Abstract
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.
Highlights
Hand gesture recognition is observed by the wearable sensor device based on the movements of the hand
The moves indicate the action to perform by the user and provide a response to the human [1]. It mostly used in the field of medical applications by utilizing the Internet of Medical Things (IoMT)
The proposed dependable gesture recognition (DGR) is compared with the methods of Utligesture [26], MobiGesture [24], and two-level hierarchy structure (TLHS)-convolutional neural network (CNN) [33] for comparative analysis
Summary
Hand gesture recognition is observed by the wearable sensor device based on the movements of the hand. The moves indicate the action to perform by the user and provide a response to the human [1]. It mostly used in the field of medical applications by utilizing the Internet of Medical Things (IoMT). The gesture recognition which is observed by the sensor are matched with the IoMT and provides the results. Using IoMT, the diagnosis is easy and accurate [2, 3]. The information is secure to handle [4, 5]. The medical data can be quickly sent and received from the healthcare IoMT
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