Training Classifiers with Shadow Features for Sensor-Based Human Activity Recognition

Sensors

卷:17

Simon Fong¹  Wei Song²  Kyungeun Cho³  Raymond Wong⁴  Kelvin K. L. Wong⁵ 

[1] Department of Computer and Information Science, University of Macau, Taipa 999078, Macau, China;

[2] Department of Digital Media Technology, North China University of Technology, Beijing 100144, China;

[3] Department of Multimedia Engineering, Dongguk University, Seoul 04620, Korea;

[4] School of Computer Science and Engineering, University of New South Wales, Sydney 2052, NSW, Australia;

[5] School of Medicine, Western Sydney University, Sydney 2560, NSW, Australia;

关键词: feature selection;    supervised learning;    classification;    human activity recognition;   

DOI  :  10.3390/s17030476

来源: DOAJ

【 摘 要 】

In this paper, a novel training/testing process for building/using a classification model based on human activity recognition (HAR) is proposed. Traditionally, HAR has been accomplished by a classifier that learns the activities of a person by training with skeletal data obtained from a motion sensor, such as Microsoft Kinect. These skeletal data are the spatial coordinates (x, y, z) of different parts of the human body. The numeric information forms time series, temporal records of movement sequences that can be used for training a classifier. In addition to the spatial features that describe current positions in the skeletal data, new features called ‘shadow features’ are used to improve the supervised learning efficacy of the classifier. Shadow features are inferred from the dynamics of body movements, and thereby modelling the underlying momentum of the performed activities. They provide extra dimensions of information for characterising activities in the classification process, and thereby significantly improve the classification accuracy. Two cases of HAR are tested using a classification model trained with shadow features: one is by using wearable sensor and the other is by a Kinect-based remote sensor. Our experiments can demonstrate the advantages of the new method, which will have an impact on human activity detection research.

【 授权许可】

Unknown