Toward Robust Interpretable Human Movement Pattern Analysis in a Workplace Setting

Gaining a better understanding of how people move about and interact with their environment is an important piece of understanding human behavior. Careful analysis of individuals' deviations or variations in movement over time can provide an awareness about changes to their physical or mental state and may be helpful in tracking performance and well-being especially in workplace settings. We propose a technique for clustering and discovering patterns in human movement data by extracting motifs from the time series of durations where participants linger at different locations. Using a data set of over 200 participants moving around a hospital for ten weeks, we show this technique intuitively captures local temporal relationships between hospital rooms and also clusters them in a fashion consistent with the room type labels (e.g. lounge, break room, etc.) without using prior knowledge. Machine learning features derived from these clusters are empirically shown to provide information similar to features attained using domain knowledge of the room type labels directly when predicting mental wellness from self-reports.