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Using Lower Limb Wearable Sensors to Identify Gait Modalities: A Machine-Learning-Based Approach.

Liam David Hughes¹, Martin Bencsik¹, Maria Bisele¹

¹School of Science and Technology, Nottingham Trent University, Nottingham NG11 8NS, UK.

Sensors (Basel, Switzerland)

|November 25, 2023

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Real-world gait analysis using accelerometers can distinguish between walking, running, and other movements with high accuracy. The sacrum location provided the best results for this simple machine learning approach.

Area of Science:

Biomechanics
Wearable Technology
Machine Learning

Background:

Real-world gait analysis offers valuable insights for clinical assessments and interventions outside laboratory settings.
Individual accelerometers provide a feasible method for non-intrusive gait monitoring.

Purpose of the Study:

To evaluate a simple machine learning method for discriminating between three self-selected cyclical locomotion types using accelerometers.
To determine the accuracy of gait modality discrimination at different sensor attachment locations (sacrum, thighs, shanks).

Main Methods:

Thirty-five participants walked at self-selected speeds along a 10m walkway with triaxial accelerometers attached to the sacrum, thighs, and shanks.
Accelerometer data (3-second intervals) were transformed into 2D Fourier spectra.

Keywords:

accelerometer discriminant function analysis gait machine learning principal component analysis wearable sensors

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Principal Component Analysis (PCA) and Discriminant Function Analysis (DFA) were used for data reduction, feature selection, and classification.

Main Results:

The machine learning algorithm achieved high accuracy in discriminating gait modalities across all sensor locations.
Accuracy rates were 91% at the sacrum, 90% at the shanks, and 87% at the thighs.
The sacrum location demonstrated the highest accuracy for gait modality discrimination.

Conclusions:

Simple machine learning algorithms can effectively discriminate between different gait modalities using accelerometer data from common body locations.
The sacrum offers the most accurate sensor placement for this type of real-world gait analysis.
Future research should optimize data processing for sensor locations beneficial for specific applications, such as shank placement for prosthetic devices.