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TPM Feature Set: a Universal Algorithm for Spatial-Temporal Pressure Mapping Imagery Data

Authors:
Bo Zhou
Paul Lukowicz

Keywords: textile pressure mapping; data analysis; machine learning algorithm

Abstract:
There have been many studies in recent years using the Textile planar Pressure Mapping (TPM) technology for computer-human interactions and ubiquitous activity recognition. A TPM sensing system generates a time sequence of spatial pressure imagery. We propose a novel, comprehensive and unified feature set to evaluate TPM data from the space and time domain. The initial version of the TPM feature set presented in this paper includes 663 temporal features and 80 spatial features. We evaluated the feature set on 3 datasets from past studies in the scopes of ambient, smart object and wearable sensing. The TPM feature set has shown superior recognition accuracy compared with the ad-hoc algorithms from the corresponding studies. Furthermore, we have demonstrated the general approach to further reduce and optimise the feature calculation process for specific applications with neighbourhood component analysis.

Pages: 1 to 8

Copyright: Copyright (c) IARIA, 2019

Publication date: September 22, 2019

Published in: conference

ISSN: 2308-4278

ISBN: 978-1-61208-736-8

Location: Porto, Portugal

Dates: from September 22, 2019 to September 26, 2019