Validation of Affect-tag Affective and Cognitive Indicators

ACACIAS 1: The physiological and subjective impacts of high fidelity simulation of the breaking of bad news

STUDY OBJECTIVE

Breaking bad news (BN) is difficult and necessitates targeted training. To be effective, training may call for High Fidelity Simulation (HFS). This prospective study was conducted to objectively assess the impact of HFS as a tool conducive to the development of clinical competence in situations involving the delivery of bad news.

METHODS

This feasibility study was conducted from January to May 2021 and included students in medical oncology and digestive surgery. The subjective and objective impacts of HFS were evaluated by means of a self-administered questionnaire and a wristband, Affect-tag, which recorded several indicators: emotional power (EP), emotional density (DE) and cognitive load (CL) in students undergoing training.

RESULTS

Forty-six (46) students with a median age of 25 years (21-34 years) were included. While the participants were effectively and emotionally involved in the HFS training, they were not completely overwhelmed by their emotions, a possible occurrence in this type of program. After two training programs, the students presented with lower EP (P<0.001) and higher DE (P=0.005), while their CL remained stable (P=0.751). The information given in the self-administered questionnaires and the evaluations by outside professionals (actor, nurse, psychologist…) highlighted improved skills.

CONCLUSION

Taking into account the emotional parameters observed and the questionnaires collected, HFS can be considered as a suitable and effective tool in the breaking of bad news.

Towards Human Stress and Activity Recognition: A Review and a First Approach Based on Low-Cost Wearables

Detecting stress when performing physical activities is an interesting field that has received relatively little research interest to date. In this paper, we took a first step towards redressing this, through a comprehensive review and the design of a low-cost body area network (BAN) made of a set of wearables that allow physiological signals and human movements to be captured simultaneously. We used four different wearables: OpenBCI and three other open-hardware custom-made designs that communicate via bluetooth low energy (BLE) to an external computer—following the edge-computingconcept—hosting applications for data synchronization and storage. We obtained a large number of physiological signals (electroencephalography (EEG), electrocardiography (ECG), breathing rate (BR), electrodermal activity (EDA), and skin temperature (ST)) with which we analyzed internal states in general, but with a focus on stress. The findings show the reliability and feasibility of the proposed body area network (BAN) according to battery lifetime (greater than 15 h), packet loss rate (0% for our custom-made designs), and signal quality (signal-noise ratio (SNR) of 9.8 dB for the ECG circuit, and 61.6 dB for the EDA). Moreover, we conducted a preliminary experiment to gauge the main ECG features for stress detection during rest.