Emotion recognition using a glasses-type wearable device via multi-channel facial responses
This work addresses unobtrusive emotion monitoring for daily life, with potential applications in healthcare, though it is incremental as it builds on existing wearable and sensor technologies.
The researchers tackled emotion recognition by developing a glasses-type wearable device that captures multi-channel facial responses, including physiological data and local facial expressions, achieving an accuracy of 78% with local expressions and increasing it by 10.1% with multi-channel data.
We present a glasses type wearable device to detect emotions from a human face in an unobtrusive manner. The device is designed to gather multi channel responses from the user face naturally and continuously while the user is wearing it. The multi channel responses include physiological responses of the facial muscles and organs based on electrodermal activity (EDA) and photoplethysmogram. We conducted experiments to determine the optimal positions of EDA sensors on the wearable device because EDA signal quality is very sensitive to the sensing position. In addition to the physiological data, the device can capture the image region representing local facial expressions around the left eye via a built in camera. In this study, we developed and validated an algorithm to recognize emotions using multi channel responses obtained from the device. The results show that the emotion recognition algorithm using only local facial expressions has an accuracy of 78 percent at classifying emotions. Using multi channel data, this accuracy was increased by 10.1 percent. This unobtrusive wearable system with facial multi channel responses is very useful for monitoring a user emotions in daily life, which has a huge potential for use in the healthcare industry.