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Feature Selection for Wearable Smartphone-Based Human Activity Recognition with Able bodied, Elderly, and Stroke Patients
Open accessAbstract
Human activity recognition (HAR), using wearable sensors, is a growing area with the potential to provide valuable information on patient mobility to rehabilitation specialists. Smartphones with accelerometer and gyroscope sensors are a convenient, minimally invasive, and low cost approach for mobility monitoring. HAR systems typically pre-process raw signals, segment the signals, and then extract features to be used in a classifier. Feature selection is a crucial step in the process to reduce potentially large data dimensionality and provide viable parameters to enable activity classification. Most HAR systems are customized to an individual research group, including a unique data set, classes, algorithms, and signal features. These data sets are obtained predominantly from able-bodied participants. In this paper, smartphone accelerometer and gyroscope sensor data were collected from populations that can benefit from human activity recognition: able-bodied, elderly, and stroke patients. Data from a consecutive sequence of 41 mobility tasks (18 different tasks) were collected for a total of 44 participants. Seventy-six signal features were calculated and subsets of these features were selected using three filter-based, classifier-independent, feature selection methods (Relief-F, Correlation-based Feature Selection, Fast Correlation Based Filter). The feature subsets were then evaluated using three generic classifiers (Naïve Bayes, Support Vector Machine, j48 Decision Tree). Common features were identified for all three populations, although the stroke population subset had some differences from both able-bodied and elderly sets. Evaluation with the three classifiers showed that the feature subsets produced similar or better accuracies than classification with the entire feature set. Therefore, since these feature subsets are classifier-independent, they should be useful for developing and improving HAR systems across and within populations.
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“Other studies [44,58] employed Relief-F that takes a filter-method approach to feature selection to keep only the most relevant features.”
From Wearable sensors and machine learning in post-stroke rehabilitation assessment: A systematic review (2022) · cites this paper as [44]
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Metadata
- Authors
- Capela, N. A., Lemaire, E. D., & Baddour, N.
- Affiliations
- University of Ottawa (CA)
- Corresponding author
- Capela, Nicole A.
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2015-04-17
- Volume
- 10
- Issue
- 4
- Pages
- e0124414
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC4401457
- OpenAlex
- W2066338981
- Cited by
- 197
- References
- 36
- Citations by year
- 2026: 9 · 2025: 6 · 2024: 13 · 2023: 20 · 2022: 23 · 2021: 20 · 2020: 22 · 2019: 27 · 2018: 24 · 2017: 18 · 2016: 10 · 2015: 4
- FWCI
- 16.17
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS ONE (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Natural Sciences and Engineering Research Council of Canada
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Physical Activity and Health
- Physical Sciences › Engineering › Biomedical Engineering › Gait Recognition and Analysis
- Keywords
- human activity recognition, feature selection, accelerometer, gyroscope, stroke patients, older adults, mobility monitoring, rehabilitation, ReliefF, correlation-based feature selection, fast correlation-based filter
- MeSH terms
- Smartphone, Aged, Humans, Man-Machine Systems, Monitoring, Physiologic, Safety, Case-Control Studies, Stroke
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