What Makes Games Challenging? · Exergames have small effects [BBC+08, KW10] Objective scientific...

© author(s) of these slides including research results from the KOM research network and TU Darmstadt; otherwise it is specified at the respective slide

7-Nov-14

Prof. Dr.-Ing. Ralf Steinmetz

KOM - Multimedia Communications Lab

ACMM2014___Challenge_in_Exergames___Sandro_Hardy___v0.3.pptx

What Makes Games Challenging?

Considerations on How to Determine the “Challenge”

Posed by an Exergame for BalanceTraining

Source: http://www.sycor-asia.com/opencms/as/products_services/complementary_services/Telecommunication/

Sandro Hardy, M.Sc.

[email protected]

mailto:[email protected]



KOM – Multimedia Communications Lab 2

Motivation

Today

Lack of exercise health risks [WHO10, EC10]

Sports reduce physical ailments [WHO10-1, ACSM11]

Challenge

Exergames have small effects [BBC+08, KW10]

Objective scientific methodologies [IQWiG11]

Health technology assessment (HTA)

Idea

Real-time “in game”- measurement

Real-time “In game”- adaptation

Image Sources: Child Playing Video Game: EinsLive, Femoral Neck Fracture: KH Schwarzach


RQ1: How can we

assure the motivation

of the players?

RQ2: How can we

increase the training

effects of Exergames?

Research Questions

RQ1 RQ2


State of the Art

Game Theory Attractivity [Malo82, YHL06]

Flow [Csik75], Gameflow [SW05]

Dual Flow [SHM07]

Elderly [GeSM10, GLN+12]

Sport Games Wii Fit, DDR [BBC+08]

Balance [KW10]

Interfaces & Sensors Cardio Training [SGYR09]

Gait Rehab, Balance [GHS+11]

Feedback [MHAE11]

Game Experience (abbr. GX) [Nac09]

Vital Parameters [SHM07, SGYR09]

Stroke Rehabilitation [AHJS09]

Dual Flow, [SHM07]


Software

• Gaming

• Training

Software

• Gaming

• Training

Vital Sensors

Physiology

• Vital Status

• Performance

Psychology

• Attractiveness

• Effectiveness

Hardware

• Electronics

• Mechanics

Sta

tic

Adap

tive

System User

Concept: Adaptation Model for Exergames

Publications: [HGG+11]

Adaption Fields

Hardware / Physis

Game Experience

Training

Software

Focus

Adaptive Software

Gaming

Training

Vital Status

http://www.kom.tu-darmstadt.de/research-results/publications/publications-details/publications/HGG+11-5/?no_cache=1


Act Modification Adaptation Analyze Measure

Training

UX

Physis

Training / Playing

• Enjoyment • Motivation • Fun

UX-Measure

• Questionnaire • Video Analysis • BCI / EEG

Data Analysis

• UX Calcul.

Game Adaptation

• Challenge • Curiosity • Arousal

Gameplay Modification

• Game Type • Graphics • Style

Training / Playing

• Strength • Movements

Data Analysis

• Initial Setup

Calculation

Training Settings

• Control

Parameters


• Hardware

Performance/ Test

• Force • Endurance

Training / Playing

• Vital

Parameters

Performance Sensors

• Power • Movement • Heart Rate

Data Analysis

• Intensity • Difficulty • Fitness

Training Adaptation

• Parameter

Adaptation


• Actuators • Game Objects • Game Control

Publications: [WH13]

Concept: Adaptation Layers

Act Modification Adaptation Measure

Training

Game Experi- ence

Physis/ Hardware

Training / Playing

• Enjoyment • Motivation • Fun

Game Experience

Measurement

• Questionnaire • Video Analysis • BCI / EEG

Data Analysis

• Game Experience

• Metrics

Game Adaptation

• Challenge • Curiosity • Arousal


• Game Type • Graphics • Style

Training / Playing


Data Analysis

• Initial Setup

Calculation

Training Settings

• Control

Parameters

Setting Modification

• Hardware

Performance Test


Training / Playing

• Vital

Parameters

Performance Measurement

• Power • Movement • Heart Rate

Data Analysis

• Intensity • Difficulty • Fitness

Training Adaptation

• Parameter

Adaptation


• Actuators • Game Objects • Game Control

http://www.kom.tu-darmstadt.de/research-results/publications/publications-details/publications/WH13-1/?no_cache=1


