The Otago Exercise Program (OEP) represents a crucial rehabilitation initiative tailored for older adults, aimed at enhancing balance and strength. Despite previous efforts utilizing wearable sensors for OEP recognition, existing studies have exhibited limitations in terms of accuracy and robustness. This study addresses these limitations by employing a single waist-mounted Inertial Measurement Unit (IMU) to recognize OEP exercises among community-dwelling older adults in their daily lives. A cohort of 36 older adults participated in laboratory settings, supplemented by an additional 7 older adults recruited for at-home assessments. The study proposes a Dual-Scale Multi-Stage Temporal Convolutional Network (DS-MS-TCN) designed for two-level sequence-to-sequence classification, incorporating them in one loss function. In the first stage, the model focuses on recognizing each repetition of the exercises (micro labels). Subsequent stages extend the recognition to encompass the complete range of exercises (macro labels). The DS-MS-TCN model surpasses existing state-of-the-art deep learning models, achieving f1-scores exceeding 80% and Intersection over Union (IoU) f1-scores surpassing 60% for all four exercises evaluated. Notably, the model outperforms the prior study utilizing the sliding window technique, eliminating the need for post-processing stages and window size tuning. To our knowledge, we are the first to present a novel perspective on enhancing Human Activity Recognition (HAR) systems through the recognition of each repetition of activities.

翻译：奥塔戈运动计划（OEP）是一项针对老年人设计的旨在改善平衡与力量的关键康复措施。尽管已有研究利用可穿戴传感器进行OEP识别，但现有研究在准确性和鲁棒性方面仍存在局限。本研究通过在社区老年人群体的日常生活中使用单个腰部佩戴的惯性测量单元（IMU）来解决这些局限。36名老年人在实验室环境中参与实验，另招募7名老年人进行居家评估。研究提出了一种双尺度多阶段时序卷积网络（DS-MS-TCN），专用于两个层次的序列到序列分类，并将两者纳入统一的损失函数。在第一阶段，模型聚焦于识别每次运动重复（微观标签）；后续阶段则将识别范围扩展至完整运动序列（宏观标签）。DS-MS-TCN模型超越了现有最优深度学习模型，在评估的四项运动任务中均实现了超过80%的F1分数以及超过60%的交并比（IoU）F1分数。值得注意的是，该模型优于先前采用滑动窗口技术的研究，无需后处理阶段和窗口大小调整。据我们所知，本工作是首次通过识别每次重复运动来提升人体活动识别（HAR）系统，这一视角具有创新性。