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BEGIN:VEVENT
DTSTAMP:20260512T162229Z
DTSTART;VALUE=DATE-TIME:20190220T140000
DTEND;VALUE=DATE-TIME:20190220T150000
SUMMARY:Thomas Ploetz: Computational Behavior Analysis through Wearables 
 and Machine Learning
TZID:Europe/London
UID:20190220-8a1785d868c87b470168e2f788931d0f@warwick.ac.uk
CREATED:20190212T182935Z
DESCRIPTION:Abstract: We live in an era in which the number of smartphone
 s is now greater than the number of humans living on Earth. As such\, th
 e field of mobile and ubiquitous computing is transforming many--if not 
 all--areas of our lives. With the next wave of technological breakthroug
 hs now wearables\, such as smartwatches but also head-worn devices\, are
  becoming mainstream. This overall transformation has great potential fo
 r many application areas. Most prominently\, it is now possible to conti
 nuously and unobtrusively record rich behavior data that can inform obje
 ctive health assessments thereby serving as basis for improved care and 
 treatment\, and thus wellbeing. The basis for effective health assessmen
 ts are robust and reliable methods for human activity recognition -- mor
 e generally referred to as sensor-based Computational Behavior Analysis 
 (CBA). From a technical perspective the analysis task translates into a 
 time-series assessment problem\, yet with a number of domain-specific co
 nstraints and requirements. In this talk I will explore these specific c
 hallenges and give an overview of work in our group that is pushing the 
 boundaries of CBA with specific focus on usable Digital Health. In respo
 nse to challenges such as noisy sensor data\, ambiguous ground truth ann
 otation\, and typically limited size sample datasets we have developed a
 nd validated sensor data analysis and machine learning methods that focu
 s on these domain specifics and thus enable effective operation. I will 
 illustrate how the constraints and requirements of real-world applicatio
 n scenarios have allowed me and my team to push the boundaries of core s
 ensor data analysis research. About the speaker: Thomas Ploetz is a Comp
 uter Scientist with expertise and more than 15 years of experience in Pa
 ttern Recognition and Machine Learning research (PhD from Bielefeld Univ
 ersity\, Germany). His research agenda focuses on applied machine learni
 ng\, that is developing systems and innovative sensor data analysis meth
 ods for real world applications. Primary application domain for his work
  is computational behaviour analysis where he develops methods for autom
 ated and objective behaviour assessments in naturalistic environments\, 
 thereby making opportunistic use of ubiquitous and wearable sensing meth
 ods. Main driving functions for his work are "in the wild" deployments a
 nd as such the development of systems and methods that have a real impac
 t on people's lives. In 2017 Thomas joined the School of Interactive Com
 puting at the Georgia Institute of Technology in Atlanta\, USA where he 
 works as an Associate Professor of Computing. Prior to this he was an ac
 ademic at the School of Computing Science at Newcastle University in New
 castle upon Tyne\, UK\, where he was a Reader (Assoc. Prof.) for "Comput
 ational Behaviour Analysis" affiliated with Open Lab\, Newcastle's inter
 disciplinary research centre for cross-disciplinary research in digital 
 technologies.
LOCATION:MSB 2.23
CATEGORIES:
LAST-MODIFIED:20190212T182935Z
ORGANIZER;CN=Sara Kalvala:
END:VEVENT
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