Authors:
Gennaro Laudato
1
;
Giovanni Rosa
1
;
Simone Scalabrino
2
;
1
;
Jonathan Simeone
2
;
Francesco Picariello
3
;
Ioan Tudosa
3
;
Luca De Vito
3
;
Franco Boldi
4
;
Paolo Torchitti
4
;
Riccardo Ceccarelli
5
;
Fabrizio Picariello
6
;
Luca Torricelli
6
;
Aldo Lazich
7
and
Rocco Oliveto
2
;
1
Affiliations:
1
STAKE Lab, University of Molise, Pesche (IS), Italy
;
2
Datasound SRL, Pesche (IS), Italy
;
3
LESIM lab, University of Sannio, Italy
;
4
XEOS, Roncadelle (BS), Italy
;
5
Formula Medicine, Viareggio (LU), Italy
;
6
TexTech Technologies, Reggio Emilia (RE), Italy
;
7
Ministero della Difesa, Roma (RM), Italy
Keyword(s):
Wearable Devices, Machine Learning, Healthcare, Decision Support System.
Abstract:
In the last few years wearable devices are becoming always more important. Their usefulness mainly lies in the continuous monitoring of vital parameters and signals, such as electrocardiogram. However, such a monitoring results in an enormous amount of data which cannot be precisely analyzed manually. This recalls the need of approaches and tools for the automatic analysis of acquired data. In this paper we present MIPHAS, a software system devised to meet this need in a well-defined context: the monitoring of athletes during sport activities. MIPHAS is a system composed of several components: a smart t-shirt, an electronic component, a web application, a mobile APP and an advanced decision support system based on machine learning techniques. This latter is the core component of MIPHAS dedicated to the automatic detection of potential anomalies during the monitoring of vital parameters.