Abstract
An advanced driver-assistance system is developed, in which the conventional steering wheel and brake pedal are replaced by a novel human-machine interface using surface electromyography (sEMG) electrodes attached to the driver’s muscles, and this paper is dedicated to braking control assistance. In the first part, how the sEMG signals are measured is presented, then the signals are analyzed in the frequency and time domain to determine the noise they contain, and signal processing using optimal linear Wiener filtering (finite impulse response) is proposed and compared with two other methods. In the second part, using deep learning for detecting whether the driver is braking the vehicle is discussed, where the authors present the steps of preparing the data sets, extracting time-series features, and training a long short-term memory network. Lastly, the potential of applying the proposed deep learning method online, i.e., while the vehicle is in use, is analyzed.
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References
Nacpil, E.J.C., Wang, Z., Zheng R., Kaizuka, T., Nakano, K.: Design and evaluation of a surface electromyography-controlled steering assistance interface. Sensors (2019)
Nacpil, E.J.C., Zheng R., Kaizuka, T., Nakano, K.: A Surface Electromyography Controlled Steering Assistance Interface. Journal of Intelligent and Connected Vehicles (2019)
Nacpil, E.J.C., Nakano, K.: Surface Electromyography-Controlled Automobile Steering Assistance. Sensors (2020)
Pandis, P., Prinold, J.A.I., Bull, A.M.J.: Shoulder muscle forces during driving: sudden steering can load the rotator cuff beyond its repair limits. Clin. Biotech (2015)
Wada, M., Kameda, F.: A Joystick car drive system with seating in a wheelchair. In: 18th IEEE International Symposium on Robot and Human Interactive Communication (2009)
Minato,T., Murata,Y., Suzuki A.: Proposal of Automobile Driving Interface Using Strain Sensor for the Disabled People. In: 2015 Tohoku-Section Joint Convention of Institutes of Electrical and Information Engineers (2015)
Perotto, A., Delagi E.F.: Anatomical Guide for the Electromyographer: The Limbs and Trunks. Charles C. Thomas Pub Ltd, 5th edn. (2011)
Wiener, N.: Extrapolation, Interpolation, and Smoothing of Stationary Time Series. Wiley, New York (1949)
Kolmogorov, A.N.: Stationary Sequences in Hilbert Space. Moscow Univ, Bull (1941) (in Russian)
Eddins, S.: Classify ECG Signals Using LSTM Networks (2018). https://blogs.mathworks.com/deep-learning/2018/08/06/classify-ecg-signals-using-lstm-networks/. Accessed 20 Dec 2020
Brownlee, J.: How to Scale Data for Long Short-term Memory Networks in Python (2017). https://machinelearningmastery.com/how-to-scale-data-for-long-short-term-memory-networks-in-python/. Accessed 20 Dec 2020
Menner, M., Neuner, L., Lünenburger, L., Zeilinger, M.N.: Using human ratings for feedback control: a supervised learning approach with application to rehabilitation robotics. IEEE Transactions on Robotics (2020)
Wang, Z., Yan, Z., Nakano, K.: Comfort-oriented haptic guidance steering via deep reinforcement learning for individualized lane keeping assist. In: IEEE International Conference on Systems, Man and Cybernetics (2019)
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Tran, G.Q.B., Wang, Z., Yusuke, K., Nakano, K. (2021). Surface Electromyography-Controlled Vehicle Braking Assistance System Using Deep Learning. In: Stanton, N. (eds) Advances in Human Aspects of Transportation. AHFE 2021. Lecture Notes in Networks and Systems, vol 270. Springer, Cham. https://doi.org/10.1007/978-3-030-80012-3_16
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DOI: https://doi.org/10.1007/978-3-030-80012-3_16
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