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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:2014_statistics_in_mobile_computing:start

2014 Statistics in Mobile Computing

CmpE 594 Course, Monday 13:00 - 16:00, Room ETA A5


Intro

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 courses.


Examination

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

Please find all details about the examination here: Examination.pdf

Deadlines

  • Midterm technical report submission: Friday, 28. March
  • Midterm presentation: Monday, 31. March
  • Final technical report submission: Friday, 9. May
  • Final presentation: Monday, 12. May

Teams

  • Team 1: Statistical Analysis of Accelerometer Sensor Data for Daily Life Activities
    • BURÇAK AĞRIDAĞ
    • NECATİ CİHAN CAMGÖZ
    • BİNNUR GÖRER
  • Team 2: A Study on Identifying Attention Distraction
    • NEVAL EDEN
    • ÇAĞATAY YURDASAL
  • Team 3: LINDY HOPOMETER
    • ARDA ÇELEBİ
    • ÇAĞIL ULUŞAHİN SÖNMEZ
  • Team 4: Comparison of Four Games' Aggression Levels: Temple Run, Subway Surfers, Tetris, Hungry Shark
    • ENES EMRE BABUR
    • KEREM ÇELİK
    • GÖKSEL TAŞKAYA
  • Team 5: Handwriting Differentiation
    • MEHMET CAN GÜVEN
    • SEBAHAT SİNEM KAFILOĞLU
    • ERDEM ORMAN
  • Team 6: Measuring Magnetic Fields
    • UFUK CAN BİÇİCİ
  • Team 7a: Comparing Magnetic Fields of B.U. Campuses
    • FURKAN GÜRPINAR
  • Team 7b: Study of Environmental Factors in Different locations
    • WAQAS MOEED
  • Team 8: GestureApp: Realtime Gesture Recognition Application on Mobile Devices
    • YASEMİN TİMAR
  • Team 9: Recognition of Fitness Exercises using Mobile Phone Sensors
    • OLIVER STEFAN RÖSS
    • GÜL VAROL

Lecture Program

Intro (2014/02/17)

  • Course Objectives
  • Tools to be used in the course
  • Examination Procedure
  • Intro general approach of statistical data analysis
  • Example: Acceleration magnitude during Running
  • Intro Mobile Computing
  • Intro Smartphone Sensor Data Logging

Tools to be used in the course (2014/02/24)

  • Mobile Sensor Data Logging
    • Preferred Android Sensor Log app: installation and usage
    • iOS Alternatives
  • The R Project for Statistical Computing
    • What is R?
    • R environment
    • Download and Installation
    • Contributed R Packages
    • Getting Started with R

Data Exploration (2014/03/03)

Distribution Graphics and Summary Statistics (2014/03/10)

Student's t-Test (2014/03/17)

  • Recap: Statistical Analysis – General Approach
  • Example: Smart Phone Battery Runtime
  • Normal distribution assumption
  • Key concept Error of Mean
  • Student’s t distribution
  • Test Statistic
  • Distribution quantiles
  • Direction of an effect
  • Significance level vs. p-value
  • Student’s t-Test in R
  • Two-sample Student’s t-Test
  • t-Test for independent samples

Two-sample tests and Power calculations (2014/03/24)

  • Two-sample t-test
  • Paired t-test
  • Intro Distribution-free Methods (non-parametric statistical hypothesis tests)
  • Wilcoxon Test: two-sample, matched pairs
  • Comparison of variances: F-test
  • Power and the computation of sample size
  • Principles of power calculations
  • Approximate Power Calculations
  • Two-sample power calculation in R

Midterm presentation (2014/03/31)

  • Team 1: Statistical Analysis of Accelerometer Sensor Data for Daily Life Activities
  • Team 2: A Study on Identifying Attention Distraction
  • Team 3: Lindy Hop-o-meter
  • Team 4: Comparison of Two Games' Aggression Levels: Temple Run vs. Subway Surfers
  • Team 5: Cursive Handwriting vs. Normal Handwriting
  • Team 6: Measuring Magnetic Fields
  • Team 7:
    • Comparing Magnetic Fields of B.U. Campuses
    • Study of Environmental Factors in different locations

Midterm presentation Part 2 (2014/04/07)

  • Team 8: Realtime Gesture Recognition Application on Mobile Devices
  • Team 9: Recognition of Fitness Exercises using Mobile Phone Sensors

Comparisons among more than two groups (2014/04/14)

  • 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

Tabular Data (2014/04/28)

Final Presentation Part 1 / Linear Regression (2014/05/05)

  • Final Presentation Part 1
    • Team 7: Comparing Magnetic Fields of B.U. Campuses
    • Team 9: Recognition of Fitness Exercises using Mobile Phone Sensors

Final Presentation Part 2 (2014/05/12)

  • Team 1: Statistical Analysis of Accelerometer Sensor Data for Daily Life Activities
  • Team 2: A Study on Identifying Attention Distraction
  • Team 3: Lindy Hop-o-meter
  • Team 4: Comparison of Two Games' Aggression Levels: Temple Run vs. Subway Surfers
  • Team 5: Cursive Handwriting vs. Normal Handwriting
  • Team 6: Measuring Magnetic Fields
  • Team 8: Realtime Gesture Recognition Application on Mobile Devices

Literature

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

pub/lectures/2014_statistics_in_mobile_computing/start.txt · Last modified: 2015/08/07 14:31 by barnrich