提出了一种新颖的半监督机器学习方法，旨在弥补OEP微观活动识别领域的这一空白。为应对有限的数据集规模，我们的模型采用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，该Transformer编码器被用于掩码无监督学习以重构输入信号。结果表明，掩码无监督学习任务提升了监督学习（分类任务）的性能，其F1分数超过临床适用阈值0.8即为明证。从微观活动中可得出两项临床相关结果：统计每项练习的重复次数，以及计算椅子起立过程中的速度。这些结果使得对老年人日常生活中运动强度与难度的自动监测成为可能。