research-article

Everywhere run: a virtual personal trainer for supporting people in their running activity

Authors:

Fabrizio Mulas,

Salvatore Carta,

Paolo Pilloni,

Matteo MancaAuthors Info & Claims

ACE '11: Proceedings of the 8th International Conference on Advances in Computer Entertainment Technology

Article No.: 70, Pages 1 - 2

https://doi.org/10.1145/2071423.2071510

Published: 08 November 2011 Publication History

Get Access

Abstract

In the last years many medical researches have reported an increase of health problems in developed countries, mostly related to a sedentary lifestyle (as obesity and linked pathologies like diabetes and cardiovascular diseases). As a consequence. many research efforts have been carried out for finding strategies for motivating people to exercise regularly.

In this paper we present an Android-based mobile application, called Everywhere Run [1], that aims at motivating and supporting people during their running activities, behaving as a virtual personal trainer. Everywhere Run fosters the interaction between users and real personal trainers, in order to make it easy to non expert people to start working out in a healthy and safe way.

References

[1]

Everywhererun. http://www.everywhererun.com.

Google Scholar

[2]

F. Buttussi and L. Chittaro. Smarter phones for healthier lifestyles: An adaptive fitness game. IEEE Pervasive Computing, 9(4), 2010.

Digital Library

Google Scholar

[3]

F. Buttussi, L. Chittaro, and D. Nadalutti. Bringing mobile guides and fitness activities together: a solution based on an embodied virtual trainer. MobileHCI, 2006.

Digital Library

Google Scholar

[4]

S. Consolvo, K. Everitt, I. Smith, and J. A. Landay. Design requirements for technologies that encourage physical activity. Proceedings of the SIGCHL., 2006.

Digital Library

Google Scholar

[5]

R. de Oliveira and N. O. TripleBeat. Enhancing exercise performance with persuasion. MobileHCI, 2008.

Digital Library

Google Scholar

[6]

C. Jayant and T. S. Saponas. Mariofit: Exercise through mobile entertainment. 2005.

Google Scholar

[7]

J. Nielsen and T. Landauer. A mathematical model of the finding of usability problems. INTERCHI, 1993.

Digital Library

Google Scholar

[8]

T. Toscos. A. Faber, S. An, and M. P. Gandhi. Chick clique: persuasive technology to motivate teenage girls to exercise. CHI'06, 2006.

Digital Library

Google Scholar

[9]

R. Virzi. Refining the test phase of usability evaluation How many subjects is enough? Human Factors, 1992.

Digital Library

Google Scholar

Cited By

View all

Kashanj SWang XPerin C(2024)Visualizations on Smart Watches while Running: It Actually Helps!2024 IEEE Visualization and Visual Analytics (VIS)10.1109/VIS55277.2024.00016(41-45)Online publication date: 13-Oct-2024
https://doi.org/10.1109/VIS55277.2024.00016
Ivanova IWald M(2023)Recommender Systems for Outdoor Adventure Tourism Sports: Hiking, Running and ClimbingHuman-Centric Intelligent Systems10.1007/s44230-023-00033-33:3(344-365)Online publication date: 18-Jul-2023
https://doi.org/10.1007/s44230-023-00033-3
Clashing CSmith IMontoya MPatibanda RAnanthanarayan SPell SMueller F(2022)Going into Depth: Learning from a Survey of Interactive Designs for Aquatic RecreationProceedings of the 2022 ACM Designing Interactive Systems Conference10.1145/3532106.3533543(1119-1132)Online publication date: 13-Jun-2022
https://dl.acm.org/doi/10.1145/3532106.3533543
Show More Cited By

Index Terms

Everywhere run: a virtual personal trainer for supporting people in their running activity
1. Applied computing
  1. Computers in other domains
    1. Personal computers and PC applications

Recommendations

An e-coaching ecosystem: design and effectiveness analysis of the engagement of remote coaching on athletes

