A Smart IoT System for Detecting the Position of a Lying Person Using a Novel Textile Pressure Sensor

Bedsores are one of the severe problems which could affect a long-term lying subject in the hospitals or the hospice. To prevent lying bedsores, we present a smart Internet of Things (IoT) system for detecting the position of a lying person using novel textile pressure sensors. To build such a syste...

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Veröffentlicht in:Sensors (Basel, Switzerland) Jg. 21; H. 1; S. 206
Hauptverfasser: Hudec, Robert, Matúška, Slavomír, Kamencay, Patrik, Benco, Miroslav
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Switzerland MDPI AG 31.12.2020
MDPI
Schlagworte:
Accelerometers
Classification
convolutional neural network
data classification
Experiments
Humans
Internet of Things
IoT system
Monitoring systems
Neural networks
Neural Networks, Computer
Personal computers
Posture
Pressure distribution
pressure sensor
Pressure ulcers
Prevention
Prone Position
Sensors
smart sensor
Software
Telemetry
Textiles
Velostat
pressure sensor
IoT system
position detection
convolutional neural network
data classification
smart sensor
Velostat
ISSN:1424-8220, 1424-8220
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Zusammenfassung:Bedsores are one of the severe problems which could affect a long-term lying subject in the hospitals or the hospice. To prevent lying bedsores, we present a smart Internet of Things (IoT) system for detecting the position of a lying person using novel textile pressure sensors. To build such a system, it is necessary to use different technologies and techniques. We used sixty-four of our novel textile pressure sensors based on electrically conductive yarn and the Velostat to collect the information about the pressure distribution of the lying person. Using Message Queuing Telemetry Transport (MQTT) protocol and Arduino-based hardware, we send measured data to the server. On the server side, there is a Node-RED application responsible for data collection, evaluation, and provisioning. We are using a neural network to classify the subject lying posture on the separate device because of the computation complexity. We created the challenging dataset from the observation of twenty-one people in four lying positions. We achieved a best classification precision of 92% for fourth class (right side posture type). On the other hand, the best recall (91%) for first class (supine posture type) was obtained. The best F1 score (84%) was achieved for first class (supine posture type). After the classification, we send the information to the staff desktop application. The application reminds employees when it is necessary to change the lying position of individual subjects and thus prevent bedsores.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s21010206