Qi Jun, Yang Yun, Peng Xiyang, Newcombe Lee, Simpson Andrew, Yang Po
Annu Int Conf IEEE Eng Med Biol Soc. 2019 Jul;2019:2492-2495. doi: 10.1109/EMBC.2019.8856617.
Physical activity (PA) is widely recognized as one of the important elements of personal healthy life. To date, as the development of wearable sensing technologies, it is possible to utilize wearable devices and machine learning algorithms to efficiently and accurately monitor PA types, intensity and its associated human pattern for many health applications. But there is a trade-off between less-attachment of wearable devices and achievement of high accuracy in existing PA recognition studies. This paper attempts to investigate possible utilisation of Artificial Neural Networks (ANN) achieving high recognition accuracy of PA using less-attachments of wearable devices. Following a four-steps designed experimental protocol, we collect the real activities dataset with only belt and wristband devices from 10 healthy subjects at home and gym environment. The parameters of typical PA recognition with ANN including time window sizes, features and activation functions are evaluated under 24 different subjects of activities. The experimental results indicate that ANN dealing with belt and wristband data can achieve satisfactory PA recognition results in dynamic and sedentary activities but suffers from transitional activities in both environments.
体育活动（PA）被广泛认为是个人健康生活的重要元素之一。迄今为止，随着可穿戴传感技术的发展，利用可穿戴设备和机器学习算法来高效、准确地监测PA类型、强度及其相关的人类模式以用于多种健康应用成为可能。但在现有的PA识别研究中，可穿戴设备的较少附着与高精度的实现之间存在权衡。本文试图研究利用人工神经网络（ANN）在可穿戴设备附着较少的情况下实现PA的高识别准确率的可能性。遵循一个四步设计的实验方案，我们仅使用腰带和腕带设备，从10名健康受试者在家庭和健身房环境中收集真实活动数据集。在24种不同的活动受试者情况下，评估了使用ANN进行典型PA识别的参数，包括时间窗口大小、特征和激活函数。实验结果表明，处理腰带和腕带数据的ANN在动态和久坐活动中可以取得令人满意的PA识别结果，但在两种环境中的过渡活动中存在不足。
