High-Knee-Flexion Posture Recognition Using Multi-Dimensional Dynamic Time Warping on Inertial Sensor Data

Laudanski AF, Küderle A, Kluge F, Eskofier B, Acker SM (2025)

Publication Type: Journal article

Publication year: 2025

Journal

Sensors MDPI

Book Volume: 25

Article Number: 1083

Journal Issue: 4

DOI: 10.3390/s25041083

Abstract

Relating continuously collected inertial data to the activities or postures performed by the sensor wearer requires pattern recognition or machine-learning-based algorithms, accounting for the temporal and scale variability present in human movements. The objective of this study was to develop a sensor-based framework for the detection and measurement of high-flexion postures frequently adopted in occupational settings. IMU-based joint angle estimates for the ankle, knee, and hip were time and scale normalized prior to being input to a multi-dimensional Dynamic Time Warping (mDTW) distance-based Nearest Neighbour algorithm for the identification of twelve postures. Data from 50 participants were divided to develop and evaluate the mDTW model. Overall accuracies of 82.3% and 55.6% were reached when classifying movements from the testing and validation datasets, respectively, which increased to 86% and 74.6% when adjusting for imbalances between classification groups. The highest misclassification rates occurred between flatfoot squatting, heels-up squatting, and stooping, while the model was incapable of identifying sequences of walking based on a single stride template. The developed mDTW model proved robust in identifying high-flexion postures performed by participants both included and precluded from algorithm development, indicating its strong potential for the quantitative measurement of postural adoption in real-world settings.

Authors with CRIS profile

Arne Küderle Machine Learning and Data Analytics Lab Felix Kluge Machine Learning and Data Analytics Lab Björn Eskofier Machine Learning and Data Analytics Lab

Involved external institutions

University of Waterloo (UW)

Canada (CA)

How to cite

APA:

Laudanski, A.F., Küderle, A., Kluge, F., Eskofier, B., & Acker, S.M. (2025). High-Knee-Flexion Posture Recognition Using Multi-Dimensional Dynamic Time Warping on Inertial Sensor Data. Sensors, 25(4). https://doi.org/10.3390/s25041083

MLA:

Laudanski, Annemarie F., et al. "High-Knee-Flexion Posture Recognition Using Multi-Dimensional Dynamic Time Warping on Inertial Sensor Data." Sensors 25.4 (2025).

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