GAD: A Real-time Gait Anomaly Detection System with Online Adaptive Learning

TL;DR

GAD is a real-time gait anomaly detection system that adapts online to individual walking patterns using accelerometer data, enabling effective detection of gait deviations without offline preprocessing.

Contribution

This paper introduces GAD, a novel real-time gait anomaly detection system that employs online adaptive learning with LSTM and dimensionality reduction, eliminating the need for offline training.

Findings

Higher detection accuracy with personalized gait segment method

Effective online adaptation to gait pattern changes

Successful validation on open-source gait dataset

Abstract

Gait anomaly detection is a task that involves detecting deviations from a person's normal gait pattern. These deviations can indicate health issues and medical conditions in the healthcare domain, or fraudulent impersonation and unauthorized identity access in the security domain. A number of gait anomaly detection approaches have been introduced, but many of them require offline data preprocessing, offline model learning, setting parameters, and so on, which might restrict their effectiveness and applicability in real-world scenarios. To address these issues, this paper introduces GAD, a real-time gait anomaly detection system. GAD focuses on detecting anomalies within an individual's three-dimensional accelerometer readings based on dimensionality reduction and Long Short-Term Memory (LSTM). Upon being launched, GAD begins collecting a gait segment from the user and training an anomaly detector to learn the user's walking pattern on the fly. If the subsequent model verification is successful, which involves validating the trained detector using the user's subsequent steps, the detector is employed to identify abnormalities in the user's subsequent gait readings at the user's request. The anomaly detector will be retained online to adapt to minor pattern changes and will undergo retraining as long as it cannot provide adequate prediction. We explored two methods for capturing users' gait segments: a personalized method tailored to each individual's step length, and a uniform method utilizing a fixed step length. Experimental results using an open-source gait dataset show that GAD achieves a higher detection accuracy ratio when combined with the personalized method.