What to expect in the next 4 years of Computer Science in Sport

What to expect in the next 4 years of Computer Science in Sport Arnold Baca Department of Biomechanics, Kinesiology and Applied Computer Science ZSU University of Vienna

Prof. Heinz Zemanek * 1. 1. 1920 Mailüfterl , 1955 (From: mailuefterl.at/images/mailu/control.jpg )

Introduction

Current Trends (This conference)

Trends in Computer Science

Megatrends – Main developments

Ubiquitous Computing

Data Analysis Methods

Summary/Expectations

Survey

Introduction

Jürgen Perl, 2006 :

“ Future development of rapidly changing fields like CSS is difficult to predict“

2 aspects:  Amount of available data increases permanently – how to get the useful information?

 World wide communication needs improved IT

Computer Science in Sport: An Overview of Present Fields and Future Applications (Int J Comp Sci Sport, Spec. Ed., 2006)

Introduction

Josef Wiemeyer, 2006 :

“ The quality and quantity of research in computer science in sport grow continuously. One future task is to keep pace with the development of computer technology and computer science“.

“ Aktuelle Trends in der Sportinformatik“ [“Current Trends in Computer Science in Sport“] (Leipziger Sportwissenschaftliche Beiträge, 47, pp. 19-38).

Introduction

Daniel Link / Martin Lames, 2008 :

Forecasts until 2020:

(1) Positioning systems and lightweight sensors can be used for the capturing of total physiological and positional data. This information will be provided to trainers, athletes and mass media in real time.

(2) Based on the enhancements in the field of artificial intelligence, tactical behavior in sport games can be analyzed automatically.

(3) Virtual environments can simulate many different sports close to reality. This allows a training of perception, cognition and decision in sport specific situations.

(4) New display technologies will be integrated in sport clothes (e.g. sun glasses) and provide athletes with information during training and competition.

(5) Sport uses smart clothes including sensory (measuring and transmitting of data) and actuator (adaptation of material characteristics) features for diagnosis, prevention and performance improvement .

“ Matrix Reloaded – Struktur und Gegenstand der Sportinformatik“ [“Matrix Reloaded – Structure and Subject of Computer Science in Sport“ (Schriften der dvs, 189, pp. 11-32).

Introduction

Daniel Link / Martin Lames, 2008 :

Forecasts until 2020:

(6) Biomechanical models can simulate human movements perfectly, without using motion capturing or key frames. This allows the animation of virtual characters (e.g. for computer games), which look and move exactly like humans. These models can also be used to make the motor behavior of humanoid robots more manlike.

(7) Simulation has become an important research tool for natural scientific disciplines. The simulation of training processes (e.g. performance adaptation) can replace the real experiment in many cases.

(8) The increased computational power allows simulating the vibration and flow behavior of sport devices and athletes more realistic than today (e.g. flow resistance of swimmers).

(9) The relevance of sport clubs as a local organizer of training sessions decreases. Group building via social networks on the Internet gets more important for everyday sportspersons.

(10) In-class lectures at universities will lose their importance. Almost 50 % of theoretical courses and seminars in sport science will be replaced by online equivalents.

“ Matrix Reloaded – Struktur und Gegenstand der Sportinformatik“ [“Matrix Reloaded – Structure and Subject of Computer Science in Sport“ (Schriften der dvs, 189, pp. 11-32).

This Conference

Current Trends

Current Trends – This Conference (40 Presentations) Sensors/Wireless Technologies/Tracking 10 Modelling 5 Game/Performance Analysis 5 Data Analysis (Scoring/Ranking) 3 13 To be considered with caution!

Moore‘s Law

… not expected to stop until 2015

(M. Kanellos, 2005)

Source: Intel

Miniaturisation

Wireless Technologies

Networks

Mobile Ad Hoc Networks

 Mobile devices connected to a network by wireless links (self-configuration)

Open Source

 Hardware

 Software

Cloud Computing

Social Networks

Trends in Computer Science / Informatics Example: InteractivE-Training for Elderly People

Pervasive/Ubiquitous Computing

Data Processing/Analysis

Megatrends

Pervasive/ubiquitous computing in sports

2 categories:

(1) Technological developments in miniaturisation of the devices and increasing their ability to transmit data at considerable distances and

(2) Development of algorithms for fast and/or accurate data processing in (speedy) feedback provision that represents a firm step in the direction towards intelligent systems.

