Title
A deep learning approach for pressure ulcer prevention using wearable computing.
Abstract
In recent years, statistics have confirmed that the number of elderly people is increasing. Aging always has a strong impact on the health of a human being; from a biological of point view, this process usually leads to several types of diseases mainly due to the impairment of the organism. In such a context, healthcare plays an important role in the healing process, trying to address these problems. One of the consequences of aging is the formation of pressure ulcers (PUs), which have a negative impact on the life quality of patients in the hospital, not only from a healthiness perspective but also psychologically. In this sense, e-health proposes several approaches to deal with this problem, however, these are not always very accurate and capable to prevent issues of this kind efficiently. Moreover, the proposed solutions are usually expensive and invasive. In this paper we were able to collect data coming from inertial sensors with the aim, in line with the Human-centric Computing (HC) paradigm, to design and implement a non-invasive system of wearable sensors for the prevention of PUs through deep learning techniques. In particular, using inertial sensors we are able to estimate the positions of the patients, and send an alert signal when he/she remains in the same position for too long a period of time. To train our system we built a dataset by monitoring the positions of a set of patients during their period of hospitalization, and we show here the results, demonstrating the feasibility of this technique and the level of accuracy we were able to reach, comparing our model with other popular machine learning approaches.
Year
DOI
Venue
2020
10.1186/s13673-020-0211-8
Human-centric Computing and Information Sciences
Keywords
Field
DocType
Wearable technology, AI, Machine learning, Deep learning, Healthcare system, IoT, Keras, TensorFlow, Cloud
Computer science,Wearable computer,Pressure Ulcer Prevention,Artificial intelligence,Deep learning,Multimedia
Journal
Volume
Issue
ISSN
10
1
2192-1962
Citations 
PageRank 
References 
0
0.34
22
Authors
5
Name
Order
Citations
PageRank
Giovanni Cicceri100.34
Fabrizio De Vita264.36
Dario Bruneo336237.34
Giovanni Merlino420828.11
Antonio Puliafito51562145.29