Concept: Conditions and Metric

Conditions

Game Experience Models

Training Plans

Metric

Keep Game Experience

Improve Training Effects

Concept

Adaptation

Training Guidelines

GX Theories

Training / Playing

UX Measure

Data Analysis

Game Adap- tation

Game- play

Modi- fication

Training / Playing

Perfor- mance

Sensors

Data Analysis

Training Adap- tation

Game- play Modi

fication

Training Load/

GX

Conditions Metric Potential


Concept: Formal Hypothesis

Training Guidelines

GX Theory

Performance Metrics

GX Assessment

Conditions Metric Interdependencies

Cardio Training Heart Rate …

Fall Prevention Stability …

Cardio Traing Interval …

Fall Prevention Balance …

Fall Risk

Health Status

UX Rating Fun …

Usage Freqency &

Duration

Dual Flow Attractivity …

Parameter Relation Output

Adaptation Measurement

a = Attributes of a single game

c, e, p = Effects of one attribute (cognitive, emotional, physical)

M = Motivational effects (positives affects, correlate with usage)

T = Training effects

Q = Quality (achievements of objectives)

𝑎𝑖 𝑐𝛽1, 𝑒𝛽2, 𝑝𝛽3 = 𝑀 ∗ 𝑇 = 𝑄

𝑛

𝑖=1


• GXQ • Mimic • EMG • EEG • GSR • Respiration • ECG • Kinect • ArToolkit • Move • Razor

Hydra • Balance

Board • VO2max • …

Concept: Parameters

Physis

Training / Playing


Data Analysis

• Initial Setup

Calculation

Training Settings

• Control

Parameters


• Hardware

Performance/ Test


Training Guidelines

GX Theory

Performance Metrics

GX Assessment






Fall Risk

Health Status

GX Rating Fun …

Usage Freqency &

Duration


• Flow • Challenge • Workload • Clear Goals • Focus • Single Task • Control • Empowerment • Immersion • Awareness

• Training

Load • Exertion • Range • Speed • Resistance • Reaction • …

• Emotional Feeling

• Cognitive Load

• Physical Performance

• …




Concept: Parameters

[Nacke]

[Masek]

[Hingston]

[Sinclair]

Physis

Training / Playing


Data Analysis

• Initial Setup

Calculation

Training Settings

• Control

Parameters


• Hardware

Performance/ Test


• GXQ

• Mimic • Iris • VO2max • EMG • EEG • GSR • Respiration • ECG • Kinect • ArToolkit • Move • Razor

Hydra • Balance

Board • …

Training Guidelines

GX Theory

Performance Metrics

GX Assessment






Fall Risk

Health Status

GX Rating Fun …

Usage Freqency &

Duration


• Clear Goals • Focus • Single Task • Feedback • Immersion

• Control

• Challenge Training Load

• Skills:

Flexibility Speed Strength Coordination Endurance


• Cognitive Load

• Training Success Physical Performance




A: Asset Configuration Configuration of objects and interactions

C: Control Adaptation Relation between physiological measurements and control of the game

D: Difficulty Adaptation Adaptation of the game environment

Estimation of user behavior

Concept: Adaptation & Personalization

• GXQ

• Mimic • Iris • VO2max • EMG • EEG • GSR • Respiration • ECG • Kinect • ArToolkit • Move • Razor

Hydra • Balance

Board • …

Interdependencies

• Clear Goals • Focus • Single Task • Feedback • Immersion

• Control

• Challenge Training Load

• Skills:

Flexibility Speed Strength Coordination Endurance


• Cognitive Load

• Training Success Physical Performance



A

C D


Object Types Player

Target

Enemy

Game Environment Obstacles

Design Graphics

Sound

Interaction Position/Speed

Physics/AI

Concept: Asset Configuration

x

y


Interaction Position/Movement

Physics/AI

Control Physiological Reaction

Sensor (S)

