Estimation of Office Worker’s Emotion Types Using Two-dimensional Model Consisted of Biometric Signals

Abstract

A model that estimates emotion types from bio-signals obtained by wearable monitoring systems was developed. A total of 470 events of strongly conscious emotions (happy, relaxed, sad, and angry) during work hours were recorded in 11 office workers under a total of 911 days of continuous monitoring of pulse rate, pulse rate variability (PRV), skin temperature, body motion, and conversation time with a bracelet-shape wearable sensor. When the four emotion types were developed on the two-dimensional plane consisted of valence and arousal axes, the former axis was composed of the coefficient of variation of high-frequency PRV amplitude and skin temperature, while the latter axis was composed of the frequency variability of PRV respiratory peak and conversation time. Among office workers, two emotional coordinates comprising the Russel's Circumplex model may be estimated by bio-signals obtained by a wearable watch device.

Emodiversity evaluation of remote workers through health monitoring based on intra-day emotion sampling

“…Notably, strong emotional events were less frequently collected through voluntary responses that were not triggered by smartphone notifications. In a previous study that collected strong emotional experiences ( 42 ), a total of 954 experiences from 911 days × 11 subjects were collected, suggesting that strong emotions can be collected at a rate of 0.095 [times/days × people]. Compared to this rate, our rate of 0.038 (=9 [times] over 14 [days] × 17 [participants]) [times/days × people] was not extremely low.…”

“…This biased demographic characteristic may limit the generalizability of our findings to the broader population of remote workers. Regarding this limitation, our sample size was relatively small compared with previous studies, although similar sample sizes were sometimes reported in pilot studies ( 16 , 19 , 42 , 68 , 69 ). To enhance the generalizability of our findings, future studies future studies should aim for larger sample sizes that encompass diverse participants.…”

“…To realize such long-term monitoring, alternative low-burden methods of capturing emotional experiences will be required. One possible solution is the use of emotion recognition technology based on physiological sensor data obtained through ambulatory sensing ( 11 , 37 , 42 ). Since emotions are influenced by the autonomic nervous system ( 36 , 38 ), emotion recognition based on physiological data has shown success in laboratory settings but is currently limited in real-life settings ( 11 , 37 , 42 ).…”

“…One possible solution is the use of emotion recognition technology based on physiological sensor data obtained through ambulatory sensing ( 11 , 37 , 42 ). Since emotions are influenced by the autonomic nervous system ( 36 , 38 ), emotion recognition based on physiological data has shown success in laboratory settings but is currently limited in real-life settings ( 11 , 37 , 42 ). Future improvements in emotion recognition in the wild will enable automatic emotion measurement, reducing the burden of using emodiversity monitoring in real life.…”

“…If we can randomly sample self-reports within a day, we can evaluate participants’ emotional conditions without depending on specific contexts or situations in their daily lives. Considering previous ESM study protocols ( 12 , 39 , 41 , 42 ), participants were randomly prompted to respond to six ESM event notifications per day from 8:00 am to 9:30 pm through the experience sampling support system (exkuma, Japan Experience Sampling Method Association, Tokyo, Japan). Notifications were sent to participants’ smartphone apps that are popular in Japan (LINE, LINE Corp. Tokyo, Japan).…”