Soft Transducer for Patient’s Vitals Telemonitoring with Deep Learning-Based Personalized Anomaly Detection

This work addresses the design, development and implementation of a 4.0-based wearable soft transducer for patient-centered vitals telemonitoring. In particular, first, the soft transducer measures hypertension-related vitals (heart rate, oxygen saturation and systolic/diastolic pressure) and sends...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:Sensors (Basel, Switzerland) Ročník 22; číslo 2; s. 536
Hlavní autori: Arpaia, Pasquale, Crauso, Federica, De Benedetto, Egidio, Duraccio, Luigi, Improta, Giovanni, Serino, Francesco
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Switzerland MDPI AG 01.01.2022
MDPI
Predmet:
Algorithms
Artificial intelligence
Blood pressure
Chronic illnesses
COVID-19
Data mining
Deep Learning
Expected values
Forecasts and trends
Heart beat
Humans
Hypertension
Internet of Things
LSTM
Machine learning
Mobile Applications
Mobile communications networks
Neural networks
Oxygen Saturation
Patients
remote health monitoring
Sensors
Time series
Transducers
Vital signs
Wearable computers
wearable sensors
wearable systems
Wireless telephone software
deep learning
health 4.0
LSTM
telemonitoring
vital sign monitoring
remote health monitoring
wearable sensors
machine learning
wearable systems
ISSN:1424-8220, 1424-8220
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:This work addresses the design, development and implementation of a 4.0-based wearable soft transducer for patient-centered vitals telemonitoring. In particular, first, the soft transducer measures hypertension-related vitals (heart rate, oxygen saturation and systolic/diastolic pressure) and sends the data to a remote database (which can be easily consulted both by the patient and the physician). In addition to this, a dedicated deep learning algorithm, based on a Long-Short-Term-Memory Autoencoder, was designed, implemented and tested for providing an alert when the patient’s vitals exceed certain thresholds, which are automatically personalized for the specific patient. Furthermore, a mobile application (EcO2u) was developed to manage the entire data flow and facilitate the data fruition; this application also implements an innovative face-detection algorithm that ensures the identity of the patient. The robustness of the proposed soft transducer was validated experimentally on five individuals, who used the system for 30 days. The experimental results demonstrated an accuracy in anomaly detection greater than 93%, with a true positive rate of more than 94%.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:1424-8220
1424-8220
DOI:10.3390/s22020536