Home Health Monitoring Using Edge Computing and Deep Transfer Learning

This paper presents an edge computing-based deep transfer learning method for home health monitoring. The suggested approach analyzes medical data from wearable technology and Internet of Things sensors using pre-trained Convolutional Neural Networks (CNNs), such as ResNet50. By deploying the model on edge devices, health monitoring applications may process data in real-time and experience a reduction in latency. The objectives of this system are to promote proactive healthcare management by offering ongoing health monitoring and early problem detection. The outcomes show that deep transfer learning works well for home health monitoring because of its outstanding precision in health assessment tasks.