Analysis of Visible Light Communication using Integrated Avalanche Photodiode
Volume-4 | Issue-2
A Review on Identifying Suitable Machine Learning Approach for Internet of Things Applications
Volume-3 | Issue-3
TOWARDS GHZ METALLIC ACCESS NETWORKS
Volume-1 | Issue-1
REVIEW ON UBIQUITOUS CLOUDS AND PERSONAL MOBILE NETWORKS
Volume-1 | Issue-3
Process Control Ladder Logic Trouble Shooting Techniques Fundamentals
Volume-1 | Issue-4
TRUST BASED ROUTING ALGORITHM IN INTERNET OF THINGS (IoT)
Volume-1 | Issue-1
COMPUTATIONAL OFFLOADING FOR PERFORMANCE IMPROVEMENT AND ENERGY SAVING IN MOBILE DEVICES
Volume-1 | Issue-4
ANALYSIS OF ROUTING PROTOCOLS IN FLYING WIRELESS NETWORKS
Volume-1 | Issue-3
Dual Edge-Fed Left Hand and Right Hand Circularly Polarized Rectangular Micro-Strip Patch Antenna for Wireless Communication Applications
Volume-2 | Issue-3
Modified Gray Wolf Feature Selection and Machine Learning Classification for Wireless Sensor Network Intrusion Detection
Volume-3 | Issue-2
TRUST BASED ROUTING ALGORITHM IN INTERNET OF THINGS (IoT)
Volume-1 | Issue-1
Hybrid Micro-Energy Harvesting Model using WSN for Self-Sustainable Wireless Mobile Charging Application
Volume-3 | Issue-3
Three Phase Coil based Optimized Wireless Charging System for Electric Vehicles
Volume-3 | Issue-3
Cyber-attack and Measuring its Risk
Volume-3 | Issue-4
REVIEW ON UBIQUITOUS CLOUDS AND PERSONAL MOBILE NETWORKS
Volume-1 | Issue-3
Analysis of Solar Power Generation Performance Improvement Techniques
Volume-4 | Issue-3
Pollination Inspired Clustering Model for Wireless Sensor Network Optimization
Volume-3 | Issue-3
Design of Low Power Cam Memory Cell for the Next Generation Network Processors
Volume-3 | Issue-4
A STUDY OF RESEARCH NOTIONS IN WIRELESS BODY SENSOR NETWORK (WBSN)
Volume-1 | Issue-2
Computation of Constant Gain and NF Circles for 60 GHz Ultra-low noise Amplifiers
Volume-3 | Issue-3
Volume - 5 | Issue - 4 | december 2023
Published
07 February, 2024
The current state of health detection in IoT sensors is affected by several limitations. Existing systems often struggle with inaccurate and unreliable readings, leading to potential misdiagnoses and patient discomfort. These systems also tend to lack adaptability and robustness when dealing with various modalities of data, hindering their overall effectiveness. Furthermore, the reliance on traditional algorithms in the absence of machine learning hampers their ability to provide precise and real-time heartbeat information. In light of these shortcomings, this work seeks to address these issues by studying the enhanced approach, emphasizing the integration of multimodal data fusion techniques and machine learning algorithms. The aim is to identify the drawbacks associated with existing systems and provide the more accurate and responsive solution for heath detection offered by IoT sensors through the application of data fusion and machine learning.
KeywordsIoT Sensors Wearable device Multimodal Data Fusion Machine learning Deep Learning Heart Beat Detection
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