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Application of Raw Accelerometer Data and Machine-Learning Techniques to Characterize Human Movement Behavior: A
Journal of Physical Activity & Health
|February 9, 2020
Summary
Machine learning accurately classifies human behavior from accelerometer data, achieving over 85% accuracy in most studies. However, its real-world application in free-living settings remains uncertain.
Area of Science:
- Human behavior classification
- Wearable sensor technology
- Machine learning applications
Background:
- Machine learning (ML) is increasingly used for human behavior classification.
- Advancements in accelerometer data access facilitate this trend.
Purpose of the Study:
- To assess the accuracy of ML techniques in identifying human activities from raw accelerometer data.
- To summarize the practical implications of these ML techniques for future research.
Main Methods:
- Scoping review of studies published up to 2018.
- Included studies applied supervised ML to accelerometer data for physical activity estimation.
- Extracted data on study characteristics, ML models, and findings.
Main Results:
- 75% of 53 studies were published in the last 5 years.
- Most studies focused on posture and activity type, not intensity, in controlled settings.
- Common ML models included support vector machines, random forests, and neural networks.
- Classification accuracy ranged from 62% to 99.8%, with 80% of studies exceeding 85% accuracy.
Conclusions:
- ML algorithms show high accuracy for predicting physical activity components in controlled studies.
- The effectiveness of these ML techniques in free-living, real-world conditions is yet to be determined.
