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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


2016 Statistics in Mobile Computing

CmpE 594 Course, Monday 12:15 - 15:00, Room B5


This course introduces basic concepts of statistical data analysis and their practical application in mobile computing.

The course covers the entire range of statistical data analysis. Topics include how to design an empirical data collection in a statistical valid way, how to collect data from daily life with the help of mobile computing, and how to achieve statistical test results.

Lessons learned will be applied in practice by conducting empirical experiments with mobile phones.

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

Lecture topics

  • Intro Mobile Computing
  • Mobile Sensor Data Logging
  • The R Project for Statistical Computing
  • Feasibility Study
  • Distribution Graphics
  • Grouped Data
  • Student’s t-test
  • Two-Sample Tests
  • Variance Tests
  • Power Calculations
  • Tabular Data
  • Comparisons among more than two groups


  • Design and conduct a data collection experiment with mobile phones
  • Perform statistical data analysis of the collected data
  • Write a technical report about your experimental study and present it in the lecture

Please find all details about the examination procedure in the handouts of the first course “Welcome and Introduction” provided below.

Important Dates

  • Monday, 15th February: Team assignments
  • Monday, 29th February: Project topic announcement
  • Sunday, 20th March: Midterm technical report submission
  • Monday, 21st March: Midterm presentation
  • Sunday, 1st May: Final technical report submission
  • Monday, 2nd May: Final Presentation

Lecture Program

Welcome and Introduction (2016-02-08)

Tools to be used in the course (2016-02-15)

  • Mobile Sensor Data Logging
    • Preferred Android Sensor Log app: installation and usage
    • Potential examination project topics using GPS, WiFi, Accelerometer, Gyroscope, Light sensor
    • Data collection and export
    • iOS Alternatives
    • Funf Open Sensing Framework
  • The R Project for Statistical Computing
    • What is R?
    • R environment
    • Download and Installation
    • Getting Started with R
    • Help and Documentation
    • Contributed R Packages
    • R Programming Environment

Feasibility Study (2016-02-22)

  • Design and conduct a feasibility study
  • Collect experimental data with Sensor Log
  • Data export
  • Import collected data into R
  • Data preprocessing
  • Data frame indexing and filtering
  • Add and transform features
  • Scatter plots
  • Compute data characteristics
  • Define research hypotheses
  • From the feasibility study to the real experiment

Distribution Graphics (2016-02-29)

Grouped Data (2016-03-07)

Student’s t-test (2016-03-14)

Two-Sample Tests (2016-04-04)

Variance Tests and Power Calculations (2016-04-11)

Tabular Data (2016-04-25)

  • Discrete Distributions
  • Point probabilities
  • Single Proportions Approximate Test
  • Exact Binomial Test
  • Binomial Test for Walking and Running
  • Two or more proportions
  • 2-sample test for equality of proportions
  • Fisher’s test
  • Chi-Square Test
  • Test for Trend
  • Contingency Tables


Peter Dalgaard. Introductory Statistics with R. Springer (2008).

pub/lectures/2016_statistics_in_mobile_computing/start.txt · Last modified: 2016/04/25 10:13 by barnrich