Abstract
This article explores how placement variations in user-carried electronic appliances influence human action recognition and how such influence can be mitigated. The authors categorize possible variations into three classes: placement on different body parts (such as a jacket pocket versus a hip holster versus a trouser pocket), small displacement within a given coarse location (such as a device shifting in a pocket), and different orientations. For each of these variations, they present a systematic evaluation of the impact on human action recognition and give an overview of possible approaches to deal with them. They conclude with a detailed practical example on how to compensate for on-body placements variations that builds on an extension of their previous work. This article is part of a special issue on wearable computing.
Original language | English |
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Article number | 6926690 |
Pages (from-to) | 32-41 |
Number of pages | 10 |
Journal | IEEE Pervasive Computing |
Volume | 13 |
Issue number | 4 |
DOIs | |
Publication status | Published - 2014 Oct 1 |
Externally published | Yes |
Keywords
- activity recognition
- inertial motion sensors
- mobile
- pervasive computing
- placement variations
- wearables
ASJC Scopus subject areas
- Software
- Computer Science Applications
- Computational Theory and Mathematics