Gait Segmentation and Grouping in Daily Data Collected from Wearable IMU Sensors

Abstract

Gait analysis plays a vital role in medicine as it can help diagnose illnesses, monitor recovery, and measure physical performance. Related work in gait analysis has primarily utilized laboratory data due to the inherently low noise and ease of preprocessing. Daily data, gathered through wearable sensors, can also significantly impact medical care. Nonetheless, working with such data poses numerous challenges. This paper proposes an algorithm to solve the problems associated with gait segmentation of daily data obtained by inertial measurement units (IMUs) in wearable devices. The proposed algorithm can handle time-series data collected by wearable IMU sensors, including noise and different gaits. The proposed algorithm within this paper 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.

Keywords

• gait analysis
• step segmentation
• wearable sensors
• inertial measurement unit (IMU)