A Deep-Learning-Based Smart Healthcare System for Patient's Discomfort Detection at the Edge of Internet of Things

Imran Ahmed, Gwanggil Jeon, Francesco Piccialli

Research output: Contribution to journalArticlepeer-review

71 Citations (Scopus)


The Internet of Things (IoT) widely supports the smart healthcare field; combined with computer vision, machine, and deep learning techniques; it provides fast and accurate services for automated patient discomfort monitoring/detection systems. Traditional patient monitoring systems are commonly composed of wearable sensors and vision-based methods. In this article, an IoT-based noninvasive automated patient's discomfort monitoring/detection system is presented and implemented, using a deep-learning-based algorithm. The system is based on an IP camera device; the patient's body's movement and posture are detected without using any wearable devices. The Mask-RCNN method is employed for the extraction of different key points on the patient body. These detected key points are then transformed into six major body organs using association rules of data mining. Furthermore, for analyzing the patient's discomfort, detected key point coordinates information is measured. Finally, the distance and the temporal threshold are applied to classify movements as either associated with normal or discomfort conditions. These key points information is also used to determine the postures of the patient lying on the bed. The patient's body position and posture are continuously monitored, based on which comfort and discomfort level are discriminated. For experimental evaluation, different video sequences are recorded covering two patient's beds. The experimental results show the proposed system's worth by achieving a true-positive rate of 94% and a false-positive rate of 7%.

Original languageEnglish
Article number9326342
Pages (from-to)10318-10326
Number of pages9
JournalIEEE Internet of Things Journal
Issue number13
Publication statusPublished - 1 Jul 2021
Externally publishedYes


  • Computer vision
  • Deep learning
  • Internet of Things (IoT)
  • Noninvasive
  • Patient's discomfort
  • Smart healthcare system


Dive into the research topics of 'A Deep-Learning-Based Smart Healthcare System for Patient's Discomfort Detection at the Edge of Internet of Things'. Together they form a unique fingerprint.

Cite this