Abstract
Signal segmentation is a crucial stage in the activity recognition process; however, this has been rarely and vaguely characterized so far. Windowing approaches are normally used for segmentation, but no clear consensus exists on which window size should be preferably employed. In fact, most designs normally rely on figures used in previous works, but with no strict studies that support them. Intuitively, decreasing the window size allows for a faster activity detection, as well as reduced resources and energy needs. On the contrary, large data windows are normally considered for the recognition of complex activities. In this work, we present an extensive study to fairly characterize the windowing procedure, to determine its impact within the activity recognition process and to help clarify some of the habitual assumptions made during the recognition system design. To that end, some of the most widely used activity recognition procedures are evaluated for a wide range of window sizes and activities. From the evaluation, the interval 1–2 s proves to provide the best trade-off between recognition speed and accuracy. The study, specifically intended for on-body activity recognition systems, further provides designers with a set of guidelines devised to facilitate the system definition and configuration according to the particular application requirements and target activities.
Highlights
During the last few years, a tremendous interest in the evaluation of people’s habits and daily routines has awakened
Considered the sliding window approach, the most widely used segmentation method, we evaluate the performance of several recognition systems for an extensive set of window sizes that covers the values used in previous works
The activity recognition process consists of several stages, each one of crucial importance
Summary
During the last few years, a tremendous interest in the evaluation of people’s habits and daily routines has awakened. The analysis of human behavior has been demonstrated to be of key value to better understand people’s necessities and demands. This understanding is of utility in a wide variety of fields, from education, medicine or sociology, to gaming or other kinds of industries with a demonstrated potential impact on society [1]. Promoting healthier lifestyles (e.g., encouraging exercising [2,3]), preventing unhealthy habits (e.g., tobacco use or unwholesome food [4,5]), detecting anomalous behaviors (e.g., fall detection [6,7,8]) or tracking conditions (e.g., mobility worsening due to aging or illnesses [9]) are different applications which may profit from the inference of human behavior
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