A. Baca, P. Dabnichki, M. Heller, P. Kornfeind, Journal of Sports Sciences, in press

Feedback Systems

Feedback Systems A. Baca, P. Dabnichki, M. Heller, P. Kornfeind, Journal of Sports Sciences, in press

Rowing oarlock foot stretcher d y n a m o m e t e r s outrigger

PDA-System PDA + NI DAQ Card Wireless LAN

Sensors in boat (e.g. oarlock dynamometer, oar angle,...)

Data acquisition using PDA (+ data acquisition card)

Online-transmission via WLAN

Online-Feedback

Rowing

Table Tennis

Target Athlete Digital Video Camera 50m Biathlon 5m 1m ~1.6m above ground

Biathlon

->

Training in natural, complex and ecological valid situations

Tracking Systems

Source: http://www.zonefivesoftware.com/SportTracks / GPS based systems

Player tracking Radio- & microwave based systems Transponder

Beetz et al. Proc. 5th Int. Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS), 2006. Player tracking

Kinematics Bandouch et al.. Proc. 5th Int. Conf. on Articulated Motion and Deformable Objects (AMDO), 2008. Courtesy of M. Beetz, TU Munich

Co-operation Computer Science – Sport Science (Human body models, joint centres, …)

->

Leisure and Entertainment

Leisure and Entertainment Games, e.g. Mountainbike, Marathon Ubiquitous-fitness support Broadcast

Example: RFID-chips in running

Example: Training in virtual environments B. Bideau et al. (2003) Presence-Teleoperators and Virtual Environments, 12 (4), 2003.

Example: Wearable Computing A. Baca, P. Dabnichki, M. Heller, P. Kornfeind, Journal of Sports Sciences , in press Electronic circuits are woven or printed on textiles and allow measuring a variety of physiological parameters directly.

Example: Wearable Computing 13 th Int. Symposium on Wearable Computers, September 2009, Linz, Austria: Highest potential of “Wearable Computing“‚ in the fields of medicine, health and sport. Gerhard Tröster , Head, Wearable Computing Group and Laboratory, ETH Zurich Application: Monitoring of snowboarders using sensors on the board and the body (clothes) to identify riding mistakes and to improve the style.

Megatrends

Automatic position oriented game protocol (Grunz, Memmert, Perl, 2009)

Player + team analysis

Basketball free shots (Grunz, Memmert, Perl, 2009) – From PhD-thesis A. Schmidt

Process models

Biathlon Shooting (Baca, Perl, Kornfeind, Böcskör, 2009)

Process models A 1 2 C 1 2 D 1 2 Color profile: sorted by total muzzle velocity

Process models Similarity Analysis: 3 athletes (before/after load, 16 shots) good stability less stable unstable very unstable A 1 A 2 C 1 C 2 D 1 D 2 before physical load after physical load

Classifying sports and physical activity

Positions, velocities and accelerations

Heart rate, heat, force (touch), respiration…

Wearable devices

Weber et al. International Journal of Computer Science in Sport 6 (2), 2007

Megatrends

Intelligent Systems

Revival of Expert Systems?

> 4 years

Remote Coaching System Overview Expert system

Remote Coaching – Hardware SPI Analog IN E.g. temperature, air pressure, potentiometer, … Digital I/O E.g. on-off switch, trigger, … ANT Modul  Controller Bridge Amplifier Strain gages Force sensors Accelerometer (strain gage based) Memory card „ Managed Networks“

Sports medicine specialist (physiological data) Biomechanist (dynamometric data) Athlete (e.g. report; + full access) Remote Coaching – Software Expert system Lotus Expeditor DB2-Database Device Manager Lotus Expeditor CLIENT Lotus Expeditor CLIENT

Remote Coaching Example: Rowing

Remote Coaching Example: Mountain biking Example: Mountain biking

Summary