Detecting the Human Activities of Aging People using Restricted Boltzmann Machines with Deep Learning Technique in IoT

Approximately 962,000,000 persons worldwide are 60 or older. Despite the widespread adoption of techniques for human activity detection, there has been a dearth of study into the specific challenge of identifying the tasks performed by the elderly. Given the proliferation of wearable and mobile devices, the Internet of Healthcare Things is assuming a larger role in Human Activity Recognition (HAR). This article focuses on assisting the elderly by tracking their movements in both indoor and outdoor settings. The dataset includes human actions like sitting, walking, ascending and descending stairs, standing, and lying down. Here, how deep learning might improve HAR in Internet of Home Things settings has been examined. For better HAR results, a semi-supervised deep learning framework has been developed that makes effective use of the imperfectly labelled sensor data in order to fine-tune the classifier learning model. An intelligent auto-labelling system is built on top of Deep Q-Network to enhance learning performance in Internet of Things (IoT) environment and address the issue of insufficient labelled samples. Finally, real-world data is used in trial and evaluation to prove the method’s utility and efficacy.