加速度计
计算机科学
可穿戴计算机
陀螺仪
人工智能
移动电话
活动识别
深度学习
可穿戴技术
运动(物理)
人工神经网络
移动设备
电话
机器学习
嵌入式系统
工程类
航空航天工程
哲学
操作系统
电信
语言学
作者
Yusuf Ahmed Khan,Syed Imaduddin,Ritik Prabhat,Mohd Wajid
标识
DOI:10.1109/icaccs54159.2022.9785009
摘要
Recently, human activity recognition (HAR) has gained a lot of importance due to its wide range of applications in virtual reality, healthcare, surveillance, security, automated control systems, etc. Latest mobile phones have advanced computational capabilities along with several embedded MEMS sensors, which enable us to detect various physical activities unobtrusively. Incorporating personalized motion samples can improve the accuracy of motion detection by mobile devices or wearable devices that are tailored to the individual. Recent works have demonstrated that the use of machine learning and statistical techniques can detect human activities more accurately. In this paper, we have classified two different physical activities, viz., walking and brisk-walking using a deep neural network (DNN). The personalized data has been collected using multiple sensors of the mobile phone with the two kinds of physical activity. Using mobile phone sensors like accelerometer, gyroscope, magnetometer, etc, data has been collected, examined, and used for training and testing the DNN model. Out of multiple sensors on the phone, we have identified the sensors which are more appropriate for the given motion/activities. Finally, we have achieved a classification accuracy of 96.5%.
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