Machine learning-based distinction of left and right foot contacts in lower back inertial sensor gait data

Ullrich M, Küderle A, Reggi L, Cereatti A, Eskofier B, Kluge F (2021)

Publication Type: Conference contribution, Conference Contribution

Publication year: 2021

Original Authors: Martin Ullrich, Arne Kuderle, Luca Reggi, Andrea Cereatti, Bjoern M. Eskofier, Felix Kluge

Publisher: IEEE

DOI: 10.1109/EMBC46164.2021.9630653

Abstract

Digital gait measures derived from wearable inertial sensors have been shown to support the treatment of patients with motor impairments. From a technical perspective, the detection of left and right initial foot contacts (ICs) is essential for the computation of stride-by-stride outcome measures including gait asymmetry. However, in a majority of studies only one sensor close to the center of mass is used, complicating the assignment of detected ICs to the respective foot. Therefore, we developed an algorithm including supervised machine learning (ML) models for the robust classification of left and right ICs using multiple features from the gyroscope located at the lower back. The approach was tested on a data set including 40 participants (ten healthy controls, ten hemiparetic, ten Parkinson’s disease, and ten Huntington’s disease patients) and reached an accuracy of 96.3% for the overall data set and up to 100.0% for the Parkinson’s sub data set. These results were compared to a state-of-the-art algorithm. The ML approaches outperformed this traditional algorithm in all subgroups. Our study contributes to an improved classification of left and right ICs in inertial sensor signals recorded at the lower back and thus enables a reliable computation of clinically relevant mobility measures.

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Involved external institutions

Politecnico di Torino Italy (IT) University of Bologna / Università di Bologna Italy (IT)

How to cite

APA:

Ullrich, M., Küderle, A., Reggi, L., Cereatti, A., Eskofier, B., & Kluge, F. (2021). Machine learning-based distinction of left and right foot contacts in lower back inertial sensor gait data. In Proceedings of the 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE.

MLA:

Ullrich, Martin, et al. "Machine learning-based distinction of left and right foot contacts in lower back inertial sensor gait data." Proceedings of the 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) IEEE, 2021.

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