2022

Mendoza, Juan Ignacio; Danso, Andrew; Luck, Geoff; Rantalainen, Timo; Palmberg, Lotta; Chastin, Sebastien

Previous research has shown that the temporal dynamics of human activity recorded by accelerometers share a similar structure with music. This opens the possibility to use musical sonification of accelerometry data to raise awareness of daily physical activity. In this study a method was developed for quantifying the daily structure of human activity using multigranular temporal segmentation, and applying it to produce musical sonifications. Two accelerometry recordings of physical activity were selected from a dataset, such that one shows more physical activity than the other. These data were segmented in different time-scales so that segmentation boundaries at a given time-scale have a corresponding boundary at a finer time-scale, occurring at the same point in time. This produced a hierarchical structure of daily events embedded in larger events, which is akin to musical structure. The segmented daily data of each subject was mapped to musical sounds, resulting in two short musical pieces. A survey measured the extent to which people would identify the piece corresponding to the most active subject, resulting in a majority of correct answers. We propose that this method has potential to be a valuable and innovative technique for behavioural change towards reducing sedentary behaviour and increasing physical activity.