A Comprehensive Study of Activity Recognition Using Accelerometers
Open accessPreprint — published as 10.3390/informatics5020027 · Paper page
Abstract
This paper serves a survey and empirical evaluation of the state-of-the-art in activity recognition methods using accelerometers. We examine research that has focused on the selection of activities, the features that are extracted from the accelerometer data, the segmentation of the time-series data, the locations of accelerometers, the selection and configuration trade-offs, the test/retest reliability, and the generalisation performance. Furthermore, we study these questions from an experimental platform and show, somewhat surprisingly, that many disparate experimental configurations yield comparable predictive performance on testing data. Our understanding of these results is that the experimental setup directly and indirectly defines a pathway for context to be delivered to the classifier, and that, in some settings, certain configurations are more optimal than alternatives. We conclude by identifying how the main results of this work can be used in practice, specifically in experimental configurations in challenging experimental conditions.
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For human-scale activities, most discriminative information lies in low-frequency bands, typically well below 10-15 Hz.
“For human-scale activities (e.g., walking, running, jumping), most discriminative information lies in low-frequency bands, typically well below 10–15 Hz [42].”
From Towards User-Generalizable Wearable-Sensor-Based Human Activity Recognition: A Multi-Task Contrastive Learning Approach (2025) · cites this paper as [42]
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References 95 · Cited by 143
Research works printed but not linked: 12
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- Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 19 March 2018 doi:10.20944/preprints201803.0147.v1 Peer-reviewed version available at Informatics 2018, 5, 27; doi:10.3390/ · DOI · Paper page
- Authors
- Twomey, N., Diethe, T., Fafoutis, X., Elsts, A., McConville, R., Flach, P., & Craddock, I.
- Affiliations
- University of Bristol (GB)
- Corresponding author
- Twomey, Niall
- Diethe, Tom
- Venue
- Informatics
- Publisher
- MDPI AG
- Published
- 2018-03-19
- Volume
- 5
- Issue
- 2
- Pages
- 27-27
- ISSN
- 2227-9709 (ISSN-L)
- OpenAlex
- W2790414609
- Cited by
- 143
- References
- 95
- Citations by year
- 2026: 1 · 2025: 9 · 2024: 20 · 2023: 10 · 2022: 19 · 2021: 30 · 2020: 24 · 2019: 26 · 2018: 4
- FWCI
- 10.50
- Type
- journal article
- Language
- en
- Open access
- gold
- dx.doi.org
- Locations
- Informatics (publishedVersion, cc-by)
- Preprints.org (acceptedVersion, cc-by)
- Explore Bristol Research (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Bristol Research (University of Bristol) (submittedVersion, cc-by)
- Funders
- Horizon 2020 Framework Programme
- Medical Research Council (MC_PC_16029)
- Engineering and Physical Sciences Research Council (EP/K031910/1, H2020)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Health Sciences › Medicine › Physiology › Physical Activity and Health
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Keywords
- activity recognition, accelerometer, wearable sensors, accelerometer placement, time series segmentation, feature extraction, activity selection, test-retest reliability, generalization, field deployment
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