Signals (s) e.g. Heart Rate, Force, Speed

𝐴 = 𝐼 𝑠

Reactions e.g. Height (h), Speed, Acceleration

Concept: Control Adaptation

y

Time [s]

Sig

na

l

h

S

T

O

E

O

x,t

h1

h2


Game Objects Area (A)

Start (S) Player‘s Position

Target (T)

Enemies (E)

Obstacles (O)

Ground (G)

Distance (D)

Paths (P)

Game Structure

𝐴 =

𝑎00 ⋯ 𝑎𝑚0⋮ ⋱ ⋮𝑎0𝑛 ⋯ 𝑎𝑚𝑛

∀𝑎𝑚𝑛: 𝑎𝑚𝑛 ∈ 𝑆, 𝑇, 𝐸, 𝑂, 𝐺 ;𝑚, 𝑛 ∈ ℕ0 ∃! 𝑎𝑥𝑦: 𝑎𝑥𝑦 = 𝑆

∃! 𝑎𝑥𝑦: 𝑎𝑥𝑦 = 𝑇

𝐷 𝑎𝑥𝑦 , 𝑎𝑢,𝑣 = 𝑥 − 𝑢 2 + 𝑦 − 𝑣 2 𝑆𝑥𝑦 → 𝑆𝑥′𝑦′: (𝑥

′= 𝑥)∨(y′=y)

Strategies Shortest Path (P1)

Avoid Enemies (P2)

Easiest Path (P3)

Concept: Difficulty Adaption


Metrics Center of Pressure 𝐶𝑂𝑃 = 𝑥, 𝑦 = {

𝐹2 + 𝐹4 − (𝐹1 + 𝐹3)

∑𝐹𝑖;𝐹1 + 𝐹2 − (𝐹3 + 𝐹4)

∑𝐹𝑖}

Anterior-Posterior Stability Index 𝐴𝑃𝑆𝐼 =

∑ 0 − 𝑌 2

|𝑆𝑎𝑚𝑝𝑙𝑒𝑠|

Medio-Lateral Stability Index

𝑀𝐿𝑆𝐼 =∑ 0 − 𝑋 2


Overall Stability Index

𝑂𝑆𝐼 =∑ 0 − 𝑌 2 + ∑ 0 − 𝑋 2


Area of Sway 𝐴𝑆 = 𝑥𝑚𝑎𝑥 − 𝑥𝑚𝑖𝑛 ∗ (𝑦𝑚𝑎𝑥 − 𝑦𝑚𝑖𝑛)

Sway Path 𝑆𝑃 = ∑ 𝑥𝑖+1 − 𝑥𝑖

2 + 𝑦𝑖+1 − 𝑦𝑖 2 𝑛−1

𝑖=1

⇒ |𝑝 𝑖 − 𝑝 𝑖+1|

𝑛−1

𝑖=1

Difficulty Adaption: Balance Training


Prototype BalanceFit

Experience Low initial skills sensitivity

Low initial performance

Safety standing frame

Physical ailments

Visual accessibility

Risk of social exclusion

Long term usage

Publications: [GHS+11]

http://www.kom.tu-darmstadt.de/research-results/publications/publications-details/publications/GHS+11-1/?no_cache=1


BalanceFit: Asset Configuration

Hypothesis:

The emotional design of the

environment is reflected in ther

Game Experience Rating

Setting

Two games, same gameplay,

different emotional design. I.E.

friendly vs. melancholy.

Outcome

planned

prove effects for exergames


Hypothesis: New levels (curiosity) increase

perceived game experience

Setting 10 Level, 50 Level

Exclusion of simple levels (time limit)

Outcome Elderly are curious for new game

elements

Curiosity is important to keep a game challenging

How can we realize automatic difficulty adaptation?

BalanceFit: Difficulty Adaptation

Publications: [HGWS12], [HGS13]

http://www.kom.tu-darmstadt.de/research-results/publications/publications-details/publications/HGWS12-1/?no_cache=1

http://www.kom.tu-darmstadt.de/research-results/publications/publications-details/publications/HGS13-1/?no_cache=1


Experimental Setting

Hypothesis 1: Level features influence motivation.

