Paper Title
Deep Learning Based Models for Hand Tremor Detection Using Internet of Things

Abstract
Wearable gadgets that can monitor hand tremors are becoming more well-known. Patients can wear these gadgets to follow their tremors after some time, which can assist spe-cialists with understanding the seriousness of the tremors and how they are answering treatment. Some challenges still need to be addressed before these devices are generally utilized, like developing more accurate algorithms for detecting tremors and incorporating these gadgets with healthcare frameworks. Ana-lysts, clinicians, and industry specialists are attempting to develop new solutions for hand tremor discovery utilizing IoT devices. This innovation can work on the existences of individuals with neurological circumstances that cause hand tremors. Utilizing IoT sensors we have given a cost-effective real-time solution for the issue of early recognition of hand tremors sickness. This work additionally shows the comparison of various IoT sensors, specifically MPU9250, MPU6050 and ADXL345. A deep learning model is utilized to work with huge datasets and to acquire higher accuracy. Keywords - Internet of Things, Machine Learning, Hand Tremor, Comparison of Sensors