模式识别(心理学)
加速度计
可穿戴计算机
图形
无线传感器网络
作者
Riktim Mondal,Debadyuti Mukherjee,Pawan Kumar Singh,Vikrant Bhateja,Ram Sarkar
出处
期刊:IEEE Sensors Journal
[Institute of Electrical and Electronics Engineers]
日期:2021-05-15
卷期号:21 (10): 11461-11468
被引量:3
标识
DOI:10.1109/jsen.2020.3015726
摘要
Automatic human activity recognition (HAR) through computing devices is a challenging research topic in the domain of computer vision. It has widespread applications in various fields such as sports, healthcare, criminal investigation and so on. With the advent of smart devices like smartphones, availability of inertial sensors like accelerometer and gyroscope can easily be used to track our daily physical movements. State-of-the-art deep neural network models like Convolutional Neural Network (CNN) do not need any additional feature extraction for such applications. However, it requires huge amount of data for training which is time consuming, and requires ample resource. Another limiting factor of CNN is that it considers only the features of an individual sample for learning without considering any structural information among the samples. To address the aforesaid issues, we propose an end-to-end fast Graph Neural Network (GNN) which not only captures the individual sample information efficiently but also the relationship with other samples in the form of an undirected graph structure. To the best of our knowledge, this is the first work where the time series data are transformed into a structural representation of graph for the purpose of HAR using sensor data. Proposed model has been evaluated on 6 publicly available datasets, and it achieves nearly 100% recognition accuracy for all the 6 datasets. Source code of this work is available at https://github.com/riktimmondal/HAR-Sensor .
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