Hypothesis 2: Level features influence the perceived difficulty.

Hypothesis 3: Level features influence performance.

Hypothesis 4: Goal setting influences motivation, perceived

difficulty and performance.

Independent Variables The length of the shortest path

The number of orientation changes

The number of bottlenecks

The length of open curbs


Experimental Setting

Dependent Variables Motivation (subjective) Motivation

Questionnaire (FAM, Rheinberg, Vollmeyer & Burns, 2001)

Performance (objective): needed time to reach the goal

Control Variables Subjectivly perceived level difficulty

(„subjective mental effort questionnaire“ SMEQ, Zijlstra & Van Doorn, 1986)

Change of affects after each single level („Self-Assessment Manikin“ SAM, Bradley & Lang, 1994)

Technology-Acceptance („Technology-Acceptance-Model“ TAM, Davis, 1985) n=47

Participants age: 18-42 Years (M=24,13, SD=5,48)

33 Students of Psychology, 9 Students of Psychology in IT


Motivation Difficulty Performance

Pre Post Pre Post

Shortest Path o o *** - **

Orientation

Changes *** ** *** *** ***

Bottlenecks o o o *** * (-)

Open Curbs ** o *** ** *

Results

*:= p<=.1; **:= p<=.01; ***:= p<=.001; o:= p>.01.


Criticis

Selected Levels Features and Definitions

Number of investigated levels

Characteristics of Levels

High Standard Deviation maybe an „individualized“ view is better

Discussion

26.11.2013| Aktueller Stand| Modul : FP1-1 | A. Kern, N. Kolb, A.-L. Meimbresse, S. Merkel, J. Schwan, V. Schochlow, H. L. Spieske | Seite 22


Assumption

Challenge is related to difficulty

Difficulty depends on the possible tracks

Dificulty depends on the selected tracks

Approaches

Teleological vs. Ontogenetic

Personalisation

Shortest path

Avoid holes

Prefer walls

Results

Complexity

Number of different levels

Difficulty precision

Maze Generation

Shortest Path Avoid Holes Prefer Walls

Teleological Ontogenetic


Number of Different Levels

Runtime Comparison

Evaluation: Difficulty Adaptation

0

1000

2000

3000

4000

5000

6000

7000

8000

9x9 17x9 17x17 25x17

Grid

Binary Space Partition

Room Combine

Set-Holes

Evolutionary

0

200

400

600

800

1000

1200

9x9 17x9 17x17 25x17

Grid

Binary SpacePartition

Room Combine

Set-Holes

Evolutionary

Method/Size 9x9 17x9 17x17 25x17

Grid <1 <1 <1 <1

BS Partition <1 <1 <1 <1

Room Combine - <1 <1 <1

Set-Holes 3 30 242 817

Evolutionary 332 1086 3554 7554

Method/Size 9x9 17x9 17x17 25x17

Grid 48 737 993 1000

BS Partition 18 264 988 1000

Room Combine 0 24 96 895

Set-Holes 534 995 1000 1000

Evolutionary 1000 1000 1000 1000


Difficulty Metrics

Loss rate depends on the selected path

Same values for instability (𝒑𝒘𝒂𝒍𝒍 = 𝟎. 𝟗𝟗, 𝒑𝒇𝒓𝒆𝒆 = 𝟎. 𝟓)

Path 1 (left): Loss rate 69.4%

Path 2 (center): Loss rate 26.5%

Path 3 (right): Loss rate 7.1%

𝑤𝑚𝑎𝑥 = 100, 𝑓𝑑𝑒𝑐𝑟 = 0.7, 𝑑𝑚𝑎𝑥 = 2

𝑤𝑚𝑎𝑥 = 100, 𝑓𝑑𝑒𝑐𝑟 = 0.7, 𝑑𝑚𝑎𝑥 = 2, 𝑓𝑤𝑎𝑙𝑙 = 0.1

No terrain weights


Questions & Contact


References 1

[ACSM11] American College of Sports Medicine. Quantity and Quality of Exercise for Developing and Maintaining Cardiorespiratory, Musculoskeletal, and Neuromotor Fitness in Apparently Healthy Adults: Guidance for Prescribing Exercise. Medicine and Science in Sports and Exercise, 43 (7), 1334- 1359.

