Gait event prediction using surface electromyography in Parkinsonian patients
Autor(in); Institution
Haufe, Stefan; 8.4, Mathematische Modellierung und Datenanalyse, PTB-Berlin; Uncertainty, Inverse Modeling and Machine Learning Group, Technical University of Berlin, GERMANY; Berlin Center for Advanced Neuroimaging, Charité–Universitätsmedizin Berlin, Berlin, GERMANY
Isaias, Ioannis U.; Department of Neurology, University Hospital Würzburg and Julius-Maximilians-Universität Würzburg, GERMANY; Centro Parkinson, ASST G. Pini-CTO, Milano, ITALY
Pellergrini, Franziska; Berlin Center for Advanced Neuroimaging, Charité-Universitätsmedizin Berlin, GERMANY; Bernstein Center for Computational Neuroscience Berlin, GERMANY
Palmisano, Chiara; Department of Neurology, University Hospital Würzburg and Julius-Maximilians-Universität Würzburg, GERMANY
Gait disturbances are common manifestations of Parkinson’s disease (PD), with unmet therapeutic needs. Inertial measurement units (IMUs) are capable of monitoring gait, but they lack neurophysiological information that may be crucial for studying gait disturbances in these patients. Here, we present a machine learning approach to approximate IMU angular velocity profiles and subsequently gait events using electromyographic (EMG) channels during overground walking in patients with PD. We recorded six parkinsonian patients while they walked for at least three minutes. Patient-agnostic regression models were trained on temporally embedded EMG time series of different combinations of up to five leg muscles bilaterally (i.e., tibialis anterior, soleus, gastrocnemius medialis, gastrocnemius lateralis, and vastus lateralis). Gait events could be detected with high temporal precision (median displacement of <50 ms), low numbers of missed events (<2%), and next to no false-positive event detections (<0.1%). Swing and stance phases could thus be determined with high fidelity (median F1-score of ~0.9). Interestingly, the best performance was obtained using as few as two EMG probes placed on the left and right vastus lateralis. Our results demonstrate the practical utility of the proposed EMG-based system for gait event prediction, which allows the simultaneous acquisition of an electromyographic signal to be performed. This gait analysis approach has the potential to make additional measurement devices such as IMUs and force plates less essential, thereby reducing financial and preparation overheads and discomfort factors in gait studies.
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Open Access Gold
Rechteinformation
CC BY 4.0 ; Creative Commons Attribution 4.0 License
Themenbereich der Metrologie
Metrologie in der Medizin
Zitierung
Haufe, S., Isaias, I. U., Pellergrini, F., & Palmisano, C. (2023). Gait event prediction using surface electromyography in Parkinsonian patients. Bioengineering, 10(2), 1–16. https://doi.org/110.3390/bioengineering10020212
