Matrix Profile based Anomaly Detection in Streaming Gait Data for Fall Prevention
Branislav Gerazov, Elena Hadzieva, Andrei Krivošei, Fiorella Ines Soto Sanchez, Jakob Rostovski, Alar Kuusik, Mahtab Alam · arXiv (Cornell University) · 2023
The automatic detection of gait anomalies can lead to systems that can be used for fall detection and prevention. In this paper, we present a gait anomaly detection system based on the Matrix Profile (MP) algorithm. The MP algorithm is exact, parameter free, simple and efficient, making it a perfect candidate for on the edge deployment. We propose a gait anomaly detection system that is able to adapt to an individual's gait pattern and successfully detect anomalous steps with short latency. To evaluate the system we record a small database of enacted anomalous steps. The results show the system outperforms a more complex Neural Network baseline.