[AHJS09] Atif Alamri, Heung-Nam Kim, Jongeun Cha, Abdulmotaleb El Saddik, Serious Games for Rehabilitation of Stroke Patients with Vibrotactile Feedback. International Journal of Computer Science in Sport, Volume 9/Special Issue, IACSS, 2009

[BBC+08] Kirk A. Brumels, Troy Blasius, Tyler Cortright, Daniel Oumedian, and Brent Solberg. Comparison of efficacy between traditional and video game based balance programs. Clinical Kinesiology: Journal of the American Kinesiotherapy Association, 62(4), December 2008.

[BBTB08] Tom Baranowski, Richard Buday, Debbe I. Thompson, and Janice Baranowski. Playing for real: Video games and stories for health-related behavior change. American Journal of Preventive Medicine, 34(1):74 – 82.e10, 2008.

[BHK+12] Michael Brach, Klaus Hauer, Oliver Korn, Robert Konrad, Sven Unkauf, Sandro Hardy, Stefan Göbel: Motivotion60+: Entwicklung eines computeranimierten Systems zum Kraft- und Balancetraining für Senioren. In: VDE-Verlag: AAL-Kongress 2012, Tagungsband, January 2012.

[BR08] Helmut Bonney and Joachim Rosenkranz. ADHS – Kritische Wissenschaft und therapeutische Kunst, chapter Training von Aufmerksamkeit und Impulskontrolle: Ein Baustein der multimodalen Behandlung von Grundschulkindern mit ADHS. Pilotstudie zur Prüfung der Anwendungseffekte einer Non-go-Lernsoftware, pages 228–244. Carl-Auer-Systeme Verlag, Heidelberg, 2008.

[Csik75] Mihaly Csikszentmihalyi : Beyond Boredom and Anxiety. Jossey-Bass: San Francisco, CA, 1975[EC10] European Commission. Special Eurobarometer 334 / Wave 72.3 – TNS Opinion & Social: Sport and Physical Activity. Technical report, March 2010.

[GeSM10] Gerling, K. M., Schild, J., & Masuch, M. (2010). Exergame Design for Elderly Users : The Case Study of SilverBalance, 66–69.

[GLN+12] Gerling, K. M., Livingston, I. J., Nacke, L. E., & Mandryk, R. L. (2012). Full-Body Motion-Based Game Interaction for Older Adults 1, 1873–1882.


References 2

[GGH13] Stefan Göbel, Michael Gutjahr, Sandro Hardy: Evaluation of Serious Games. In: Bredl, K. & Bösche, W. (ed. in preparation). , vol. Serious Digital Games, MUVE and MMORPG in Adult Education and Health Care: Research, Reviews, Case Studies, and Lessons Learned, IGI-Global, February 2013.

[GHS+11] Stefan Göbel, Sandro Hardy, Ralf Steinmetz, Jongeun Cha, Abdulmotaleb El Saddik: Serious Games zur Prävention und Rehabilitation. In: 4. Deutscher AAL-Kongress, 25.-26.01.2011 in Berlin: Demographischer Wandel - Assistenzsysteme aus der Forschung in den Markt, VDE Verlag GmbH, Berlin und Offenbach, January 2011. ISBN 978-3-8007-3323-1.

[GHW+10] Stefan Göbel, Sandro Hardy, Viktor Wendel, Florian Mehm, Ralf Steinmetz: Serious Games for Health - Personalized Exergames. In: Proceedings ACM Multimedia 2010, p. 1663-1666, October 2010. ISBN ISBN: 978-1-60558-933-6.

[HEGS11] Sandro Hardy, Abdulmotaleb El Saddik, Stefan Göbel, Ralf Steinmetz: Context Aware Serious Games Framework for Sport and Health. In: Medical Measurements and Applications Proceedings (MeMeA), 2011 IEEE International Workshop on, p. 248 -252, May 2011. ISBN 978-1-4244-9336-4.

