Otago Exercise Program (OEP) is a rehabilitation program for older adults to improve frailty, sarcopenia, and balance. Accurate monitoring of patient involvement in OEP is challenging, as self-reports (diaries) are often unreliable. With the development of wearable sensors, Human Activity Recognition (HAR) systems using wearable sensors have revolutionized healthcare. However, their usage for OEP still shows limited performance. The objective of this study is to build an unobtrusive and accurate system to monitor OEP for older adults. Data was collected from older adults wearing a single waist-mounted Inertial Measurement Unit (IMU). Two datasets were collected, one in a laboratory setting, and one at the homes of the patients. A hierarchical system is proposed with two stages: 1) using a deep learning model to recognize whether the patients are performing OEP or activities of daily life (ADLs) using a 10-minute sliding window; 2) based on stage 1, using a 6-second sliding window to recognize the OEP sub-classes performed. The results showed that in stage 1, OEP could be recognized with window-wise f1-scores over 0.95 and Intersection-over-Union (IoU) f1-scores over 0.85 for both datasets. In stage 2, for the home scenario, four activities could be recognized with f1-scores over 0.8: ankle plantarflexors, abdominal muscles, knee bends, and sit-to-stand. The results showed the potential of monitoring the compliance of OEP using a single IMU in daily life. Also, some OEP sub-classes are possible to be recognized for further analysis.

翻译：奥塔哥运动计划（OEP）是一项针对老年人的康复训练方案，旨在改善其虚弱状态、肌少症及平衡能力。准确监测患者对OEP的依从性具有挑战性，因为自我报告（日记）往往不可靠。随着可穿戴传感器技术的发展，基于可穿戴传感器的人体活动识别（HAR）系统已彻底改变了医疗保健领域，但其在OEP监测中的应用仍存在性能局限。本研究旨在构建一套无干扰且精确的老年人OEP监测系统。实验收集了佩戴单个腰挂式惯性测量单元（IMU）的老年人体动数据，并构建了两个数据集：其一在实验室环境采集，另一在患者家中采集。我们提出一种分层监测系统，包含两个阶段：1）利用深度学习模型，通过10分钟滑动窗识别患者是否正在执行OEP或日常活动（ADL）；2）基于第一阶段结果，采用6秒滑动窗识别具体的OEP亚类动作。结果显示：在第一阶段，两个数据集的OEP识别窗口级F1分数均超过0.95，交并比（IoU）F1分数均超过0.85；在第二阶段居家场景中，踝关节跖屈、腹部肌肉训练、膝屈伸及坐-站转换等四项动作的F1分数均超过0.8。本研究证明了在日常生活场景中通过单个IMU监测OEP执行依从性的可行性，同时表明部分OEP亚类动作可被识别以实现进一步分析。