Annu Int Conf IEEE Eng Med Biol Soc. 2016 Aug;2016:4889-4892. doi: 10.1109/EMBC.2016.7591823.
The goal of this study is to design a novel triboelectric motion sensor in wearable body sensor network for human activity recognition. Physical activity recognition is widely used in well-being management, medical diagnosis and rehabilitation. Other than traditional accelerometers, we design a novel wearable sensor system based on triboelectrification. The triboelectric motion sensor can be easily attached to human body and collect motion signals caused by physical activities. The experiments are conducted to collect five common activity data: sitting and standing, walking, climbing upstairs, downstairs, and running. The k-Nearest Neighbor (kNN) clustering algorithm is adopted to recognize these activities and validate the feasibility of this new approach. The results show that our system can perform physical activity recognition with a successful rate over 80% for walking, sitting and standing. The triboelectric structure can also be used as an energy harvester for motion harvesting due to its high output voltage in random low-frequency motion.
本研究的目标是在可穿戴人体传感器网络中设计一种用于人体活动识别的新型摩擦电运动传感器。身体活动识别在健康管理、医学诊断和康复中得到广泛应用。除了传统的加速度计，我们基于摩擦起电设计了一种新型可穿戴传感器系统。该摩擦电运动传感器可以轻松附着在人体上，并收集由身体活动引起的运动信号。进行实验以收集五种常见活动数据：坐立、行走、上楼、下楼和跑步。采用k近邻（kNN）聚类算法来识别这些活动，并验证这种新方法的可行性。结果表明，我们的系统对于行走、坐立和站立的身体活动识别成功率超过80%。由于其在随机低频运动中具有高输出电压，该摩擦电结构还可以用作运动能量收集的能量采集器。
