Smart healthcare solutions using the internet of medical things for hand gesture recognition system

Patient gesture recognition is a promising method to gain knowledge and assist patients. Healthcare monitoring systems integrated with the Internet of Things (IoT) paradigm to perform the remote solutions for the acquiring inputs. In recent years, wearable sensors, and information and communication...

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Bibliographic Details
Published in:Complex & intelligent systems 2021-06, Vol.7 (3), p.1253-1264
Main Authors: Mahmoud, Nourelhoda M., Fouad, Hassan, Soliman, Ahmed M.
Format: Article
Language:English
Subjects:
Complexity
Computational Intelligence
Data Structures and Information Theory
Engineering
Gesture recognition
Health care
Internet of medical things
Internet of Things
Learning
Original Article
Remote monitoring
Remote sensors
Telemedicine
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Summary:Patient gesture recognition is a promising method to gain knowledge and assist patients. Healthcare monitoring systems integrated with the Internet of Things (IoT) paradigm to perform the remote solutions for the acquiring inputs. In recent years, wearable sensors, and information and communication technologies are assisting for remote monitoring and recommendations in smart healthcare. In this paper, the dependable gesture recognition (DGR) using a series learning method for identifying the action of patient monitoring through remote access is presented. The gesture recognition systems connect to the end-user (remote) and the patient for instantaneous gesture identification. The gesture is recognized by the analysis of the intermediate and structuring features using series learning. The proposed gesture recognition system is capable of monitoring patient activities and differentiating the gestures from the regular actions to improve the convergence. Gesture recognition through remote monitoring is indistinguishable due to the preliminary errors. Further, it is convertible using series learning. Therefore, the misdetections and classifications are promptly identified using the DGR and verified by comparative analysis and experimental study. From the analysis, the proposed DGR approach attains 94.92% high precision for the varying gestures and 89.85% high accuracy for varying mess factor. The proposed DGR reduces recognition time to 4.97 s and 4.93 s for the varying gestures and mess factor, respectively.
ISSN:2199-4536
2198-6053
DOI:10.1007/s40747-020-00194-9