Research Article
SHelmet: An Intelligent Self-sustaining Multi Sensors Smart Helmet for Bikers
@INPROCEEDINGS{10.1007/978-3-319-61563-9_5, author={Michele Magno and Angelo D’Aloia and Tommaso Polonelli and Lorenzo Spadaro and Luca Benini}, title={SHelmet: An Intelligent Self-sustaining Multi Sensors Smart Helmet for Bikers}, proceedings={Sensor Systems and Software. 7th International Conference, S-Cube 2016, Sophia Antipolis, Nice, France, December 1-2, 2016, Revised Selected Papers}, proceedings_a={S-CUBE}, year={2017}, month={7}, keywords={Wearable device Sensors network Energy harvesting Power management}, doi={10.1007/978-3-319-61563-9_5} }- Michele Magno
Angelo D’Aloia
Tommaso Polonelli
Lorenzo Spadaro
Luca Benini
Year: 2017
SHelmet: An Intelligent Self-sustaining Multi Sensors Smart Helmet for Bikers
S-CUBE
Springer
DOI: 10.1007/978-3-319-61563-9_5
Abstract
This paper presents the design of a wearable system to transform a helmet into a smart, multi-sensor connected helmet (SHelmet) to improve motorcycle safety. Low power design and self-sustainability are the key for the usability of our helmet, to avoid frequent battery recharges and dangerous power losses. Hidden in the helmet structure, the designed system is equipped with a dense sensor network including accelerometer, temperature, light, and alcohol gas level, in addition, a Bluetooth low energy module interfaces the device with an on-vehicle IR camera, and eventually the user’s smart phone. To keep the driver focused, the user interface consists of a small non-invasive display combined with a speech recognition system. System architecture is optimized for aggressive power management, featuring an ultra-low power wake-up radio, and fine-grained software-controlled shutdown of all sensing, communication and computing sub-systems. Finally, a multi-source energy harvesting module (solar and kinetic) performs high-efficiency power recovery, improving battery management and achieving self-sustainability. SHelmet supports rich context awareness applications; breath alcohol control; real time vehicle data; sleep and fall detection; data display. Experimental results show that is possible achieve self-sustainability and demonstrate functionality of the developed node.
