Evaluating the Viability of Neural Networks for Analysing Electromyography Data in Home Rehabilitation: Estimating Foot Progression Angle

Finn Siegel, Christian Buj, Ricarda Merfort, Andreas Hein, Frerk Aschwege

2024

Abstract

Intramedullary (IM) nailing is a widely accepted treatment for femoral shaft fractures due to its good healing rate and rapid return to full weight bearing. However, a significant number of patients experience impairments years after treatment. One possible cause is a malrotation of the femur, resulting in altered foot progression angles (FPAs), which can lead to changes in gait or persistent pain. To gain a better understanding of compensation mechanisms and improve rehabilitation strategies, a continuous surface electromyography (EMG) measurement system worn on vastus lateralis (VL) and vastus medialis (VM) is proposed. To test the feasibility of this approach, a study is conducted with healthy participants (N=10) simulating different FPA. The EMG signal was recorded and analysed using a convolutional neural network (CNN). The feasibility study showed promising results, as the CNN could on average achieve a validation accuracy of 74% in classifying FPAs as normal, inward (-15°), or outward (+15°). These results show the potential of using EMG measurements from VL and VM to monitor changes in FPA during rehabilitation. This approach offers the opportunity to increase our understanding of compensatory mechanisms and improve rehabilitation outcomes following malrotation caused by IM nailing.

Paper Citation

in Harvard Style

Siegel F., Buj C., Merfort R., Hein A. and Aschwege F. (2024). Evaluating the Viability of Neural Networks for Analysing Electromyography Data in Home Rehabilitation: Estimating Foot Progression Angle. In Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: HEALTHINF; ISBN 978-989-758-688-0, SciTePress, pages 132-141. DOI: 10.5220/0012385100003657

in Bibtex Style

@conference{healthinf24,
author={Finn Siegel and Christian Buj and Ricarda Merfort and Andreas Hein and Frerk Aschwege},
title={Evaluating the Viability of Neural Networks for Analysing Electromyography Data in Home Rehabilitation: Estimating Foot Progression Angle},
booktitle={Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: HEALTHINF},
year={2024},
pages={132-141},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012385100003657},
isbn={978-989-758-688-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: HEALTHINF
TI - Evaluating the Viability of Neural Networks for Analysing Electromyography Data in Home Rehabilitation: Estimating Foot Progression Angle
SN - 978-989-758-688-0
AU - Siegel F.
AU - Buj C.
AU - Merfort R.
AU - Hein A.
AU - Aschwege F.
PY - 2024
SP - 132
EP - 141
DO - 10.5220/0012385100003657
PB - SciTePress