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    TinyML-Based Fall Detection for Connected Personal Mobility Vehicles

    Ramon Sanchez-Iborra1, Luis Bernal-Escobedo2, Jose Santa3,*, Antonio Skarmeta2

    CMC-Computers, Materials & Continua, Vol.71, No.2, pp. 3869-3885, 2022, DOI:10.32604/cmc.2022.022610

    Abstract A new wave of electric vehicles for personal mobility is currently crowding public spaces. They offer a sustainable and efficient way of getting around in urban environments, however, these devices bring additional safety issues, including serious accidents for riders. Thereby, taking advantage of a connected personal mobility vehicle, we present a novel on-device Machine Learning (ML)-based fall detection system that analyzes data captured from a range of sensors integrated on an on-board unit (OBU) prototype. Given the typical processing limitations of these elements, we exploit the potential of the TinyML paradigm, which enables embedding powerful ML algorithms in constrained units.… More >

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