IMC 1 Lab
Multimodal Biosensors Framework for Fall Detection & Healthcare
Wearable IoT Framework for Elderly Patient Safety
Dr Adnan Seerat Sunbul Zahra
Project Overview
A wearable IoT framework integrating multimodal biosensors with intelligent signal processing for fall detection and remote health monitoring of elderly patients. The system fuses accelerometer, gyroscope, and heart rate data through deep learning models to distinguish between falls and daily activities.
System Architecture
Inputs
Accel + Gyro
Heart Rate
Processing Pipeline
Signal Denoising
Feature Extraction
DL Classifier
Decision Fusion
Outputs
Fall Alert
Health Report
Related Publications (8)
Curated from publications by Dr. Adnan Ahmad Rafique
2026journal
Vehicle Detection and Tracking Framework for Intelligent Transportation Systems using Machine Learning
KHI HTC 2026, 2026
View on Scholar2026journal
MAGCS-AFF: A Graph-Cut and Attention Fusion Framework for Object Recognition in Complex Natural Scenes
KHI HTC 2026, 2026
View on Scholar2020journal
Statistical Multi-Objects Segmentation for Indoor/Outdoor Scene Detection and Classification via Depth Images
IBCAST, 2020
View on Scholar2019journal
Salient Segmentation based Object Detection and Recognition using Hybrid Genetic Transform
ICAEM, 2019
View on Scholar2026journal
Intelligent Urban Transportation over Complex Vehicle Networks with YOLOv8 for Traffic Flow Monitoring
Computers, materials and continua, 2026
View on Scholar2025conference
Health Gaming based Activity Recognition Using Body-Worn Sensors via Artificial Neural Network
ComTech\'25, 2025
View on Scholar2025conference
Revolutionizing Exergaming: Cutting-Edge Gesture Recognition for Immersive Fitness Experiences
ComTech\'25, 2025
View on Scholar2025conference
Wearable Sensors for Exergaming Physical Exercise Monitoring via Dynamic Features
ComTech\'25, 2025
View on Scholar