论文标题
时间序列加速度计的人类活动识别使用LSTM复发神经网络数据
Human Activity Recognition on Time Series Accelerometer Sensor Data using LSTM Recurrent Neural Networks
论文作者
论文摘要
通过智能设备获得的传感器在几种应用中遍布日常生活，包括人类活动监测，医疗保健和社交网络。在这项研究中，我们专注于使用智能手表加速度计传感器来识别饮食活动。更具体地说，我们在食用披萨时从10名参与者那里收集了传感器数据。使用此信息以及其他可用于类似事件的可比较数据，例如吸烟和药物治疗，以及慢跑的不同活动，我们开发了一种LSTM-ANN体系结构，与泡芙，服药或慢跑活动相比，它在识别单个叮咬方面表现出了90％的成功。
The use of sensors available through smart devices has pervaded everyday life in several applications including human activity monitoring, healthcare, and social networks. In this study, we focus on the use of smartwatch accelerometer sensors to recognize eating activity. More specifically, we collected sensor data from 10 participants while consuming pizza. Using this information, and other comparable data available for similar events such as smoking and medication-taking, and dissimilar activities of jogging, we developed a LSTM-ANN architecture that has demonstrated 90% success in identifying individual bites compared to a puff, medication-taking or jogging activities.
