Activity Recognition Using a Single Accelerometer Placed at the Wrist or Ankle
Open accessAbstract
PURPOSE: Large physical activity surveillance projects such as the UK Biobank and NHANES are using wrist-worn accelerometer-based activity monitors that collect raw data. The goal is to increase wear time by asking subjects to wear the monitors on the wrist instead of the hip, and then to use information in the raw signal to improve activity type and intensity estimation. The purposes of this work was to obtain an algorithm to process wrist and ankle raw data and to classify behavior into four broad activity classes: ambulation, cycling, sedentary, and other activities. METHODS: Participants (N = 33) wearing accelerometers on the wrist and ankle performed 26 daily activities. The accelerometer data were collected, cleaned, and preprocessed to extract features that characterize 2-, 4-, and 12.8-s data windows. Feature vectors encoding information about frequency and intensity of motion extracted from analysis of the raw signal were used with a support vector machine classifier to identify a subject's activity. Results were compared with categories classified by a human observer. Algorithms were validated using a leave-one-subject-out strategy. The computational complexity of each processing step was also evaluated. RESULTS: With 12.8-s windows, the proposed strategy showed high classification accuracies for ankle data (95.0%) that decreased to 84.7% for wrist data. Shorter (4 s) windows only minimally decreased performances of the algorithm on the wrist to 84.2%. CONCLUSIONS: A classification algorithm using 13 features shows good classification into the four classes given the complexity of the activities in the original data set. The algorithm is computationally efficient and could be implemented in real time on mobile devices with only 4-s latency.
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A strong random forest model with tried-and-tested time series features has often been neglected in baseline model comparisons.
“a strong random forest model with tried-and-tested time series features, which has often been neglected in baseline model comparisons8,48–50.”
From Self-supervised learning for human activity recognition using 700,000 person-days of wearable data (2024) · cites this paper as [49]
The Results report activity classification using time-frequency and other accelerometer features. But Materials and methods (§3, Classifier validation) says the paper used a support vector machine, not a random forest. Its comparison with prior studies does not establish that strong random-forest baselines have often been neglected. (LLM, conf=0.83)
Experimental — accuracy not yet measuredBicycling can be reliably detected from accelerometer data, an activity previously difficult to classify in laboratory studies.
“Our study shows that bicycling can be reliably detected from accelerometer data, an activity previously difficult to classify in laboratory studies 15 , 19 .”
From Statistical machine learning of sleep and physical activity phenotypes from sensor data in 96,220 UK Biobank participants (2018) · cites this paper as [15]
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- Authors
- MANNINI, A., INTILLE, S. S., ROSENBERGER, M., SABATINI, A. M., & HASKELL, W.
- Affiliations
- Northeastern University (US)
- Stanford University (US)
- Scuola Superiore Sant'Anna (IT)
- Venue
- Medicine & Science in Sports & Exercise
- Published
- 2013-11
- Volume
- 45
- Issue
- 11
- Pages
- 2193-2203
- ISSN
- 0195-9131 (ISSN-L)
- PMC
- PMC3795931
- OpenAlex
- W2094523840
- Cited by
- 383
- References
- 23
- Citations by year
- 2026: 10 · 2025: 13 · 2024: 15 · 2023: 16 · 2022: 31 · 2021: 41 · 2020: 37 · 2019: 37 · 2018: 54 · 2017: 41 · 2016: 32 · 2015: 30 · 2014: 22 · 2013: 3
- FWCI
- 32.61
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Medicine & Science in Sports & Exercise (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- CINECA IRIS Institutional Research Information System (Sant'Anna School of Advanced Studies) (submittedVersion)
- Funders
- National Heart, Lung, and Blood Institute (U01HL091737, T32HL007034)
- 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, wrist-worn accelerometers, physical activity classification, ambulation, cycling, sedentary behavior, feature extraction, support vector machine, wearable activity trackers, real-time classification, computational complexity
- MeSH terms
- Activities of Daily Living, Adolescent, Adult, Aged, Algorithms, Ankle, Electronic Data Processing, Bicycling, Female, Humans, Male, Middle Aged, Motor Activity, Wrist, Walking, Young Adult, Accelerometry
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