Skip to content
To main site
Tampere Wireless Research Center (TWRC)
  • Home
  • Projects
  • Research Areas
    • Communication theory and fundamentals
      • Media
      • Publications
    • Radio access techniques and systems
      • Media
      • Publications
    • Radio positioning and localization
      • Media
      • Publications
    • Radio sensing and RF convergence
      • Media
      • Publications
    • Radio transceivers and signal processing
      • Media
      • Publications
    • RF microelectronics and embedded computing
      • Media
      • Publications
    • Full-duplex
      • Media
      • Publications
    • UAV communications
      • Media
      • Publications
    • Wireless for vertical industries
      • Media
      • Publications
    • Nano- and Bio-communications
      • Media
      • Publications
  • Publications
  • Infrastructures
    • Cellular Test Networks
    • IoT Test Networks
    • RF Laboratories
  • People
  • News and Events
  • Suomeksi
  • Home
  • Projects
  • Research Areas
    • Communication theory and fundamentals
      • Media
      • Publications
    • Radio access techniques and systems
      • Media
      • Publications
    • Radio positioning and localization
      • Media
      • Publications
    • Radio sensing and RF convergence
      • Media
      • Publications
    • Radio transceivers and signal processing
      • Media
      • Publications
    • RF microelectronics and embedded computing
      • Media
      • Publications
    • Full-duplex
      • Media
      • Publications
    • UAV communications
      • Media
      • Publications
    • Wireless for vertical industries
      • Media
      • Publications
    • Nano- and Bio-communications
      • Media
      • Publications
  • Publications
  • Infrastructures
    • Cellular Test Networks
    • IoT Test Networks
    • RF Laboratories
  • People
  • News and Events
  • Suomeksi

Tag: Sensor Data

Profile picture of Aditi Site. Photo: Nachiket Ayir
27.11.2024

Aditi Site: Transforming the healthcare delivery system from reactive to proactive by harnessing the power of eHealth sensors and machine learning

Wearable-based data-driven solutions using machine learning analytics can potentially simplify the entire healthcare ecosystem. Such solutions could be used for predicting chronic diseases and their severity, says MSc Aditi Site. Her doctoral dissertation analyzes the data obtained from multiple wearable sensors for identifying the disease such as Parkinson's, and diabetes, and even states of mind such as loneliness. Site’s approach could help enhancing the chronic illness management by providing solution for early detection and continuous monitoring of health parameters.

  • Doctoral dissertation

Contact persons

Mikko Valkama, Professor
mikko.valkama@tuni.fi

Joonas Säe, Staff Scientist
joonas.sae@tuni.fi

  • facebook
  • facebook
  • instagram
  • Subscribe to the RSS feed
Back to top
To main site
  • Contact →
  • Data protection →
  • Accessibility evaluation report →
  • Cookie policy →