Creating and sharing knowledge for telecommunications

EmotiphAI: a biocybernetic engine for real-time biosignals acquisition in a collective setting

Bota, P. ; Fléty, E. ; Silva, H. ; Fred, A. L. N.

Neural Computing and Applications Vol. 1, Nº 1, pp. 1 - 16, March, 2022.

ISSN (print): 0941-0643
ISSN (online): 1433-3058

Scimago Journal Ranking: 0,71 (in 2020)

Digital Object Identifier: 10.1007/s00521-022-07191-8

Abstract
In the latter years, we have been observing a growth in wearable technology for personal use. However, an analysis of the
state of the art for wearable technology shows that most devices perform data acquisition from individual subjects only,
relying on communication technologies with drawbacks that prevent their use in collective real-world scenarios (e.g. a
cinema, a theatre, and related use cases). When analysing the emotional response in groups, two types of emotions appear:
individual (influenced by the group) and group-based emotions (towards the group as an identity). To fill the existing gap,
we propose a biocybernetic engine for real-time data acquisition of multimodal physiological data in real-world scenarios.
Our system extends the state of the art with: (1) real-time data acquisition for the signals being acquired (20 devices at 25
Hz; 10 devices at 60 Hz); (2) creation of a standalone local infrastructure with end-user interface for monitoring the data
acquisition; (3) local and cloud-based data storage. We foresee that this platform could be the basis for the creation of large
databases in diverse real-world scenarios, namely health and wellbeing, marketing, art performances, and others. As a
result, this work will greatly contribute to simplify widespread biosignals data collection from unobtrusive wearables. To
evaluate the system, we report a comprehensive assessment based on a set of criteria for data quality analysis.