Using Multi-biosensing Signals for Prediction Sleeping Physiological Cognitive State in living Environment

• Main Authors: Lin,Jia Yi, 林佳儀
• Other Authors: Horng,Gwo-Jiun
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/ghvutz

碩士 === 南臺科技大學 === 資訊工程系 === 106 === Everyone has a physiological reaction to sleep. Many studies indicate that because people often sacrifice sleep, not only negative physiological and psychological effects, but also lead to abnormal cognitive behavior. However, current drowsiness-related research focuses on drowsy driving situations and uses brainwave signals and facial imaging detection. There is not enough research on physiological information to predict drowsiness. Drowsiness driving is not only the key reason for accidents. When people are at the work environment and daily activities, the chance of accidents will increase because of lack of sleep. In order to let people remind their mental condition according to drowsiness prediction results, and achieve personal sleep management. In this paper, we plan five experiments to make people remind their mental condition according to drowsiness predictions and achieve personal sleep management. This experiment included brainwave, eye tracking, heart rate and skin resistance sensing data. Then discuss the differences and analysis of these physiological data. And using the Artificial Neural Network and Support Vector Machine learning algorithm to classify drowsiness and predict its drowsiness status.