Physical activity during hip fracture rehabilitation is essential for mitigating long-term functional decline in geriatric patients. However, it is rarely quantified in clinical practice. Existing continuous monitoring systems with commercially available wearable activity trackers are typically developed in middle-aged adults and therefore perform unreliably in older adults with slower and more variable gait patterns. This study aimed to develop a robust human activity recognition (HAR) system to improve continuous physical activity recognition in the context of hip fracture rehabilitation. 24 healthy older adults aged over 80 years were included to perform activities of daily living (walking, standing, sitting, lying down, and postural transfers) under simulated free-living conditions for 75 minutes while wearing two accelerometers positioned on the lower back and anterior upper thigh. Model robustness was evaluated using leave-one-subject-out cross-validation. The synthetic data demonstrated potential to improve generalization across participants. The resulting feature intervention model (FIM), aided by synthetic data guidance, achieved reliable activity recognition with mean F1-scores of 0.896 for walking, 0.927 for standing, 0.997 for sitting, 0.937 for lying down, and 0.816 for postural transfers. Compared with a control condition model without synthetic data, the FIM significantly improved the postural transfer detection, i.e., an activity class of high clinical relevance that is often overlooked in existing HAR literature. In conclusion, these preliminary results demonstrate the feasibility of robust activity recognition in older adults. Further validation in hip fracture patient populations is required to assess the clinical utility of the proposed monitoring system.

翻译：髋部骨折康复期间的体力活动对于缓解老年患者长期功能衰退至关重要，然而在临床实践中却鲜少被量化。现有的基于商用可穿戴活动追踪器的连续监测系统通常针对中年人群开发，因此在步态模式更缓慢且多变的老年群体中表现不可靠。本研究旨在开发一种鲁棒的人类活动识别系统，以提升髋部骨折康复场景下的连续体力活动识别能力。研究纳入了24名80岁以上的健康老年人，在模拟自由生活条件下佩戴置于腰背部和大腿前侧的加速度计，执行日常活动（行走、站立、坐立、躺卧及姿势转换）75分钟。模型鲁棒性采用留一受试者交叉验证进行评估。合成数据显示出提升模型跨参与者泛化能力的潜力。通过合成数据引导构建的特征干预模型实现了可靠的活动识别，其平均F1分数分别为：行走0.896、站立0.927、坐立0.997、躺卧0.937、姿势转换0.816。与未使用合成数据的对照模型相比，该模型显著提升了姿势转换（即具有高临床价值却常被现有活动识别研究忽视的活动类别）的检测性能。综上所述，这些初步结果验证了在老年群体中实现鲁棒活动识别的可行性。未来需要在髋部骨折患者群体中进一步验证，以评估所提监测系统的临床实用性。