Measuring Arousal and Emotion in Healthcare Employees Using Novel Devices

Emma Fortune

Yaqoub Yusuf

Renaldo Blocker

A positive working environment is fundamental for reducing employee turnover, and increasing engagement and productivity. Technologies such as wearable sensors and video analysis of facial expressions can be used to objectively measure the emotional responses of employees in real-time in the workplace. The study aims were to perform a preliminary investigation into the validities of a wearable galvanic skin response (GSR) device to detect emotional arousal responses and video-based facial expression analysis (FEA) to measure emotional valence in desk-based healthcare employees in response to their own self-chosen personal pictures.

Six office employees were presented with their own pictures (two positive, two negative) in a randomized order at one time point during their regular work shifts while wearing GSR sensors on the middle and index fingers of their non-dominant hand with their face in view of a webcam. The number of peaks per minute was estimated from the GSR data and used as the arousal metric. FEA was performed on the video data to determine the emotional valence for each stimulus. Russell’s circumplex model of affect was used to determine participant’s emotional responses to the personal pictures. The GSR device detected arousal responses to the stimuli, with agreement, precision, and recall values all >96%. Video-based FEA classified emotional valence with agreement, precision, and recall values <57%. Similarly, the circumplex model determined emotional affect with poor validity due to the poor validity of the FEA-derived valence metric. The study results suggest that the GSR device is capable of detecting emotional arousal for both positive and negative emotions in real-time in the workplace.

Video-based FEA, and GSR data were simultaneously collected through the iMotions biometric research platform version 7 (iMotions, Boston, MA) as participants were presented with randomized stimuli.

This publication uses Facial Expression Analysis and GSR which is fully integrated into iMotions Lab

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