Preprocessing techniques for context recognition from accelerometer data
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Extracting more useful features from the original signal requires sufficient expert knowledge, which inevitably limits a systematic exploration of the feature space.
“Extracting more useful features from the original signal requires suf cient expert knowledge, which inevitably limits a systematic exploration of the feature space [17].”
From InnoHAR: A Deep Neural Network for Complex Human Activity Recognition (2019) · cites this paper as [17]
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- Authors
- Figo, D., Diniz, P. C., Ferreira, D. R., & Cardoso, J. M. P.
- Affiliations
- Instituto Superior Técnico (PT)
- Universidade do Porto (PT)
- Corresponding author
- Ferreira, Diogo R.
- Venue
- Personal and Ubiquitous Computing
- Published
- 2010-03-30
- Volume
- 14
- Issue
- 7
- Pages
- 645-662
- ISSN
- 1617-4909 (ISSN-L)
- 1617-4917
- OpenAlex
- W2097575504
- Cited by
- 578
- References
- 65
- Citations by year
- 2026: 15 · 2025: 10 · 2024: 21 · 2023: 23 · 2022: 36 · 2021: 55 · 2020: 55 · 2019: 59 · 2018: 65 · 2017: 51 · 2016: 52 · 2015: 45 · 2014: 39 · 2013: 27 · 2012: 14
- FWCI
- 19.31
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Personal and Ubiquitous Computing (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Indoor and Outdoor Localization Technologies
- Keywords
- context recognition, accelerometer data, activity recognition, context-aware services, mobile devices, signal processing, walking and running detection, GPS data, daily routine, preprocessing techniques
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