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BEGIN:VEVENT

CATEGORIES:Artificial Intelligence
CATEGORIES:Utilities
CATEGORIES:Main
CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None
CREATED:20250821T151912Z
DESCRIPTION:Human interactions with the environment and with others genera
 te mechano-acoustic (MA) signals that encode rich information about physi
 ological and biomechanical processes. Although these signals often attenu
 ate at the skin-air interface\, epidermal electronics provide a powerful 
 means to capture them\, offering a transformative approach for non-invasi
 ve\, real-time health monitoring. We present a soft\, skin-mounted MA sen
 sing platform that integrates inertial measurement units\, strain gauges\
 , or ultrasound transducers to record body sounds and kinematics with hig
 h fidelity. This thin\, conformal "sticker" enables MA detection at anato
 mically challenging sites inaccessible to rigid\, planar wearables. Paire
 d with frequency-domain analysis and machine-learning algorithms\, the sy
 stem interprets high-density\, networked MA data streams to infer a wide 
 spectrum of health and performance metrics. Demonstrated applications inc
 lude wireless\, continuous ambulatory monitoring of vital signs (e.g.\, h
 eart rate\, respiration\, activity)\, tissue stiffness\, muscle force\, b
 ody morphology\, posture\, and unconventional biomarkers such as speech p
 atterns\, cough frequency\, swallowing\, social interaction metrics\, and
  sleep quality. Ongoing work extends these capabilities toward quantifyin
 g proprioception through biomechanical signature analysis and advancing f
 lexible\, stretchable ultrasound systems for brain imaging and stimulatio
 n. Ultimately\, this technology aims to capture a high-dimensional array 
 of mechanical and acoustic signatures\, enabling comfortable\, accurate\,
  and comprehensive decoding of physiological state\, behavioral patterns\
 , functional performance\, and cognitive or intentional states in real ti
 me.
DURATION:PT1H
DTSTAMP:20250821T151912Z
DTSTART;TZID=America/New_York:20250923T120000
LAST-MODIFIED:20250821T151912Z
LOCATION;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf6e-00000000:None
STATUS:CONFIRMED
SUMMARY:Epidermal Electronics for Non-Invasive\, Real-Time Health Monitori
 ng 
UID:CAL-8a00ec8b-979413b9-0198-cd366cf2-0000622bdemobedework@mysite.edu
URL:https://duke.zoom.us/webinar/register/WN_aEJF6IFATxSqSQQ8ypWKFQ
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Main:/user/public-user/Utili
 ties/Main
X-BEDEWORK-ALIAS;X-BEDEWORK-PARAM-DISPLAYNAME=Artificial Intelligence:/use
 r/public-user/Topics/Artificial Intelligence
X-BEDEWORK-CS;X-BEDEWORK-PARAM-DESCRIPTION="/principals/users/agrp_provost
 _plusDataScience,/principals/users/agrp_PrattSchool_BME,/principals/users
 /agrp_SchoolofMedicine_BiostatisticsandBioinformatics,/principals/users/a
 grp_CCT,/principals/users/agrp_ArtsandSciences_ComputerScience,/principal
 s/users/agrp_CTSI CREDO,/principals/users/agrp_ClinicalandTranslationalSc
 ienceAward,/principals/users/agrp_agrpDukeTranslationalMedicineInstitute,
 /principals/users/agrp_PrattSchool_ECE,/principals/users/agrp_PrattSchool
 _MEMS,":+DataScience (+DS)\,Biomedical Engineering (BME)\,Biostatistics a
 nd Bioinformatics\,Center for Computational Thinking\,Computer Science\,C
 TSI CREDO\,Duke Clinical and Translational Science Award (CTSA)\,Duke Cli
 nical and Translational Science Institute (CTSI)\,Electrical and Computer
  Engineering (ECE)\,Mechanical Engineering and Materials Science (MEMS)
X-BEDEWORK-SPEAKER:Xiaoyue Ni\, PhD
X-BEDEWORK-DUKE-SERIES:AI Health Virtual Seminar Series
X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=aihealth@duke.edu:Duke A
 I Health
X-BEDEWORK-SUBMITTEDBY:tmt26 for AI Health (agrp_SOM_Forge)
END:VEVENT
END:VCALENDAR

