Drowsiness is a cause of accidents in industrial and mining activities. A considerable amount of effort has been put into the detection of drowsiness, and since then it has been integrated into a large variety of wearable systems. Nevertheless, the technology still suffers from high intrusiveness, short battery life and lack of generality. An opportunity to address these shortcomings arises from the use of physiological and behavioral features for bio-signals like EEG and IMU sensors. In this work, we propose an energy-efficient wearable platform for drowsiness detection. Our platform features a minimally invasive setup, based on dry EEG sensors to acquire neural data, and Mr. Wolf, an 8-core ultra-low-power digital platform. The system has been validated on three test subjects, achieving detection accuracy of 83%, using a Nearest Centroid Classifier, modeled with a semi-supervised algorithm from previously collected data. This work further extends the capabilities of our previous system, providing a more sophisticated classification mechanism that includes real-time and onboard sensor fusion processing while running into a highly efficient and unobtrusive hardware platform, outperforming the current State of the Art (SoA) in terms of wearability and battery lifetime.
Ultra Low-Power Drowsiness Detection System with BioWolf / Kartsch, V.; Benatti, S.; Guermandi, M.; Montagna, F.; Benini, L.. - 2019-:(2019), pp. 1187-1190. (Intervento presentato al convegno 9th International IEEE EMBS Conference on Neural Engineering, NER 2019 tenutosi a The Hilton Union Square, usa nel 2019) [10.1109/NER.2019.8717070].
Ultra Low-Power Drowsiness Detection System with BioWolf
Benatti S.;
2019
Abstract
Drowsiness is a cause of accidents in industrial and mining activities. A considerable amount of effort has been put into the detection of drowsiness, and since then it has been integrated into a large variety of wearable systems. Nevertheless, the technology still suffers from high intrusiveness, short battery life and lack of generality. An opportunity to address these shortcomings arises from the use of physiological and behavioral features for bio-signals like EEG and IMU sensors. In this work, we propose an energy-efficient wearable platform for drowsiness detection. Our platform features a minimally invasive setup, based on dry EEG sensors to acquire neural data, and Mr. Wolf, an 8-core ultra-low-power digital platform. The system has been validated on three test subjects, achieving detection accuracy of 83%, using a Nearest Centroid Classifier, modeled with a semi-supervised algorithm from previously collected data. This work further extends the capabilities of our previous system, providing a more sophisticated classification mechanism that includes real-time and onboard sensor fusion processing while running into a highly efficient and unobtrusive hardware platform, outperforming the current State of the Art (SoA) in terms of wearability and battery lifetime.
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