BACKGROUND: Application of machine learning for classifying human behavior is increasingly common as access to raw accelerometer data improves. The aims of this scoping review are (1) to examine if machine-learning techniques can accurately identify human activity behaviors from raw accelerometer data and (2) to summarize the practical implications of these machine-learning techniques for future work. METHODS: Keyword searches were performed in Scopus, Web of Science, and EBSCO databases in 2018. Studies that applied supervised machine-learning techniques to raw accelerometer data and estimated components of physical activity were included. Information on study characteristics, machine-learning techniques, and key study findings were extracted from included studies. RESULTS: Of the 53 studies included in the review, 75% were published in the last 5 years. Most studies predicted postures and activity type, rather than intensity, and were conducted in controlled environments using 1 or 2 devices. The most common models were support vector machine, random forest, and artificial neural network. Overall, classification accuracy ranged from 62% to 99.8%, although nearly 80% of studies achieved an overall accuracy above 85%. CONCLUSIONS: Machine-learning algorithms demonstrate good accuracy when predicting physical activity components; however, their application to free-living settings is currently uncertain.
BACKGROUND: Application of machine learning for classifying human behavior is increasingly common as access to raw accelerometer data improves. The aims of this scoping review are (1) to examine if machine-learning techniques can accurately identify human activity behaviors from raw accelerometer data and (2) to summarize the practical implications of these machine-learning techniques for future work. METHODS: Keyword searches were performed in Scopus, Web of Science, and EBSCO databases in 2018. Studies that applied supervised machine-learning techniques to raw accelerometer data and estimated components of physical activity were included. Information on study characteristics, machine-learning techniques, and key study findings were extracted from included studies. RESULTS: Of the 53 studies included in the review, 75% were published in the last 5 years. Most studies predicted postures and activity type, rather than intensity, and were conducted in controlled environments using 1 or 2 devices. The most common models were support vector machine, random forest, and artificial neural network. Overall, classification accuracy ranged from 62% to 99.8%, although nearly 80% of studies achieved an overall accuracy above 85%. CONCLUSIONS: Machine-learning algorithms demonstrate good accuracy when predicting physical activity components; however, their application to free-living settings is currently uncertain.
Authors: Sai Krupa Das; Akari J Miki; Caroline M Blanchard; Edward Sazonov; Cheryl H Gilhooly; Sujit Dey; Colton B Wolk; Chor San H Khoo; James O Hill; Robin P Shook Journal: Adv Nutr Date: 2022-02-01 Impact factor: 11.567
Authors: Mikael Anne Greenwood-Hickman; Supun Nakandala; Marta M Jankowska; Dori E Rosenberg; Fatima Tuz-Zahra; John Bellettiere; Jordan Carlson; Paul R Hibbing; Jingjing Zou; Andrea Z Lacroix; Arun Kumar; Loki Natarajan Journal: Med Sci Sports Exerc Date: 2021-11-01
Authors: Maarten Z H Kolk; Diana M Frodi; Tariq O Andersen; Joss Langford; Soeren Z Diederichsen; Jesper H Svendsen; Hanno L Tan; Reinoud E Knops; Fleur V Y Tjong Journal: Cardiovasc Digit Health J Date: 2021-11-24
Authors: Roman P Kuster; Wilhelmus J A Grooten; Victoria Blom; Daniel Baumgartner; Maria Hagströmer; Örjan Ekblom Journal: Int J Environ Res Public Health Date: 2021-05-27 Impact factor: 3.390