Gait Recognition Based on Tiny ML and IMU Sensors

TL;DR

This paper develops a low-power gait recognition system using Tiny ML and IMU sensors, capable of real-time activity classification with over 80% accuracy, suitable for energy-efficient wearable devices.

Contribution

It introduces a novel Tiny ML-based gait recognition system utilizing IMU sensors and edge AI, enabling real-time activity classification on microcontrollers.

Findings

Achieves over 80% accuracy in activity recognition

Demonstrates effective real-time classification on microcontroller

Enables anomaly detection for system robustness

Abstract

This project presents the development of a gait recognition system using Tiny Machine Learning (Tiny ML) and Inertial Measurement Unit (IMU) sensors. The system leverages the XIAO-nRF52840 Sense microcontroller and the LSM6DS3 IMU sensor to capture motion data, including acceleration and angular velocity, from four distinct activities: walking, stationary, going upstairs, and going downstairs. The data collected is processed through Edge Impulse, an edge AI platform, which enables the training of machine learning models that can be deployed directly onto the microcontroller for real-time activity classification.The data preprocessing step involves extracting relevant features from the raw sensor data using techniques such as sliding windows and data normalization, followed by training a Deep Neural Network (DNN) classifier for activity recognition. The model achieves over 80% accuracy on a test dataset, demonstrating its ability to classify the four activities effectively. Additionally, the platform enables anomaly detection, further enhancing the robustness of the system. The integration of Tiny ML ensures low-power operation, making it suitable for battery-powered or energy-harvesting devices.