Department of Computer Engineering and Informatics, University of Patras, 26504 Patras, Greece.
Department of Neurology, University Hospital of Patras, 26504 Patras, Greece.
Sensors (Basel). 2019 Feb 20;19(4):880. doi: 10.3390/s19040880.
The physiological monitoring of older people using wearable sensors has shown great potential in improving their quality of life and preventing undesired events related to their health status. Nevertheless, creating robust predictive models from data collected unobtrusively in home environments can be challenging, especially for vulnerable ageing population. Under that premise, we propose an activity recognition scheme for older people exploiting feature extraction and machine learning, along with heuristic computational solutions to address the challenges due to inconsistent measurements in non-standardized environments. In addition, we compare the customized pipeline with deep learning architectures, such as convolutional neural networks, applied to raw sensor data without any pre- or post-processing adjustments. The results demonstrate that the generalizable deep architectures can compensate for inconsistencies during data acquisition providing a valuable alternative.
利用可穿戴传感器对老年人进行生理监测，已显示出极大的潜力，可以提高他们的生活质量，并预防与其健康状况相关的不良事件。然而，要从非标准化环境中收集到的非侵入式数据中创建稳健的预测模型可能具有挑战性，尤其是对于脆弱的老年人群。在此前提下，我们提出了一种利用特征提取和机器学习的老年人活动识别方案，以及启发式计算解决方案，以解决由于非标准化环境中测量不一致而导致的挑战。此外，我们还将定制的管道与深度学习架构（例如卷积神经网络）进行了比较，这些架构应用于原始传感器数据，而无需进行任何预处理或后处理调整。结果表明，可推广的深度架构可以弥补数据采集过程中的不一致性，提供了一种有价值的替代方法。
