Department of Physical Therapy, Maastricht University Medical Center, 6229 HX Maastricht, The Netherlands.
CAPHRI School for Public Health and Primary Care, Maastricht University, 6200 MD Maastricht, The Netherlands.
Sensors (Basel). 2021 Feb 27;21(5):1652. doi: 10.3390/s21051652.
Low amounts of physical activity (PA) and prolonged periods of sedentary activity are common in hospitalized patients. Objective PA monitoring is needed to prevent the negative effects of inactivity, but a suitable algorithm is lacking. The aim of this study is to optimize and validate a classification algorithm that discriminates between sedentary, standing, and dynamic activities, and records postural transitions in hospitalized patients under free-living conditions. Optimization and validation in comparison to video analysis were performed in orthopedic and acutely hospitalized elderly patients with an accelerometer worn on the upper leg. Data segmentation window size (WS), amount of PA threshold (PA Th) and sensor orientation threshold (SO Th) were optimized in 25 patients, validation was performed in another 25. Sensitivity, specificity, accuracy, and (absolute) percentage error were used to assess the algorithm's performance. Optimization resulted in the best performance with parameter settings: WS 4 s, PA Th 4.3 counts per second, SO Th 0.8 g. Validation showed that all activities were classified within acceptable limits (>80% sensitivity, specificity and accuracy, ±10% error), except for the classification of standing activity. As patients need to increase their PA and interrupt sedentary behavior, the algorithm is suitable for classifying PA in hospitalized patients.
低量的身体活动（PA）和长时间的久坐活动在住院患者中很常见。需要客观的 PA 监测来预防不活动带来的负面影响，但缺乏合适的算法。本研究的目的是优化和验证一种分类算法，该算法可以区分久坐、站立和动态活动，并记录住院患者在自由生活条件下的姿势转换。在矫形和急性住院的老年患者中，将加速度计佩戴在上腿上，与视频分析进行了优化和验证。在 25 名患者中优化了数据分段窗口大小（WS）、PA 阈值（PA Th）和传感器方向阈值（SO Th），在另外 25 名患者中进行了验证。使用灵敏度、特异性、准确性和（绝对）百分比误差来评估算法的性能。优化后的最佳参数设置为：WS 4 秒，PA Th 4.3 计数/秒，SO Th 0.8 g。验证结果表明，除了站立活动的分类外，所有活动都在可接受的范围内（>80%的灵敏度、特异性和准确性，±10%的误差）。由于患者需要增加 PA 并打断久坐行为，因此该算法适合于对住院患者的 PA 进行分类。
