Dealing with sensor displacement in motion-based onbody activity recognition systems
Abstract
We present a set of heuristics that significantly increase the robustness of motion sensor-based activity recognition with respect to sensor displacement. In this paper placement refers to the position within a single body part (e.g, lower arm). We show how, within certain limits and with modest quality degradation, motion sensorbased activity recognition can be implemented in a displacement tolerant way. We first describe the physical principles that lead to our heuristic. We then evaluate them first on a set of synthetic lower arm motions which are well suited to illustrate the strengths and limits of our approach, then on an extended modes of locomotion problem (sensors on the upper leg) and finally on a set of exercises performed on various gym machines (sensors placed on the lower arm). In this example our heuristic raises the displaced recognition rate from 24% for a displaced accelerometer, which had 96% recognition when not displaced, to 82%.
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The design and functionality of ACC sensors may lead to sensor noise and other inconsistencies like sensor saturation or displacement.
“The design and functionality of ACC sensors may lead to sensor noise and other inconsistencies like sensor saturation or displacement63–65.”
From Data quality evaluation in wearable monitoring (2022) · cites this paper as [65]
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- Authors
- Kunze, K., & Lukowicz, P.
- Affiliations
- University of Passau (DE)
- Venue
- Proceedings of the 10th international conference on Ubiquitous computing
- Publisher
- ACM
- Published
- 2008-09-21
- Pages
- 20-29
- OpenAlex
- W2137846113
- Cited by
- 161
- References
- 20
- Citations by year
- 2025: 2 · 2024: 2 · 2023: 12 · 2022: 7 · 2021: 8 · 2020: 5 · 2019: 2 · 2018: 13 · 2017: 6 · 2016: 7 · 2015: 16 · 2014: 16 · 2013: 12 · 2012: 14
- FWCI
- 10.41
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Health Sciences › Medicine › Physiology › Physical Activity and Health
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Human Pose and Action Recognition
- Keywords
- sensor displacement, motion sensors, accelerometer, sensor placement, lower arm, upper leg, locomotion modes, gym exercise, recognition robustness
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