ECLIPA: Seamless Integration of a Tri-Wearable Multimodal Health Intelligence System for Adaptive Monitoring
Abstract Eclipa is a comprehensive, tri-wearable, AI-powered health intelligence platform designed to deliver continuous, non-invasive, and context-aware monitoring of human physiology and behavior. The system integrates three synchronized wearables—a smart ring, a smart wristband, and an ear-mounted clip—each equipped with advanced multimodal sen-sors to capture a wide range of physiological and behavioral data. Eclipa employs AI-driven state classification and sensor fusion algorithms to enable adaptive mode switching, seamlessly activating eight intelligent modes—Sleep, Exercise, Work, Focus, Social, Daily Wellness, Nervous Response, and Disease Management—based on real-time physiological patterns and contextual cues. The smart ring, wristband, and ear clip are embedded with photoplethys-mography (PPG) sensors for continuous monitoring of cardiovascular parameters, while a combination of accelerom-eters and microphones allows for precise tremor detection, motion tracking, and activity recognition. Additionally, ECG electrodes integrated into the wearables facilitate accurate cardiac waveform analysis for early detection and management of cardiovascular anomalies. The platform’s multimodal data acquisition, coupled with advanced AI analytics, enables personalized health insights, proactive disease management, and real-time behavioral monitoring, offering a holistic approach to preventive and precision healthcare. Eclipa’s design emphasizes non-intrusive, real-world applicability, making it suitable for continuous long-term monitoring in diverse daily settings. By combining physiological sensing, behavioral analysis, and AI-driven intel-ligence, Eclipa represents a next-generation solution for continuous health monitoring, personalized wellness, and adaptive lifestyle management. Keywords: AI-powered health monitoring, Wearable devices, Smart ring, Smart wristband, Ear-mounted clip, Photoplethysmography (PPG), Electrocardiography (ECG), Sensor fusion
Authors
- E. Karolinekersin
- Dhanush.U
- Baba.C
- Udhaya Ram.T.M
Institutions
- SRM Institute of Science and Technology (IN)
- SRM Dental College (IN)
Publication Details
- Journal
- Zenodo (CERN European Organization for Nuclear Research)
- Published
- 2026-04-30
- DOI
- https://doi.org/10.5281/zenodo.19909662
- Primary Topic
- Non-Invasive Vital Sign Monitoring
- Type
- article
- Field-Weighted Citation Impact
- 0.00