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 motion data as a means of raising awareness of an individuals own daily physical activity and promote healthy activity behaviour, granted that human activity and music also share similar temporal structure. 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. To that extent, 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 timescales so that segmentation boundaries at a given timescale have a corresponding boundary at a finer timescale, occurring at the same point in time. These properties are useful to display the hierarchical structure of daily events embedded in larger events, which is akin to musical structure. The segmented physical activity data for one day was mapped to musical sounds, resulting in two short musical pieces, one for each subject. 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. We discuss its potential to aid in interventions for behavioural change towards reducing sedentary behaviour and increasing physical activity.

翻译：先前研究表明，加速度计记录的人类活动时间动态与音乐具有相似结构。鉴于人类活动与音乐在时间结构上的共通性，将运动数据进行音乐化呈现可作为提升个体对自身日常身体活动认知、促进健康活动行为的有效手段。本研究开发了一种通过多粒度时间分割量化人类日常活动结构的方法，并将其应用于生成音乐化呈现。为此，我们从数据集中选取了两组身体活动加速度记录，其中一组显示的活动量高于另一组。这些数据在不同时间尺度上进行分割，使得特定时间尺度的分割边界在更细时间尺度上存在对应边界，且两者发生于同一时间点。这种特性有助于展现嵌套于较大事件中的日常活动层级结构，其与音乐结构具有相似性。我们将单日分割后的身体活动数据映射为音乐声音，最终生成了两段分别对应两名受试者的简短音乐片段。通过问卷调查评估参与者识别活跃度较高受试者对应音乐片段的能力，结果显示大多数答案正确。我们认为该方法有潜力成为行为干预领域具有价值的创新技术，并探讨了其在减少久坐行为、增加身体活动方面的行为改变干预应用前景。