machine and deep learning approaches for human activity recognition

HUMAN ACTIVITY RECOGNITION SYSTEM (HARS) USING HISTOGRAM OF NORMALIZED FOURIER DESCRIPTOR

Smart Health Monitoring System based on IoT and Cloud Computing

Internet of Things (IoT) based smart device for cardiac patients monitoring using Blynk App

Object Tracking Using Vision on Raspberry Pi

Heart Disease Detection using ML and ES (Smart Wearable Health Monitoring System)

Building a Smartphone application to pair inertial sensors (Notch) for mechanical motion analysis

HUMAN ACTIVITIES RECOGNITION USING SHAPE MOMENTS AND HISTOGRAM OF NORMALIZED DISTANCES “HND”

Improvement of the performance analysis of activation functions based on DLLSTM classifiers on Human Activity Recognition for classification

Smart-Home using “IoT”