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Related Experiment Video

Updated: Jun 18, 2026

Low-Cost Gait Analysis for Behavioral Phenotyping of Mouse Models of Neuromuscular Disease

Low-Cost Gait Analysis for Behavioral Phenotyping of Mouse Models of Neuromuscular Disease

Published on: July 18, 2019

Gait Stride Length Estimation Using Embedded Machine Learning.

Joeri R Verbiest^1,2, Bruno Bonnechère^2,3, Wim Saeys⁴

¹Department of Sciences and Technology, Karel de Grote (KdG) University of Applied Sciences and Arts, 2660 Antwerp, Belgium.

Sensors (Basel, Switzerland)

|August 26, 2023

Summary

This summary is machine-generated.

This study developed embedded machine learning models for estimating gait stride length on microcontrollers. The int8 model achieved high accuracy with minimal memory, enabling on-device gait analysis.

Keywords:

IMU MCU embedded machine learning gait analysis gait stride length healthcare inertial measurement unit machine learning microcontroller neural network regression tinyML wearable sensors

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Area of Science:

Biomedical Engineering
Wearable Technology
Machine Learning

Background:

Traditional gait analysis relies on lab-based instrumentation.
Existing wearable gait assessment devices are resource-constrained, limiting on-device machine learning.
Embedded machine learning (tinyML) offers a solution for processing on microcontrollers.

Purpose of the Study:

To develop a machine learning model for gait stride length estimation.
To create a model deployable on resource-constrained microcontrollers.
To enable direct on-device gait analysis using wearable sensors.

Main Methods:

A dataset of 4467 gait strides from 15 healthy individuals was used.
A multilayer 1D convolutional neural network (CNN) was developed.
Both float32 and int8 precision models were created using MLOps tools.

Main Results:

The int8 model achieved a mean accuracy and precision of 0.07 ± 4.3 cm.
The int8 model required only 91.6 kB flash and 13.6 kB RAM.
Both models were successfully deployed on a Cortex-M4F microcontroller.

Conclusions:

Estimating gait stride length directly on a microcontroller is feasible.
Embedded machine learning (tinyML) has significant potential for wearable gait analysis devices.
This approach enhances the capabilities of resource-constrained wearable sensors.