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Sensors 2017, 17(3), 476; doi:10.3390/s17030476

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

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
*
Authors to whom correspondence should be addressed.
Academic Editor: Angelo Maria Sabatini
Received: 16 October 2016 / Revised: 20 December 2016 / Accepted: 22 December 2016 / Published: 27 February 2017
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [7309 KB, uploaded 28 February 2017]   |  

Abstract

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. View Full-Text
Keywords: feature selection; supervised learning; classification; human activity recognition feature selection; supervised learning; classification; human activity recognition
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Fong, S.; Song, W.; Cho, K.; Wong, R.; Wong, K.K.L. Training Classifiers with Shadow Features for Sensor-Based Human Activity Recognition. Sensors 2017, 17, 476.

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