Geometric Gait Clustering for Unobtrusive Analysis.

Document Type

Conference Proceeding

Publication Date

2023

Abstract

An important field within Human Activity Recognition is the evaluation of disease and patient recovery through the assessment of gait patterns. Clustering has been used as a data-mining technique to find the prior patterns in subjects’ gait patterns. Previous studies have shown the discriminative power of gait clustering on biometrics, and the ability to detect abnormal gait patterns and gait pathology. Previous techniques have relied on expensive machinery and closed environments for gait pattern extraction and/or simplistic featured approaches to clustering. Geometric time series clustering has developed in other fields as a method for incorporating the information from an entire time series sequence and comparing sequences with temporal distortions. We present a method for geometric gait clustering using accelerometer data from wearable sensors. Our methods include an approach to gait cycle averaging and a two-way clustering method for assessing the similarity of biometrics within gait cycle clusters. Our results demonstrate that our methods have significant discriminative efficacy for biometrics and may be a useful analytical tool for gait pathology.

Original Publication Citation

Ellison, Grant, et al. “Geometric Gait Clustering for Unobtrusive Analysis.” 2023 IEEE 19th International Conference on Body Sensor Networks (BSN), 2023, pp. 1–4. IEEE Xplore, https://doi.org/10.1109/BSN58485.2023.10331324.

Link to Full Text

Share

COinS