Human activity recognition based on the inertial information and convolutional neural network.
10.7507/1001-5515.201905042
- Author:
Xinke LI
1
,
2
;
Xinyu LIU
3
;
Yongming LI
3
;
Hailin CAO
3
;
Yihang CHEN
3
;
Yicheng LIN
3
;
Xinxin HUANG
3
Author Information
1. School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, P.R.China
2. College of Medical Informatics, Chongqing Medical University, Chongqing 400016, P.R.China.
3. School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, P.R.China.
- Publication Type:Journal Article
- Keywords:
K nearest neighbor algorithm;
acceleration sensors;
convolutional neural network;
human activity recognition;
random forest
- MeSH:
Algorithms;
Cluster Analysis;
Human Activities;
Humans;
Motion;
Neural Networks, Computer
- From:
Journal of Biomedical Engineering
2020;37(4):596-601
- CountryChina
- Language:Chinese
-
Abstract:
With the rapid improvement of the perception and computing capacity of mobile devices such as smart phones, human activity recognition using mobile devices as the carrier has been a new research hot-spot. The inertial information collected by the acceleration sensor in the smart mobile device is used for human activity recognition. Compared with the common computer vision recognition, it has the following advantages: convenience, low cost, and better reflection of the essence of human motion. Based on the WISDM data set collected by smart phones, the inertial navigation information and the deep learning algorithm-convolutional neural network (CNN) were adopted to build a human activity recognition model in this paper. The K nearest neighbor algorithm (KNN) and the random forest algorithm were compared with the CNN network in the recognition accuracy to evaluate the performance of the CNN network. The classification accuracy of CNN model reached 92.73%, which was much higher than KNN and random forest. Experimental results show that the CNN algorithm model can achieve more accurate human activity recognition and has broad application prospects in predicting and promoting human health.
