Abstract
Recent research has developed surface electromyography (sEMG)-based interfaces to provide steering assistance to drivers with physical disabilities. Advances in artificial intelligence have produced machine learning methods and datasets that could be employed by these interfaces to accurately recognize steering commands resulting from the muscle activity of drivers. The current study investigates an armband interface that employs linear discriminant analysis (LDA) classification of forearm sEMG signal features to control the steering of a driving simulator. Using previously acquired sEMG signals from the wrist movements of 44 test subjects who contributed to the open access dataset, putEMG, the proposed interface had respective precision values of 94% and 96% for flexion and extension, whereas the average total signal processing time was 0.254 s/gesture across all test subjects. U-turns performed with the driving simulator demonstrated a maximum average vehicle lateral error of 0.378 ± 0.200 m.
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Nacpil, E.J., Nakano, K. (2021). Driving Simulator Validation of Machine Learning Classification for a Surface Electromyography-Based Steering Assistance Interface. In: Cassenti, D., Scataglini, S., Rajulu, S., Wright, J. (eds) Advances in Simulation and Digital Human Modeling. AHFE 2020. Advances in Intelligent Systems and Computing, vol 1206. Springer, Cham. https://doi.org/10.1007/978-3-030-51064-0_19
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