Gait analysis plays a vital role in medicine as it can help diagnose illness, monitor recovery, and measure physical performance. Related work in gait analysis has primarily utilized laboratory data due to its inherently low noise and ease of preprocessing. Daily data, gathered through wearables sensors, can also significantly impact medical care. Nonetheless, working with such data poses numerous challenges.

This article proposes an algorithm to solve the problems associated with gait segmentation in daily data obtained by inertial measurement units (IMUs) on wearable devices. The proposed algorithm can handle time-series data collected by wearable IMU sensors, including noise and different gaits. It also remains effective even when the placement of wearable sensors is in a non-standard manner, making it well-suited for use in various settings.

Data was collected using Xsens wearable device, which primarily employ inertial measurement units (IMUs) for capturing precise movement data. Principal component analysis (PCA) was used to reduce dimensionality. The proposed algorithm within this article can identify the start and end points of each gait segment within the time series, and the same type of gait will be grouped together.

Based on comparison with data marked manually, our algorithm achieved high performance for real life gait segmentation, while also demonstrating strong ability to distinguish between different gait patterns. Moreover, the versatility of the algorithm shows promising applications in fields such as rehabilitation, disease evaluation, and sports performance optimization.