Human Metrics Explorer System for Multi-Device Physiological Measurements in Emotion Estimation

Synchronously measuring and analyzing multiple physiological signals is useful for estimating human emotion in more detail; however, it is difficult in practical environments because few systems can integrate various types of devices and be used easily. We developed a human metrics explorer (HuME) ™ system for realizing easy synchronous measurement and analysis of data from several devices. The purposes of this study are to confirm the accuracy of the trigger signal used for synchronization of the HuME system in a basic experiment and to demonstrate the feasibility of the use of the system through synchronous measurement and analysis of signals from multiple device types through a practical experiment of emotion estimation. In a basic experiment, we examined the time lag between the transmission of trigger signals from the transmitter and the recording of these signals on sensing devices placed at various positions. In a practical experiment concerning emotion estimation applications, we measured the physiological data of two participants during a board game using the developed wearable facial electromyogram and electrocardiogram devices, commercial electrodermal activity devices, and cameras. Consequently, the absolute maximum variation in the time lag was 2 ms. Additionally, we could measure and analyze data synchronously from the multiple device types using the HuME system and capture the physiological responses of the participants during the game. In conclusion, we confirmed that the trigger signal was accurate for the HuME system synchronization and demonstrated the feasibility of this system in the synchronization of multiple device types.

I. Introduction

Objective emotion estimation in daily life is useful and required for psychological research, marketing, and evaluating user experience. The internal states of humans, such as emotions, appear as changes in various physiological signals such as electrocardiogram (ECG), electrodermal activity (EDA), electroencephalography (EEG), and facial expressions [1]–[3]. It has been reported that emotions can be estimated more accurately by analyzing a combination of multiple physiological signals [4], [5]. Many studies have evaluated product prototypes and media contents in practical environments by measuring and analyzing multiple physiological signals [6]–[8].