Wearable Technologies for Mental Workload, Stress, and Emotional State Assessment during Working-Like Tasks: A Comparison with Laboratory Technologies

Abstract

The capability of monitoring user’s performance represents a crucial aspect to improve safety and efficiency of several human-related activities. Human errors are indeed among the major causes of work-related accidents. Assessing human factors (HFs) could prevent these accidents through specific neurophysiological signals’ evaluation but laboratory sensors require highly-specialized operators and imply a certain grade of invasiveness which could negatively interfere with the worker’s activity. On the contrary, consumer wearables are characterized by their ease of use and their comfortability, other than being cheaper compared to laboratory technologies. Therefore, wearable sensors could represent an ideal substitute for laboratory technologies for a real-time assessment of human performances in ecological settings. The present study aimed at assessing the reliability and capability of consumer wearable devices (i.e., Empatica E4 and Muse 2) in discriminating specific mental states compared to laboratory equipment. The electrooculographic (EOG), electrodermal activity (EDA) and photoplethysmographic (PPG) signals were acquired from a group of 17 volunteers who took part to the experimental protocol in which different working scenarios were simulated to induce different levels of mental workload, stress, and emotional state. The results demonstrated that the parameters computed by the consumer wearable and laboratory sensors were positively and significantly correlated and exhibited the same evidences in terms of mental states discrimination.

Tourist Experiences Recommender System Based on Emotion Recognition with Wearable Data

“…The proposed architecture is a reference for developing recommendation systems based on users’ emotional states in different contexts. Furthermore, this model allows adding wearable devices with more accuracy physiological sensors [ 11 , 13 , 31 , 60 ]. However, when cheap wearable devices become more popular in the market, manufacturers will probably include more accuracy sensors for monitoring biosignals and physical activity [ 28 , 29 , 34 , 35 , 36 , 37 ].…”

“…However, not all wearables have the same level of accuracy in getting the physiological signals [ 29 ]. There are devices ranges [ 10 , 26 ]: Expensive devices with high accuracy sensors, principally used for healthcare purposes, for instance, include the Emphatica E4 [ 11 , 13 , 28 , 31 ]. Medium range devices with precise sensors have more extensive use, targeted to high-performance athletes, mainly for fitness and sports, and these include, for example, Garmin and Microsoft Band [ 10 , 12 , 32 , 33 ].…”

Cross-Modal Sentiment Sensing with Visual-Augmented Representation and Diverse Decision Fusion

“…For markets, understanding the feelings and preferences of customers can contribute to personalized recommendations and marketing [ 13 ]. For individuals, recognizing and monitoring the personal psychology states are crucial for mental health and emotion management [ 14 ]. Conventional methods about sentiment analysis are based on representation learning to capture semantic and sentimental information.…”

MuseStudio: Brain Activity Data Management Library for Low-Cost EEG Devices

“…Due to the previously annotated increasing demand of EEG devices, the number of available devices is on the rise, and they have many different characteristics [3,10,11]. Moreover, these devices are not only present in the research environment [12][13][14][15][16][17], but also in the entertainment one [18][19][20]. A recent article [21] analyzed the number of electrodes included in devices depending on their design.…”