Kirchberger Joris, Kunz Dominik, Perrot Guido, Hirsch Sven, Leifke Maren, Hölz Bianca, Geissmann Lukas, Käch Miro, Wehrli Samuel, Eckstein Jens
Directorate of Nursing and Allied Health Professionals, Therapy, University Hospital Basel, Spitalstrasse 21, 4031, Basel, Switzerland.
Research Centre for Computational Health, Institute of Computational Life Sciences, Zurich University of Applied Sciences (ZHAW), Wädenswil, Zürich, Switzerland.
Sci Rep. 2025 Jun 6;15(1):19941. doi: 10.1038/s41598-025-04340-6.
Insufficient physical activity during hospitalization correlates with decreased physical functionality, prolonged stays, and higher readmission rates among the elderly population. Wearable systems provide an approach for monitoring patients' physical activity, data to set achievable goals and motivation for patients to stay active. However, elderly patients often present distinct gait patterns due to walking aids or co-morbidities, and most existing monitoring solutions are trained on data from healthy individuals. Therefore, the main study goal was to develop a wearable based algorithm prototype for three wearing locations (ankle, thigh, wrist) and assess its comparative classification accuracy to determine the optimal location for classifying patient activities during hospitalization. We collected raw accelerometer and gyroscope data from three different body locations (wrist, ankle, and thigh) from 40 patients at the University Hospital Basel. Depending on the patient's mobility status, the protocol comprised up to six activities, including lying, sitting, standing, sit-to-stand, walking, and climbing stairs. We trained two classification models for each location; one based on accelerometer and gyroscope input and the other on accelerometer only. In addition, we assessed the patient experience by questionnaire. The ankle model performs best with an accuracy of 84.6% (accelerometer and gyroscope) and 82.6% (accelerometer). The wrist and thigh models show accuracy results in the 72.4-76.8% range. The patient questionnaire evaluation reveals a high acceptance of 97.7% towards carrying a monitoring device for 8 h throughout the day, regardless of the wearing location. Patients reported the ankle as the least disturbing location in 87.2% cases. Our study showed that the accuracy of the model is clearly dependent on the individual location of the sensor, with the ankle showing the highest weighted accuracy results of the three body sites. In addition, patients reported a high acceptance towards a sensor-based classification system, underlining the feasibility in a clinical setting.Trial registration: The study was approved by the Ethics Committee of Northwest and Central Switzerland (BASEC 202202035) and has been registered at clinicaltrials.gov (NCT06403826).
住院期间身体活动不足与老年人群身体功能下降、住院时间延长和再入院率升高相关。可穿戴系统提供了一种监测患者身体活动的方法，能为设定可实现的目标提供数据，并激励患者保持活动。然而，老年患者由于使用助行器或存在合并症，往往呈现出独特的步态模式，且大多数现有的监测解决方案是基于健康个体的数据进行训练的。因此，主要研究目标是为三个佩戴位置（脚踝、大腿、手腕）开发一种基于可穿戴设备的算法原型，并评估其比较分类准确性，以确定在住院期间对患者活动进行分类的最佳位置。我们从巴塞尔大学医院的40名患者的三个不同身体部位（手腕、脚踝和大腿）收集了原始加速度计和陀螺仪数据。根据患者的活动状态，该方案包括多达六种活动，包括躺卧、坐着、站立、从坐到站、行走和爬楼梯。我们为每个位置训练了两种分类模型；一种基于加速度计和陀螺仪输入，另一种仅基于加速度计。此外，我们通过问卷调查评估了患者的体验。脚踝模型表现最佳，基于加速度计和陀螺仪的准确率为84.6%，仅基于加速度计的准确率为82.6%。手腕和大腿模型的准确率结果在72.4 - 76.8%范围内。患者问卷调查评估显示，无论佩戴位置如何，97.7%的患者对全天佩戴监测设备8小时表示高度接受。87.2%的患者报告称脚踝是最不干扰的位置。我们的研究表明，模型的准确性明显取决于传感器的个体位置，脚踝在三个身体部位中显示出最高的加权准确率结果。此外，患者对基于传感器的分类系统表示高度接受，这突出了其在临床环境中的可行性。试验注册：该研究已获得瑞士西北部和中部伦理委员会（BASEC 202202035）的批准，并已在clinicaltrials.gov（NCT06403826）上注册。
