# Emotion recognition using a glasses-type wearable device via multi-channel facial responses

arXiv: https://arxiv.org/abs/1905.05360 · HTML: https://mlsanity.com/paper/1905.05360/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 May 2019.

## Built on by (most cited first, 3 of 3)

- [InMyFace: Inertial and Mechanomyography-Based Sensor Fusion for Wearable Facial Activity Recognition](https://mlsanity.com/paper/2302.04024/) (2023)
- [egoEMOTION: Egocentric Vision and Physiological Signals for Emotion and Personality Recognition in Real-World Tasks](https://mlsanity.com/paper/2510.22129/) (2025)
- [MeciFace: Mechanomyography and Inertial Fusion-based Glasses for Edge Real-Time Recognition of Facial and Eating Activities](https://mlsanity.com/paper/2306.13674/) (2023)

## Abstract (arXiv)

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.