  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.