[HGG+11] Sandro Hardy, Stefan Göbel, Michael Gutjahr, Josef Wiemeyer, Ralf Steinmetz:Adaptation Model for Indoor Exergames (accepted for publication). In: International Journal of Computer Science in Sport, Vol. 10, Special edition: Serious Games – Theory, Technology & Practice , November 2011.

[HGS13] Sandro Hardy, Stefan Göbel, Ralf Steinmetz: Adaptable and Personalized Game-based Training System for Fall Prevention (Technical Demo Paper). In: ACM New York, NY, USA ©2013 : MM '13 Proceedings of the 21st ACM international conference on Multimedia , p. 431-432 , October 2013. ISBN 978-1-4503-2404-5.

[HGWS12] Sandro Hardy, Stefan Göbel, Josef Wiemeyer, Ralf Steinmetz: Adaption und Personalisierung von digitalen spielerischen Trainingssystemen für die Sturzprävention. In: R. Byshko, T. Dahmen, M. Gratkowski, M. Gruber, J. Quintana, D. Saupe, M. Vieten, A. Woll: Sportinformatik 2012 : 9. Symposium der Sektion Sportinformatik der Deutschen Vereinigung für Sportwissenschaft vom 12.-14. Sept. 2012 in Konstanz : extended abstracts., KOPS Institutional Repository University of Konstanz, September 2012.

[HWHG14] Katrin Hoffmann, Josef Wiemeyer, Sandro Hardy, Stefan Göbel: Personalized adaptive control of training load in exergames from a sport-scientific perspective (accepted for publication). In: Gamedays2014, Proceedings of, Springer, LNCS, April 2014.

http://www.kom.tu-darmstadt.de/en/publications/publications-details/publications/GHS+11-1/?no_cache=1

http://www.kom.tu-darmstadt.de/en/kom-multimedia-communications-lab/people/staff/stefan-goebel/?no_cache=1


http://www.kom.tu-darmstadt.de/en/kom-multimedia-communications-lab/people/staff/sandro-hardy/?no_cache=1

http://www.kom.tu-darmstadt.de/en/kom-multimedia-communications-lab/people/staff/ralf-steinmetz/?no_cache=1







http://www.kom.tu-darmstadt.de/en/publications/publications-details/publications/GHW+10-1/?no_cache=1




http://www.kom.tu-darmstadt.de/en/kom-multimedia-communications-lab/people/staff/viktor-wendel/?no_cache=1


http://www.kom.tu-darmstadt.de/en/kom-multimedia-communications-lab/people/staff/florian-mehm/?no_cache=1








http://www.kom.tu-darmstadt.de/en/publications/publications-details/publications/HEGS11-1/?no_cache=1







http://www.kom.tu-darmstadt.de/en/publications/publications-details/publications/HGG+11-5/?no_cache=1













http://www.kom.tu-darmstadt.de/kom-multimedia-communications-lab/people/staff/sandro-hardy/?no_cache=1


http://www.kom.tu-darmstadt.de/kom-multimedia-communications-lab/people/staff/stefan-goebel/?no_cache=1

http://www.kom.tu-darmstadt.de/kom-multimedia-communications-lab/people/staff/ralf-steinmetz/?no_cache=1






















http://www.kom.tu-darmstadt.de/kom-multimedia-communications-lab/people/staff/ralf-steinmetz/?no_cache=1




http://www.kom.tu-darmstadt.de/research-results/publications/publications-details/publications/HWHG14-1/?no_cache=1

































References 3

[KCBP08] Pamela M. Kato, Steve W. Cole, Andrew S. Bradlyn, and Brad H. Pollock. A video game

improves behavioral outcomes in adolescents and young adults with cancer: A randomized trial.

Pediatrics, 122(2):305–317, 2008.

[KW10] Annika Kliem and Josef Wiemeyer. Comparison of a traditional and a video game based balance

training program. International Journal of Computer Science in Sport, 9/ Special Edition:80–91, 2010.

