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Open positions for Doctoral Students (Ph.D.) and Postdoctoral Researchers in Digital Health with emphasis on Connected Health available

Editorial Wearable Therapy – Detecting Information from Wearables and Mobiles that are Relevant to Clinical and Self-directed Therapy has been accepted for publication in Methods of Information in Medicine, 2017

Paper Detecting Anxiety States when Caring for People with Dementia has been accepted for publication in Methods of Information in Medicine, 2017

Paper Variability analysis of therapeutic movements using wearable inertial sensors has been accepted for publication in Journal of Medical Systems, 2017

Paper Semi-supervised Model Personalization for Improved Detection of Learner’s Emotional Engagement accepted for ICMI 2016

Paper mk-sense: An extensible platform to conduct multi-institutional mobile sensing campaigns accepted for UCAmI 2016

Paper Towards an emotional engagement model : Can affective states of a learner be automatically detected in a 1 : 1 learning scenario? accepted for PALE 2016

Paper What good leaders actually do: Micro-level leadership behavior, leader evaluations, and team decision quality accepted in European Journal of Work and Organizational Psychology, 2016

Paper Exploring the link between behaviour and health available online in Personal and Ubiquitous Computing Journal 2015

Paper Mobile phones as medical devices in mental disorder treatment: an overview available online in Personal and Ubiquitous Computing Journal 2015

9th International Conference on Pervasive Computing Technologies for Healthcare

pub:lectures:2016_personal_electronic_health_assistants:start

2016 Personal Electronic Health Assistants

CmpE 580 Course, Wednesday 14:00 - 17:00, Room BM A5

Intro

Many of our behavior patterns are major determinants of important health outcomes. Positive health effects can be achieved when indicators of individual’s lifestyle and behavior are kept in healthy ranges.

Personal electronic health assistants support health and wellness in every-day life.

This lecture provides an introduction into personal electronic health assistants by combining relevant methods, tools and practical applications from research and industry.

Methods include physiological signal processing, data privacy methods and relevant methods from machine learning and data mining.

Applications include a personal stress assistant, ambient assisted living and the smartphone as electronic health assistant.

There are no special requirements to attend this lecture since all needed background knowledge is provided within the courses.


Examination

Please find all details about the examination in the handouts of the first lecture: 2016_09_21_cmpe_580_personal_electronic_health_assistants_01_intro.pdf

Important Dates

  • October 18: Proposal Draft
  • November 1: State of the Art
  • November 2: Midterm Presentation
  • November 15: Research proposal
  • December 6: Feasibility Study
  • December 7: Final Presentation

Teams

  • Team 1: Sleep Apnea Detection, Burçin Camcı and Ali Yavuz Kahveci
  • Team 2: FitMatch, Deniz Ekiz and Serkan Buğur
  • Team 3: Healthy Route Recommendation System, Ahmet Safa Orhan and Dursun Ahmet Keleş
  • Team 4: MyPace Reader: Attention-Sensitive Speed-Reading Assistant, Tuğçe Akkoç and Yavuz Köroğlu
  • Team 5: Toilet Tracker, Doruk Dundar and Lance Powell
  • Team 6: Monitoring sport exercises, Burak Dağlı and Hakan Demirel
  • Team 7: Parkinson’s Disease Monitoring, Ömer Yetik

Lecture Program

Introduction and Motivation (2016-09-21)

Physiological Sensing I (2016-09-28)

Physiological Sensing II (2016-10-05)

Tools (2016-10-12)

Emotion Recognition (2016-10-19)

Smart Watch (2016-10-26)

  • Data collection and labeling with the “UBI Health Sensor Sample” app
  • Smart watch sensor data export
  • Data exploration
  • Data Import
  • Data Visualization

Emotion Recognition II (2016-11-09 and 2016-11-16)

  • Emotion Elicitation
    • Affective pictures
    • Movies and music
    • Psychological approaches
    • Daily life
  • Self-Organizing Maps (SOMs) for emotion recognition
    • Mapping Signal Space to SOM
    • SOM Training
    • SOM Prediction
    • Supervised SOMs: Supervised Kohonen Network, Bi-Directional Kohonen Network, Counter Propagation Network, XY-fused Kohonen Network
  • Emotion SOM for detecting affective state from our voice
  • Stress SOM for discriminating between cognitive load and stress
pub/lectures/2016_personal_electronic_health_assistants/start.txt · Last modified: 2016/11/15 10:44 by barnrich