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Using a Split-belt Treadmill to Evaluate Generalization of Human Locomotor Adaptation

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Exploring parameter optimisation in machine learning algorithms for locomotor task discrimination using wearable

L D Hughes^1,2, M Bencsik¹, M Bisele³

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

Scientific Reports

|October 9, 2025

Summary

This summary is machine-generated.

Optimizing machine learning parameters like window length and sampling frequency significantly improves the accuracy of identifying human locomotion states from wearable sensor data.

Keywords:

Discriminant function analysis Locomotion Machine learning Optimisation Principal component analysis Wearable sensors

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Area of Science:

Biomechanics and Wearable Technology
Machine Learning in Healthcare

Background:

Accurate identification of locomotion states from wearable sensors is crucial for health monitoring and rehabilitation.
Selecting optimal algorithm parameters for machine learning models remains a significant challenge.

Purpose of the Study:

To systematically optimize key parameters for machine learning models used in classifying human locomotion.
To enhance the accuracy of distinguishing different locomotor tasks using wearable accelerometer data.

Main Methods:

Participants (N=35) wore accelerometers on the sacrum, thighs, and shanks.
Principal component and discriminant function analyses were applied to data from slow, normal, and fast walking tasks.
Key parameters optimized included window length, sampling frequency, temporal resolution, overlapping value, and normalization.

Main Results:

Unnormalized data with longer feature window lengths and decreasing temporal resolutions showed the highest discrimination quality.
Optimal discrimination was achieved with a sampling rate of 40 Hz and an overlapping value of 66%.
The sacrum was the best sensor location initially, but optimal parameter settings shifted this to the shanks.

Conclusions:

Specific parameter values were identified as optimal for accurate locomotion state identification.
Findings provide guidance for designing effective wearable devices and machine learning algorithms.
Results can inform practitioners and clinicians in selecting appropriate tools for research and clinical objectives.