Multimodal Ai With Wearable And Iot Biosensors: A Review Of PPG, ECG And Temperature Fusion On ESP32 Platforms For Continuous Health Monitoring

Advances in artificial intelligence, the Internet of Things and wearable technology enable continuous health monitoring instead of periodic examinations that may miss physiological changes. This paper reviews multimodal AI health monitoring based on low-cost biosensors. It examines photoplethysmography for heart rate and blood oxygen saturation, single-lead electrocardiography for cardiac rhythm, and body temperature sensing, and describes how an ESP32 microcontroller acquires these signals and transmits them over Wi-Fi to a computer or cloud service. Signal processing steps including filtering, QRS detection and feature extraction are reviewed together with public physiological databases used to develop and validate algorithms. Machine learning methods such as random forest and logistic regression for classifying normal and potentially abnormal states are compared with deep learning on raw signals, and multimodal fusion strategies, early, late and hybrid, are discussed in the context of multimodal machine learning. Dashboard design for displaying live readings, history and threshold alerts is described. A five-layer architecture comprising sensing, acquisition, communication, multimodal AI and dashboard layers is proposed, along with research gaps, objectives, a comparison of sensors and challenges including motion artefacts, sensor accuracy limitations such as bias in pulse oximetry, battery life, privacy and the need for clinical validation. The review concludes that multimodal AI with wearable biosensors supports timely observation of physiological change under professional supervision.