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.
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This work was supported by Texas Instruments during the TI Contest Europe 2016 and ETH Zürich.
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Magno, M., D’Aloia, A., Polonelli, T., Spadaro, L., Benini, L. (2017). SHelmet: An Intelligent Self-sustaining Multi Sensors Smart Helmet for Bikers. In: Magno, M., Ferrero, F., Bilas, V. (eds) Sensor Systems and Software. S-CUBE 2016. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 205. Springer, Cham. https://doi.org/10.1007/978-3-319-61563-9_5
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