Driver drowsiness monitoring is one of the most demanded technologies for active prevention of severe road accidents. Electroencephalogram (EEG) and several peripheral signals have been suggested for the drowsiness monitoring. However, each type of signal has partial limitations in terms of either convenience or accuracy. Recent emerged concept of in-ear EEG raises expectations due to reduced obtrusiveness. It is yet unclear whether the in-ear EEG is effective enough for drowsiness detection in comparison with on-scalp EEG or peripheral signals. In this work, we evaluated performance of the in-ear EEG in drivers' alertness-drowsiness classification for the first time. Simultaneously, we also tested three peripheral signals including electrocardiogram (ECG), photoplethysmogram (PPG), and galvanic skin response (GSR) which have advantage in convenience of measurement. The classification analysis using the in-ear EEG resulted in high classification accuracy comparable to that of the individual on-scalp EEG channels. The ECG, PPG and GSR showed competitive performance but only when used together in pairwise combinations. Our results suggest that the in-ear EEG would be viable alternative to the single channel EEG or the individual peripheral signals for the drowsiness monitoring.
Photoplethysmography (PPG) of the foot sole could provide additional health-related information compared with traditional PPG of the finger or wrist. Previously, foot PPG required the procedural binding of a light-emitting diode (LED)-photodetector (PD) pair. We achieved PPG of the foot sole without binding any sensors to the foot while the participant stood in a natural standing position on the testing device. Foot PPG was performed using multiple LED-PD pairs to overcome motion artefacts caused by stabilization. We identified regions of the sole suitable for reliable sensor positioning with optimal LED-PD pairs on the basis of the estimated heart rate (HR) and signal quality index derived by dynamic time warping (wSQI). The first experiment included four participants with direct skin-to-sensor contact, and the results showed a mean HR estimation error of 0.01 beats/min and a wSQI of 0.909. The extended experiment with 53 participants, which involved including a gap between the skin and sensors to consider real-life applications, yielded a mean HR estimation error of 0.638 beats/min and a wSQI of 0.751. Based on the selection ratio of optimal LED-PD pairs, the best region of the sole for PPG was the midfoot, except the medial longitudinal arch. In conclusion, we confirmed that foot PPG using multiple LED-PD pairs is appropriate for HR evaluation and further applications.
We investigated the effects of a quality screening method on unconstrained measured signals, including electrocardiogram (ECG), photoplethysmogram (PPG), and ballistocardiogram (BCG) signals, in our collective chair system for smart healthcare. Such an investigation is necessary because unattached or unbound sensors have weaker connections to body parts than do conventional methods. Using the biosignal chair, the physiological signals collected during sessions included a virtual driving task, a physically powered wheelchair drive, and three types of body motions. The signal quality index was defined by the similarity between the observed signals and noise-free signals from the perspective of the cross-correlations of coefficients with appropriate individual templates. The goal of the index was to qualify signals without a reference signal to assess the practical use of the chair in daily life. As expected, motion artifacts have adverse effects on the stability of physiological signals. However, we were able to observe a supplementary relationship between sensors depending on each movement trait. Except for extreme movements, the signal quality and estimated heart rate (HR) remained within the range of criteria usable for status monitoring. By investigating the signal reliability, we were able to confirm the suitability of using the unconstrained biosignal chair to collect real-life measurements to improve safety and healthcare.
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