Classification of rheumatoid arthritis from hand motion capture data using machine learning

Fleischmann S, Holzwarth Correa V, Coppers B, Sadeghi M, Richer R, Kleyer A, Simon D, Bräunig J, Vossiek M, Schönau V, Schett G, Koelewijn A, Leyendecker S, Eskofier B, Liphardt AM (2024)

Publication Type: Conference contribution, Conference Contribution

Publication year: 2024

Event location: Heidelberg

Abstract

Intro
Classifying movement restrictions in patients with rheumatoid arthritis (RA) is challenging. Machine learning (ML) can identify patterns in the data that cannot be captured with statistical tools.
Aim
To test the feasibility of classifying RA patients and controls from hand motion capture data using automatic feature extraction (AFE) and ML and to evaluate if a larger feature set improves the performance.
Methods
24 RA patients (ACR/EULAR 2010 criteria) and 23 controls performed tipping and flexion of the hand [1], which was captured by an optoelectronic measurement system at 100 Hz with 29 reflective markers placed on the hand dorsum. We extracted the 3D trajectories, expressed relative to the central wrist marker and normalized to the hand length, of 24 markers on one hand (excluding thumb markers). After linearly interpolating missing values we filtered the data using a 4th-order Butterworth filter at a 6 Hz cut-off frequency and extracted a minimal and extensive feature set from the hand trajectories using the AFE package tsfresh [2]. We compared 4 classifiers in combination with scaling and dimensionality reduction (Fig 1). For every combination of algorithms, we trained a model using a stratified 10-fold nested cross-validation (CV). Hyperparameters were found from an inner five-fold CV on the respective train set using grid search. We report the accuracy of the best model averaged over the 10 folds.
Results
The best accuracy was 68.2% for flexion and 62.7% for tipping (Fig 2). Using the extensive instead of the minimal feature set led to no major change in accuracy.
Summary
The classification performance based on AFE was below the anticipated level of accuracy (>70%), likely due to the small dataset with limited data quality. We currently evaluate the approach on a more consistent, larger dataset and include expert features in addition to AFE.
References
[1] Phutane et al., Sensors, 21(4), 1208, 2021
[2] Christ et al., Neurocomputing, 307, 72-77, 2018

Authors with CRIS profile

Sophie Fleischmann Sonderforschungsbereich 1483/1 Empathokinästhetische Sensorik – Sensortechniken und Datenanalyseverfahren zur empathokinästhetischen Modellbildung und Zustandsbestimmung (EmpkinS) Birte Coppers Department of Medicine 3 – Rheumatology and Immunology Misha Sadeghi Sonderforschungsbereich 1483/1 Empathokinästhetische Sensorik – Sensortechniken und Datenanalyseverfahren zur empathokinästhetischen Modellbildung und Zustandsbestimmung (EmpkinS) Robert Richer Sonderforschungsbereich 1483/1 Empathokinästhetische Sensorik – Sensortechniken und Datenanalyseverfahren zur empathokinästhetischen Modellbildung und Zustandsbestimmung (EmpkinS) Arnd Kleyer Medizinische Fakultät David Simon Department of Medicine 3 – Rheumatology and Immunology Johanna Bräunig Sonderforschungsbereich 1483/1 Empathokinästhetische Sensorik – Sensortechniken und Datenanalyseverfahren zur empathokinästhetischen Modellbildung und Zustandsbestimmung (EmpkinS) Martin Vossiek Lehrstuhl für Hochfrequenztechnik (LHFT) Verena Schönau Department of Medicine 3 – Rheumatology and Immunology Georg Schett Lehrstuhl für Innere Medizin III Anne Koelewijn Juniorprofessur für Computational Movement Science (Stiftungsprofessur) Sigrid Leyendecker Institute of Applied Dynamics Björn Eskofier Machine Learning and Data Analytics Lab Anna-Maria Liphardt Department of Medicine 3 – Rheumatology and Immunology

Related research project(s)

Erforschung der posturalen Kontrolle basierend auf sensomotorisch erweiterten muskuloskelettalen Menschmodellen (SFB 1483 EmpkinS C03) Empatho-Kinaesthetic Sensor Technology (SFB 1483 EmpkinS) July 1, 2021 - June 30, 2025

How to cite

APA:

Fleischmann, S., Holzwarth Correa, V., Coppers, B., Sadeghi, M., Richer, R., Kleyer, A.,... Liphardt, A.-M. (2024). Classification of rheumatoid arthritis from hand motion capture data using machine learning. In Proceedings of the 13. Kongress der Deutschen Gesellschaft für Biomechanik (DGfB). Heidelberg.

MLA:

Fleischmann, Sophie, et al. "Classification of rheumatoid arthritis from hand motion capture data using machine learning." Proceedings of the 13. Kongress der Deutschen Gesellschaft für Biomechanik (DGfB), Heidelberg 2024.

BibTeX: Download