Soft Transducer for Patient's Vitals Telemonitoring with Deep Learning-Based Personalized Anomaly Detection.
Sensors (Basel)
; 22(2)2022 Jan 11.
Article
in En
| MEDLINE
| ID: mdl-35062496
ABSTRACT
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%.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Mobile Applications
/
Deep Learning
Type of study:
Diagnostic_studies
Limits:
Humans
Language:
En
Journal:
Sensors (Basel)
Year:
2022
Document type:
Article
Affiliation country:
Italy