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DTSTART;TZID=Europe/London:20240306T133000
DTEND;TZID=Europe/London:20240306T143000
DESCRIPTION:Recent years have witnessed an explosive growth in wearable tec
 hnology. This area is experiencing a massive expansion thanks to huge tech
 nical advances in information and communication technology driven by chang
 es in demography\, lifestyle\, environment\, etc. Wearable sensors are cur
 rently popular as personal tracking devices\, but wearables can assume a m
 ore significant role in multiple applications\, such as personalized healt
 h\, sports\, rehabilitation\, personal entertainment\, etc. In conjunction
  with technological advances in smart systems\, the continuous growth in n
 umbers of connected wearable devices raises major issues in terms of deali
 ng with huge amounts of data originating from heterogeneous devices. Machi
 ne learning and artificial intelligence will enable a new capability to pr
 ovide real-time recognition of patterns in the sensor data which can help 
 to identify events of interest and provide real-time feedback on such even
 ts to the wearer or caregiver so appropriate decisions can be made. This p
 resentation is focused on investigating the integration of wearable techno
 logy and developing machine learning models in the application spaces of h
 ealthcare\, rehabilitation\, and fitness monitoring. The works discussed i
 n the presentation demonstrate the potential of wearables in diverse appli
 cations and emphasizes the role of machine learning in enhancing their pra
 ctical use for widespread adoption in healthcare and fitness.\n\nSpeaker(s
 ): Salvatore Tedesco \n\nBldg: BC-03-102\, Ulster University Belfast Campu
 s\, Belfast\, Northern Ireland\, United Kingdom\, Virtual: https://events.
 vtools.ieee.org/m/410043
LOCATION:Bldg: BC-03-102\, Ulster University Belfast Campus\, Belfast\, Nor
 thern Ireland\, United Kingdom\, Virtual: https://events.vtools.ieee.org/m
 /410043
ORGANIZER:m.garcia-constantino@ulster.ac.uk
SEQUENCE:3
SUMMARY:AI-based Smart Wearable Systems for Health and Wellness in Sports\,
  Ageing\, and Rehabilitation
URL;VALUE=URI:https://events.vtools.ieee.org/m/410043
X-ALT-DESC:Description: <br /><p>Recent years have witnessed an explosive g
 rowth in wearable technology. This area is experiencing a massive expansio
 n thanks to huge technical advances in information and communication techn
 ology driven by changes in demography\, lifestyle\, environment\, etc. Wea
 rable sensors are currently popular as personal tracking devices\, but wea
 rables can assume a more significant role in multiple applications\, such 
 as personalized health\, sports\, rehabilitation\, personal entertainment\
 , etc. In conjunction with technological advances in smart systems\, the c
 ontinuous growth in numbers of connected wearable devices raises major iss
 ues in terms of dealing with huge amounts of data originating from heterog
 eneous devices. Machine learning and artificial intelligence will enable a
  new capability to provide real-time recognition of patterns in the sensor
  data which can help to identify events of interest and provide real-time 
 feedback on such events to the wearer or caregiver so appropriate decision
 s can be made. This presentation is focused on investigating the integrati
 on of wearable technology and developing machine learning models in the ap
 plication spaces of healthcare\, rehabilitation\, and fitness monitoring. 
 The works discussed in the presentation demonstrate the potential of weara
 bles in diverse applications and emphasizes the role of machine learning i
 n enhancing their practical use for widespread adoption in healthcare and 
 fitness.</p>
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