[Malo82] T. W. Malone, Heuristics for designing enjoyable user interfaces: Lessons from computer games.

In Proceedings of the 1982 conference on human factors in computing systems, Gaithersburg, Maryland,

United States, 1982.

[MHAE11] Kazi Masudul Alam, Sandro Hardy, Aysha Akther, Abdulmotaleb El Saddik: SMS Text Based

Affective Haptic Application. In: Proceedings of Virtual Reality International Conference (VRIC 2011),

April 2011.

[MHGS11] Florian Mehm, Sandro Hardy, Stefan Göbel, Ralf Steinmetz: Collaborative Authoring of Serious

Games for Health. In: MM '11 Proceedings of the 19th ACM international conference on Multimedia , p.

807-808, ACM New York, NY, USA, December 2011. ISBN 978-1-4503-0616-4.

[Nac09] Lennart Nacke, Scientific Measurement of User Experience in Interactive Entertainment Blekinge

Institute of Technology School of Computing., 2009

[SHM07] J. Sinclair, P. Hingston, and M. Masek: Considerations for the design of exergames. In

Proceedings of the 5th international Conference on Computer Graphics and interactive Techniques in

Australia and Southeast Asia (Perth, Australia, December 01 - 04, 2007). GRAPHITE '07. ACM, New

York, NY, 289-295, 2007.

http://www.kom.tu-darmstadt.de/en/publications/publications-details/publications/MHAE11-1/?no_cache=1




http://www.kom.tu-darmstadt.de/en/publications/publications-details/publications/MHGS11-1/?no_cache=1













References 4

[SGYR09] T. Stach, T. C. Graham, J. Yim, R. E. Rhodes:. Heart rate control of exercise video games. In Proceedings of Graphics interface 2009 (Kelowna, British Columbia, Canada, May 25 - 27, 2009). ACM International Conference Proceeding Series, vol. 324. Canadian Information Processing Society, Toronto, Ont., Canada, 125-132, 2009.

[SW05] P. Sweetser, P. Wyeth: GameFlow: A Model for Evaluating Player Enjoyment in Games, ACM Computers in Entertainment, Vol. 3, No. 3, July 2005.

[WH13] Josef Wiemeyer, Sandro Hardy: Serious Games and motor learning - concepts, evidence, technology. In: Bredl, K. & Bösche, W. (ed. in preparation), vol. Serious Digital Games, MUVE and MMORPG in Adult Education and Health Care: Research, Reviews, Case Studies, and Lessons Learned, chap. Serious Digital Games, MUVE and MMORPG in Adult Education and Health Care: Research, Reviews, Case Studies, and Lessons Learned, IGI-Global, February 2013.

[WHGS10] Viktor Wendel, Sandro Hardy, Stefan Göbel, Ralf Steinmetz: Adaption und Personalisierung von Exergames. In: J. Wiemeyer, D. Link, R. Angert, B. Holler, A. Kliem, N. Roznawski, D. Schöberl, M. Stroß: Sportinformatik trifft Sporttechnologie: Abstractband zur Tagung der dvs-Sektion Sportinformatik und der deutschen interdisziplinären Vereinigung für Sporttechnologie, p. 97-99, Institut für Sportwissenschaft der Technischen Universität Darmstadt, September 2010.

[WHO10] Carissa Etienne and Anarfi Asamoa-Baah. The world health report: health systems financing: the path to universal coverage. WHO Press, 2010.

[WHO10-1] World Health Organisation, Global Recommendations on Physical Activity for Health, SBN: 9789241599979, 210

[YHL06] G. N. Yannakakis, J. Hallam, H. H. Lund: Comparative Fun Analysis in the Innovative Playware Game Platform. In Proceedings of the 1st World Conference for Fun’n Games, Preston, England, 2006

http://www.kom.tu-darmstadt.de/en/publications/publications-details/publications/WHGS10-1/?no_cache=1







What Makes Games Challenging? · Exergames have small effects [BBC+08, KW10] Objective scientific...

Documents

Transcript of What Makes Games Challenging? · Exergames have small effects [BBC+08, KW10] Objective scientific...