BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:Asia/Kolkata BEGIN:STANDARD DTSTART:19451014T230000 TZOFFSETFROM:+0630 TZOFFSETTO:+0530 TZNAME:IST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20260508T065947Z UID:1A9677E8-4AEA-42EB-B459-51DBE59DC6B6 DTSTART;TZID=Asia/Kolkata:20260821T090000 DTEND;TZID=Asia/Kolkata:20260821T160000 DESCRIPTION:The proposed work focuses on the design and development of Ener gy-Efficient AI-Powered Health Sensing and Monitoring Systems for next-gen eration smart healthcare applications. Rapid advancements in wearable elec tronics\, Internet of Things (IoT)\, artificial intelligence (AI)\, and bi omedical sensing technologies have enabled continuous and remote monitorin g of human health conditions. However\, existing healthcare monitoring sys tems often suffer from high power consumption\, limited battery life\, com putational complexity\, and inefficient real-time data processing.\n\nThis proposed system aims to address these challenges by integrating low-power biomedical sensors\, edge-AI techniques\, and intelligent communication f rameworks to achieve accurate\, reliable\, and energy-efficient health mon itoring. The system will continuously collect physiological signals such a s ECG\, heart rate\, blood oxygen saturation (SpO₂)\, body temperature\, respiration rate\, and physical activity parameters through wearable or p ortable sensing devices.\n\nArtificial intelligence and machine learning a lgorithms will be incorporated for intelligent analysis\, anomaly detectio n\, health prediction\, and personalized healthcare recommendations. Edge computing approaches will be utilized to process data locally\, thereby re ducing latency\, communication overhead\, and cloud dependency while signi ficantly improving energy efficiency.\n\nThe proposed framework also empha sizes secure wireless communication and cloud-assisted healthcare analytic s for remote patient monitoring and telemedicine applications. The develop ed system can support healthcare services in hospitals\, homes\, rural hea lthcare centers\, elderly care environments\, and emergency medical situat ions.\n\nThe outcomes of the proposed work are expected to contribute towa rd sustainable\, affordable\, and scalable smart healthcare technologies w ith enhanced reliability\, prolonged operational lifetime\, and improved h ealthcare accessibility. The work aligns with emerging global research tre nds and national initiatives in Digital Health\, Artificial Intelligence\, Smart Healthcare\, and IoT-enabled medical systems.\n\nSpeaker(s): Dr. M. S. Manikandan\n\nBldg: Block-D\, Vignan's Institute of Information Techno logy\,Beside VSEZ\,Vadlapudi\,Duvvada\, Visakhapatnam \, Andhra Pradesh\, India\, 530049 LOCATION:Bldg: Block-D\, Vignan's Institute of Information Technology\,Besi de VSEZ\,Vadlapudi\,Duvvada\, Visakhapatnam \, Andhra Pradesh\, India\, 53 0049 ORGANIZER:vinayapasupuletu23@gmail.com SEQUENCE:129 SUMMARY:Energy-Efficient AI-Powered Health Sensing and Monitoring Systems URL;VALUE=URI:https://events.vtools.ieee.org/m/559067 X-ALT-DESC:Description: <br /><p>The proposed work focuses on the design an d development of Energy-Efficient AI-Powered Health Sensing and Monitoring Systems for next-generation smart healthcare applications. Rapid advancem ents in wearable electronics\, Internet of Things (IoT)\, artificial intel ligence (AI)\, and biomedical sensing technologies have enabled continuous and remote monitoring of human health conditions. However\, existing heal thcare monitoring systems often suffer from high power consumption\, limit ed battery life\, computational complexity\, and inefficient real-time dat a processing.</p>\n<p>This proposed system aims to address these challenge s by integrating low-power biomedical sensors\, edge-AI techniques\, and i ntelligent communication frameworks to achieve accurate\, reliable\, and e nergy-efficient health monitoring. The system will continuously collect ph ysiological signals such as ECG\, heart rate\, blood oxygen saturation (Sp O₂)\, body temperature\, respiration rate\, and physical activity parame ters through wearable or portable sensing devices.</p>\n<p>Artificial inte lligence and machine learning algorithms will be incorporated for intellig ent analysis\, anomaly detection\, health prediction\, and personalized he althcare recommendations. Edge computing approaches will be utilized to pr ocess data locally\, thereby reducing latency\, communication overhead\, a nd cloud dependency while significantly improving energy efficiency.</p>\n <p>The proposed framework also emphasizes secure wireless communication an d cloud-assisted healthcare analytics for remote patient monitoring and te lemedicine applications. The developed system can support healthcare servi ces in hospitals\, homes\, rural healthcare centers\, elderly care environ ments\, and emergency medical situations.</p>\n<p>The outcomes of the prop osed work are expected to contribute toward sustainable\, affordable\, and scalable smart healthcare technologies with enhanced reliability\, prolon ged operational lifetime\, and improved healthcare accessibility. The work aligns with emerging global research trends and national initiatives in D igital Health\, Artificial Intelligence\, Smart Healthcare\, and IoT-enabl ed medical systems.</p> END:VEVENT END:VCALENDAR