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A New Framework for Smartphone Sensor-Based Human Activity Recognition Using Graph Neural Network
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DOI：10.1109/JSEN.2020.3015726.png)
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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.
Keyword:
Legged locomotion
Neural networks
Feature extraction
Computational modeling
Support vector machines
Activity recognition
Human activity recognition
graph neural network (GNN)
message passing
smartphone sensors
deep learning
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4.5
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2.2W
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分享收藏分享收藏Authentication of Smartphone Users Based on Activity Recognition and Mobile Sensing基于活动识别和移动感知的智能手机用户身份认证.png)
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OAAI分享收藏Human Daily and Sport Activity Recognition Using a Wearable inertial Sensor Network使用可穿戴惯性传感器网络识别人类日常和运动活动IEEE ACCESSOAAI
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