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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


2015 Statistics in Mobile Computing

CmpE 594 Course, Monday 13:00 - 16:00, Room ETA A3


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. We will learn 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.

We will apply the lessons learned in practice by conducting empirical experiments with our mobile phones.

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


  • 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, 2nd March: Announcement of course project topic from each team
  • Friday, 20th March: Midterm technical report submission
  • Monday, 23rd March: Midterm presentation
  • Friday, 1st May: Final technical report submission
  • Monday, 4th May: Final Presentation


Lecture Program

Welcome and Introduction (2015-02-09)

Tools to be used in the course (2015-02-16)

  • 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
  • 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 (2015-02-23)

  • 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 (2015-03-02)

Grouped Data (2015-03-09)

Student’s t-test (2015-03-16)

Two-Sample Tests (2015-03-30)

Variance Tests and Power Calculations (2015-04-06)

Tabular Data (2015-04-13)

  • 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

Comparisons among more than two groups (2015-04-27)

  • One-way analysis of variance - Background
    • Decomposition of deviations
    • Variations between and within groups
    • F-Test
  • One-Way Analysis of Variance in R
  • Pairwise comparisons
  • Multiple testing
    • Bonferroni correction
    • Holm correction
  • Relaxing the variance assumption: non-parametric approaches
    • Welch test
    • Pairwise t-test
  • Repetition and mixed design

Final Project Presentations Part I (2015-05-04)

  • Team 2: Differentiate drawings of different geometric shapes with accelerometer data
  • Team 3: Running Style Analysis - Professional Runners vs Amateur Runners
  • Team 5: Statistical Analysis of Mobile Sensor Data to Detect Transportation Mode
  • Team 7: Brushing Teeth Analysis
  • Team 9: Mobile Phone Usage During a Course
  • Team 10: Floor Detection System (DetFloor) Using Mobile Sensors
  • Team 11: Statistics on Asanas in Yoga

Final Project Presentations Part II (2015-05-11)

  • Team 4: Road Quality Detection
  • Team 12: Compare hand vibrations between smokers and non-smokers


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

pub/lectures/2015_statistics_in_mobile_computing/start.txt · Last modified: 2016/01/23 13:50 by barnrich