Abstract
Measuring physical activity is a critical issue for our understanding of the health benefits of human movement. Machine learning (ML), using accelerometer data, has become a common way to measure physical activity. ML has failed physical activity measurement research in four important ways. First, as a field, physical activity researchers have not adopted and used principles from computer science. Benchmark datasets are common in computer science and allow the direct comparison of different ML approaches. Access to and development of benchmark datasets are critical components in advancing ML for physical activity. Second, the priority of methods development focused on ML has created blind spots in physical activity measurement. Methods, other than cut-point approaches, may be sufficient or superior to ML but these are not prioritised in our research. Third, while ML methods are common in published papers, their integration with software is rare. Physical activity researchers must continue developing and integrating ML methods into software to be fully adopted by applied researchers in the discipline. Finally, training continues to limit the uptake of ML in applied physical activity research. We must improve the development, integration and use of software that allows for ML methods’ broad training and application in the field.
Subject
Physical Therapy, Sports Therapy and Rehabilitation,Orthopedics and Sports Medicine
Reference20 articles.
1. Application of raw accelerometer data and machine-learning techniques to characterize human movement behavior: a systematic scoping review;Narayanan;J Phys Act Health,2020
2. Centers for Disease Control and Prevention . Physical Activity Monitor - Minute (PAXMIN_H), 2020. Available: https://wwwn.cdc.gov/Nchs/Nhanes/2013-2014/PAXMIN_H.htm [Accessed 2 Nov 2021].
3. Ravi D , Wong C , Lo B . Deep learning for human activity recognition: A resource efficient implementation on low-power devices. In: BSN 2016 - 13th Annual Body Sensor Networks Conference. Institute of Electrical and Electronics Engineers Inc. 2016:71–6.doi:10.1109/BSN.2016.7516235
4. Nambiar R , Poess M . Performance evaluation and benchmarking. Springer Berlin Heidelberg, 2009.
5. Weiss GM , Weiss GM , Lockhart JW . The Impact of Personalization on Smartphone-Based Activity Recognition. In: Proceedings of the AAAI-12 Workshop on Activity Context Representation: Techniques and Languages.. Available: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.295.4754 [Accessed 22 Oct 2020].
Cited by
5 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献