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. We have generated and publicly released a large dataset, including real riding measurements and realistically simulated falling events, which has been employed to produce different TinyML models. The attained results show the good operation of the system to detect falls efficiently using embedded OBUs. The considered algorithms have been successfully tested on mass-market low-power units, implying reduced energy consumption, flash footprints and running times, enabling new possibilities for this kind of vehicles.
Highlights
The arrival of eco-efficient mobility solutions is a reality in both dense urban areas and city outskirts [1]
Both processing and memory capacities should be carefully saved in this case. This is the reason why hardware embedded in these units and application/system software should be designed to maintain a low operation profile. This is an issue for artificial intelligent algorithms needed by smart automatic fall detection (AFD) systems, which are based on complex Machine Learning (ML) techniques requiring intensive data processing
Recent access restrictions to the city center in Europe, such as the cases of London, Paris or Madrid, for regular combustion vehicles, lead to the adoption of sustainable vehicles that complement regular bicycles. Devices such as electric scooters and bikes, segways, etc. are being employed for outdoor recreational activities. As it has been done for regular vehicles, the attachment of on-board unit (OBU) to personal mobility vehicles will allow their integration within the Cooperative-Intelligent Transportation Systems (C-ITS) ecosystem
Summary
The arrival of eco-efficient mobility solutions is a reality in both dense urban areas and city outskirts [1]. This is the reason why hardware embedded in these units and application/system software should be designed to maintain a low operation profile This is an issue for artificial intelligent algorithms needed by smart AFD systems, which are based on complex Machine Learning (ML) techniques requiring intensive data processing. We propose an AFD mechanism to be embedded in an energy-constrained OBU To this end, firstly, a large dataset has been generated, which includes data collected from real e-scooter rides, including provoked fall events. A large dataset has been generated, which includes data collected from real e-scooter rides, including provoked fall events This dataset has been employed for training a set of ML algorithms, which have been integrated within an embedded OBU prototype following the TinyML paradigm for enabling on-device inference.
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