Repetitive Activity Monitoring from Multivariate Time Series: A Generic and Efficient Approach

Repetitive activities like breathing and walking account for a large fraction of human activities. Monitoring these activities with sensing technology plays a vital role in numerous applications ranging from health monitoring to manufacturing management. Over the last decade, traditional machine learning approaches and recent end-to-end deep learning paradigms have achieved massive successes in human activity recognition. However, these approaches are mostly scenario dependent and computationally expensive. Moreover, real-world repetitive activities may have varying time intervals between each repetition, which invalidate existing sliding window methods. In this paper, we propose STEM, a Scalable Template Extraction Method for scenario independent monitoring of repetitive activities with varying intervals. Instead of using sliding windows, we detect and locate the appearance of repeating patterns based on the Matrix Profile. Distributional features are then extracted from the identified patterns such that domain knowledge can be avoided. The approach is efficient and robust as shown by the evaluation on three public datasets, in which around 95% of the undesired computation were eliminated with up to 4% accuracy improvement. It is also generic as demonstrated by a use case of respiration rate estimation using wireless signals.