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：奥塔戈运动计划（OEP）是针对老年人的重要康复计划，旨在增强其力量和平衡能力，从而预防跌倒。尽管人类活动识别（HAR）系统已广泛应用于个体活动识别，但现有系统在OEP场景中侧重于宏观活动（即同一运动的重复序列）的持续时间，而忽略了识别微观活动（即运动的单个重复动作）的能力。本研究提出了一种新型半监督机器学习方法，旨在弥合OEP微观活动识别中的这一空白。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，随后由时序卷积网络（TCN）进行分类。同时，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有监督学习（分类任务）的性能，F1分数超过临床适用阈值0.8即为例证。基于微观活动，可得出两项临床相关结果：统计每项运动的重复次数并计算坐椅起立过程中的速度。这些结果能够自动监测老年人日常生活中的运动强度与难度。