# Asynchronous Averaging of Gait Cycles for Classification of Gait and Device Modes

## TL;DR

This paper introduces an innovative method for extracting gait signatures from IMU data to classify gait and device modes with high accuracy, using precise segmentation, resampling, and Fourier analysis.

## Contribution

It presents a novel approach combining segmentation, resampling, and Fourier expansion to create robust gait signatures for classification.

## Key findings

- High classification accuracy for gait and device modes
- Effective gait signature extraction from IMU data
- Robustness across multiple subjects and modes

## Abstract

An approach for computing unique gait signature using measurements collected from body-worn inertial measurement units (IMUs) is proposed. The gait signature represents one full cycle of the human gait, and is suitable for off-line or on-line classification of the gait mode. The signature can also be used to jointly classify the gait mode and the device mode. The device mode identifies how the IMU-equipped device is being carried by the user. The method is based on precise segmentation and resampling of the measured IMU signal, as an initial step, further tuned by minimizing the variability of the obtained signature within each gait cycle. Finally, a Fourier series expansion of the gait signature is introduced which provides a low-dimensional feature vector well suited for classification purposes. The proposed method is evaluated on a large dataset involving several subjects, each one containing two different gait modes and four different device modes. The gait signatures enable a high classification rate for each step cycle.