Authors:
Finn Siegel
1
;
Christian Buj
1
;
Ricarda Merfort
2
;
Andreas Hein
1
and
Frerk Aschwege
1
Affiliations:
1
OFFIS e.V.- Institute for Information Technology, Escherweg 2, Oldenburg, Germany
;
2
Universitätsklinikum Aachen, Aachen, Germany
Keyword(s):
Electromyography, Neural Network, Deep Learning, Rehabilitation, Intramedullary Nailing, Femur Shaft Fracture, Foot Progression Angle.
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.
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