Wearable sensor-based devices are increasingly applied in free-living and clinical settings to collect fine-grained, objective data about activity and sleep behavior. The manufacturers of these devices provide proprietary software that labels the sensor data at specified time intervals with activity and sleep information. If the device wearer has a health condition affecting their movement, such as a stroke, these labels and their values can vary greatly from manufacturer to manufacturer. Consequently, generating outcome predictions based on data collected from patients attending inpatient rehabilitation wearing different sensor devices can be challenging, which hampers usefulness of these data for patient care decisions. In this article, we present a data-driven approach to combining datasets collected from different device manufacturers. With the ability to combine datasets, we merge data from three different device manufacturers to form a larger dataset of time series data collected from 44 patients receiving inpatient therapy services. To gain insights into the recovery process, we use this dataset to build models that predict a patient’s next day physical activity duration and next night sleep duration. Using our data-driven approach and the combined dataset, we obtained a normalized root mean square error prediction of 9.11% for daytime physical activity and 11.18% for nighttime sleep duration. Our sleep result is comparable to the accuracy we achieved using the manufacturer’s sleep labels (12.26%). Our device-independent predictions are suitable for both point-of-care and remote monitoring applications to provide information to clinicians for customizing therapy services and potentially decreasing recovery time.

中文翻译：

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康复人群的独立于可穿戴设备的第二天活动和下一晚睡眠预测

基于可穿戴传感器的设备越来越多地应用于自由生活和临床环境，以收集有关活动和睡眠行为的细粒度、客观数据。这些设备的制造商提供专有软件，可以在指定的时间间隔内用活动和睡眠信息标记传感器数据。如果设备佩戴者有影响其运动的健康状况（例如中风），则这些标签及其值可能因制造商而异。因此，根据从佩戴不同传感器设备的住院康复患者收集的数据生成结果预测可能具有挑战性，这阻碍了这些数据对患者护理决策的有用性。在本文中，我们提出了一种数据驱动的方法来组合从不同设备制造商收集的数据集。通过合并数据集的能力，我们合并了来自三个不同设备制造商的数据，以形成一个更大的时间序列数据集，该数据集从 44 名接受住院治疗服务的患者中收集。为了深入了解恢复过程，我们使用此数据集构建模型来预测患者第二天的身体活动持续时间和下一晚的睡眠持续时间。使用我们的数据驱动方法和组合数据集，我们获得了日间体育活动的归一化均方根误差预测为 9.11%，夜间睡眠持续时间为 11.18%。我们的睡眠结果与我们使用制造商的睡眠标签获得的准确度 (12.26%) 相当。