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Accelerometers are widely used to measure physical activity behaviour,including in children. The traditional method for processing acceleration datauses cut points to define physical activity intensity, relying on calibrationstudies that relate the magnitude of acceleration to energy expenditure.However, these relationships do not generalise across diverse populations andhence they must be parametrised for each subpopulation (e.g., age groups) whichis costly and makes studies across diverse populations and over time difficult.A data driven approach that allows physical activity intensity states to emergefrom the data, without relying on parameters derived from external populations,and offers a new perspective on this problem and potentially improved results.We applied an unsupervised machine learning approach, namely a hidden semiMarkov model, to segment and cluster the accelerometer data recorded from 279children (9 to 38 months old) with a diverse range of physical andsocial-cognitive abilities (measured using the Paediatric Evaluation ofDisability Inventory). We benchmarked this analysis with the cut pointsapproach calculated using the best available thresholds for the population.Time spent active as measured by this unsupervised approach correlated morestrongly with measures of the childs mobility, social-cognitive capacity,independence, daily activity, and age than that measured using the cut pointsapproach. Unsupervised machine learning offers the potential to provide a moresensitive, appropriate, and cost-effective approach to quantifying physicalactivity behaviour in diverse populations, compared to the current cut pointsapproach. This, in turn, supports research that is more inclusive of diverse orrapidly changing populations.

Using unsupervised machine learning to quantify physical activity from accelerometry in a diverse and rapidly changing population