TinyML-Based Fall Detection for Connected Personal Mobility Vehicles

作     者：Ramon Sanchez-Iborra Luis Bernal-Escobedo Jose Santa Antonio Skarmeta 

作者机构：University Center of DefenseGeneral Air Force AcademySan Javier30720Spain University of MurciaMurcia30100Spain Technical University of CartagenaCartagena30202Spain 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2022年第71卷第5期

页      面：3869-3885页

核心收录：

学科分类：04[教育学] 0837[工学-安全科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This work has been supported by the Spanish Ministry of Science,Innovation and Universities,under the Ramon y Cajal Program(ref.RYC-2017-23823) the projects ONOFRE 3(ref.PID2020-112675RB)and Go2Edge(ref.RED2018-102585-T) by the European Commission,under the 5G-MOBIX(ref.825496)project by the Spanish Ministry for the Ecological Transition and the Demographic Challenge,under the MECANO project(ref.PGE-MOVES-SING-2019-000104). 

主　　题：Personal mobility machine learning on-board unit C-ITS IoT 

摘      要：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.