Minimally Invasive Assessment of Mental Stress based on Wearable Wireless Physiological Sensors and Multivariate Biosignal Processing

Alessandro Busacca, Luca Faes, Riccardo Pernice, Mariolino De Cecco, Matteo Zanetti, Giandomenico Nollo

Risultato della ricerca: Conference contribution

2 Citazioni (Scopus)

Abstract

The development of connected health technologies for the continuous monitoring of the psychophysical state of individuals performing daily life activities requires the aggregation of non-intrusive sensors and the availability of methods and algorithms for extracting the relevant physiological information. The present study proposes an integrated approach for the objective assessment of mental stress which combines wirelessly connected low invasive biosensors with multivariate physiological time series analysis. In a group of 18 healthy subjects monitored in a relaxed resting state and during two experimental conditions inducing mental stress and sustained attention (respectively, mental arithmetic and serious game), we collected simultaneously multichannel EEG, one lead ECG, respiration and blood volume pulse. From these signals, synchronous physiological time series were extracted measuring the _, _, _, and _ EEG amplitudes, the heart period, the sampled respiratory activity and the pulse arrival time. For each condition, five minute windows of each of these seven time series were characterized with measures in the time domain (mean, standard deviation) and in the information domain (self entropy, measuring time series regularity). We show that the dynamical activity of the different physiological systems is affected in a different way by the alteration of the psychophysical state of the subjects induced by stress, and that the measures in the two domains can elicit complementary information about mental stress and sustained attention. These results advocate the feasibility of connected health technology for minimally invasive, automatic classifiers of different levels of mental stress in real life scenarios.
Lingua originaleEnglish
Titolo della pubblicazione ospiteIEEE EUROCON 2019 -18th International Conference on Smart Technologies
Pagine1-5
Numero di pagine5
Stato di pubblicazionePublished - 2019

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Signal Processing
  • Management Science and Operations Research
  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering
  • Computational Mathematics
  • Control and Optimization
  • Health Informatics

Fingerprint Entra nei temi di ricerca di 'Minimally Invasive Assessment of Mental Stress based on Wearable Wireless Physiological Sensors and Multivariate Biosignal Processing'. Insieme formano una fingerprint unica.

  • Cita questo

    Busacca, A., Faes, L., Pernice, R., De Cecco, M., Zanetti, M., & Nollo, G. (2019). Minimally Invasive Assessment of Mental Stress based on Wearable Wireless Physiological Sensors and Multivariate Biosignal Processing. In IEEE EUROCON 2019 -18th International Conference on Smart Technologies (pagg. 1-5)