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TinyML-Based Fall Detection for Connected personal mobility Vehicles

Computers, Materials & Continua 2022年第5期71卷 3869-3885页

作者： Ramon Sanchez-Iborra Luis Bernal-Escobedo Jose Santa Antonio Skarmeta University Center of Defense General Air Force AcademySan Javier30720Spain University of Murcia Murcia30100Spain Technical University of Cartagena Cartagena30202Spain

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.

关键词： personal mobility machine learning on-board unit C-ITS IoT

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Machine Learning-Based Radio Access Technology Selection in the Internet of Moving Things

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China Communications 2021年第7期18卷 13-24页

作者： Ramon Sanchez-Iborra Luis Bernal-Escobedo Jose Santa Dept.Engineering and Applied Techniques University Centre of Defence at the Spanish Air Force Academy30729 San JavierSpain Dept.Information and Communication Engineering University of Murcia30100 MurciaSpain Dept.of Electronics Computing Technology and ProjectsTechnical University of Cartagena30202 CartagenaSpain

The Internet of Moving Things(IoMT)takes a step further with respect to traditional static IoT deployments.In this line,the integration of new eco-friendly mobility devices such as scooters or bicycles within the Cooperative-Intelligent Transportation Systems(C-ITS)and smart city ecosystems is crucial to provide novel services.To this end,a range of communication technologies is available,such as cellular,vehicular WiFi or Low-Power Wide-Area Network(LPWAN);however,none of them can fully cover energy consumption and Quality of Service(QoS)requirements.Thus,we propose a Decision Support System(DSS),based on supervised Machine Learning(ML)classification,for selecting the most adequate transmission interface to send a certain message in a multi-Radio Access Technology(RAT)set up.Different ML algorithms have been explored taking into account computing and energy constraints of IoMT enddevices and traffic type.Besides,a real implementation of a decision tree-based DSS for micro-controller units is presented and evaluated.The attained results demonstrate the validity of the proposal,saving energy in communication tasks as well as satisfying QoS requirements of certain urgent messages.The footprint of the real implementation on an Arduino Uno is 444 bytes and it can be executed in around 50µs.

关键词： internet of moving things multi-RAT CITS classification personal mobility

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