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SUMMARY:IEM Seminar Series: Translating Raw Real-World Sensor Data into Cl
 inically Meaningful Digital Health Measures
DTSTART:20250909T171500
DTEND:20250909T180000
DTSTAMP:20260508T065956Z
UID:ae3bff751f48e15555662afa1efc56e521b82713beaf5fa6cba4459e
CATEGORIES:Conferences - Seminars
DESCRIPTION:Anisoara Ionescu\, PhD\, Signal Processing Laboratory 5 (LTS5
 )\, IEM\nAbstract\nThe ability to monitor movement and activity in everyda
 y environments using wearable sensors has opened new avenues for assessing
  health status in clinical and aging populations. However\, extracting mea
 ningful\, validated clinical metrics from raw sensor data remains a comple
 x\, multidisciplinary challenge.\nIn this talk\, I will discuss the differ
 ences between consumer-grade and research-grade wearables\, and the techni
 cal and clinical hurdles in developing validated algorithms to analyze sen
 sor data from individuals with severe motor impairments (e.g.\, Parkinson
 ’s disease\, multiple sclerosis\, hip fracture\, cerebral palsy).\nI wil
 l further discuss how advanced signal processing techniques can extract ri
 ch\, multidimensional features from real-world behavioral data\, with a pa
 rticular focus on the concept of ‘complexity’. Initially developed in 
 the analysis of physiological signals\, complexity metrics are increasingl
 y recognized as comprehensive indicators of functional health. I will illu
 strate how these concepts can be extended to movement and activity pattern
 s\, and how reductions in complexity are observed in clinical conditions s
 uch as chronic pain\, frailty\, and fear of falling in older adults.\nFina
 lly\, current limitations and clinical requirements as well as the potenti
 al of AI in this context will be discussed\, highlighting future research 
 directions at the intersection of movement science\, clinical relevance\, 
 and data-driven precision health.\n\nBio\nAnisoara Ionescu\, PhD\, researc
 h focuses on algorithm development and data-driven methods to extract clin
 ically meaningful metrics from inertial and physiological signals\, aimed 
 at improving clinical assessment\, particularly in neurological and motor-
 impaired populations. She has contributed to numerous interdisciplinary na
 tional and EU projects and played a key role in the validation of digital 
 mobility outcomes in initiatives like Mobilise-D (EU\, Innovative Medicine
  Initiative -IMI).  She was a co-recipient of the 2015 Leenaards Prize fo
 r translational research.\n 
LOCATION:ELA 2 https://plan.epfl.ch/?room==ELA%202 https://epfl.zoom.us/j/
 64669516915
STATUS:CONFIRMED
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