The presence of stimuli and the consequent reactions undoubtedly reflect in experience-related changes of physiological parameters, which can be monitored by wearable devices. Generally, reactions related to the sympathetic nervous system activity are assessed through heart rate variability analysis. However, the exploitation of multimodal physiological signals provides a broader fingerprint. This study aims to identify the elicitation of acoustic stimulation through a wearable device; physiological signals, including electrodermal activity and skin temperature, were measured on a test population wearing a wrist-worn medical device. Eight machine learning algorithms were evaluated in a binary classification (presence/absence of stimuli), using 22 meaningful metrics from the collected data. The experimental results showed that Linear Regression (LR) algorithm, followed by Support Vector Machine (SVM), performed satisfactorily across all the evaluation metrics, achieving 75.00% and 72.62% of accuracy rate, respectively. Finally, the trained LR and SVM algorithms have been validated on a publicly available dataset (WESAD).
Measurement of multimodal physiological signals for stimulation detection by wearable devices
Cosoli G.
;Scalise L.;
2021-01-01
Abstract
The presence of stimuli and the consequent reactions undoubtedly reflect in experience-related changes of physiological parameters, which can be monitored by wearable devices. Generally, reactions related to the sympathetic nervous system activity are assessed through heart rate variability analysis. However, the exploitation of multimodal physiological signals provides a broader fingerprint. This study aims to identify the elicitation of acoustic stimulation through a wearable device; physiological signals, including electrodermal activity and skin temperature, were measured on a test population wearing a wrist-worn medical device. Eight machine learning algorithms were evaluated in a binary classification (presence/absence of stimuli), using 22 meaningful metrics from the collected data. The experimental results showed that Linear Regression (LR) algorithm, followed by Support Vector Machine (SVM), performed satisfactorily across all the evaluation metrics, achieving 75.00% and 72.62% of accuracy rate, respectively. Finally, the trained LR and SVM algorithms have been validated on a publicly available dataset (WESAD).I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