Nowadays, the use of mobile applications and wearable technologies to support and encourage an active lifestyle has become widespread. Several studies put in evidence that the usage of these kinds of support has to be monitored by high-qualified figures,...
Exploring Motivations of Young Adults to Participate in Physical Activities
CHI EA '15: Proceedings of the 33rd Annual ACM Conference Extended Abstracts on Human Factors in Computing Systems

This paper investigates the motivations of young adults aged 18 to 24 years to participate in physical activities and how technology might best support this motivation. Motivational factors were studied through contextual interviews, an adapted cultural ...
Flow is Not Enough: Understanding the Needs of Advanced Amateur Runners to Design Motivation Technology
CHI '15: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems

Motivation studies on running are often focused on how to convince non-runners to run, mainly through designing for extrinsic motivations such as health concerns or external reward systems. In contrast, we conducted a structured inquiry into ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

ACE '11: Proceedings of the 8th International Conference on Advances in Computer Entertainment Technology

November 2011

562 pages

ISBN:9781450308274

DOI:10.1145/2071423

General Chairs:
Teresa Romão
FCT/UNL, Portugal
,
Nuno Correia
FCT/UNL, Portugal
,
Masahiko Inami
Keio University, Japan
,
Organizing Chairs:
Eduardo Dias
FCT/UNL, Portugal
,
Teresa Chambel
FC/UL, Portugal
,
Program Chairs:
Hirokasu Kato
Nara Institute of Science and Technology, Japan
,
Rui Prada
Instituto Superior Técnico/UTL and INESC-ID, Portugal
,
Tsutomu Terada
Kobe University, Japan

In-Cooperation

SIGCHI: ACM Special Interest Group on Computer-Human Interaction

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 November 2011

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

ACE '11

Sponsor:

ACE '11: International Conference on Advances in Computer Entertainment Technology

November 8 - 11, 2011

Lisbon, Portugal

Acceptance Rates

Overall Acceptance Rate 36 of 90 submissions, 40%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

18
Total Citations
View Citations
514
Total Downloads

Downloads (Last 12 months)13
Downloads (Last 6 weeks)2

Reflects downloads up to 30 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Kashanj SWang XPerin C(2024)Visualizations on Smart Watches while Running: It Actually Helps!2024 IEEE Visualization and Visual Analytics (VIS)10.1109/VIS55277.2024.00016(41-45)Online publication date: 13-Oct-2024
https://doi.org/10.1109/VIS55277.2024.00016
Ivanova IWald M(2023)Recommender Systems for Outdoor Adventure Tourism Sports: Hiking, Running and ClimbingHuman-Centric Intelligent Systems10.1007/s44230-023-00033-33:3(344-365)Online publication date: 18-Jul-2023
https://doi.org/10.1007/s44230-023-00033-3
Clashing CSmith IMontoya MPatibanda RAnanthanarayan SPell SMueller F(2022)Going into Depth: Learning from a Survey of Interactive Designs for Aquatic RecreationProceedings of the 2022 ACM Designing Interactive Systems Conference10.1145/3532106.3533543(1119-1132)Online publication date: 13-Jun-2022
https://dl.acm.org/doi/10.1145/3532106.3533543
Feely CCaulfield BLawlor ASmyth B(2022)An Extended Case-Based Approach to Race-Time Prediction for Recreational Marathon RunnersCase-Based Reasoning Research and Development10.1007/978-3-031-14923-8_22(335-349)Online publication date: 12-Sep-2022
https://dl.acm.org/doi/10.1007/978-3-031-14923-8_22
Smyth BLawlor ABerndsen JFeely C(2021)Recommendations for marathon runners: on the application of recommender systems and machine learning to support recreational marathon runnersUser Modeling and User-Adapted Interaction10.1007/s11257-021-09299-332:5(787-838)Online publication date: 18-Aug-2021
https://doi.org/10.1007/s11257-021-09299-3
Silacci ATaiar RCaon M(2020)Towards an AI-Based Tailored Training Planning for Road Cyclists: A Case StudyApplied Sciences10.3390/app1101031311:1(313)Online publication date: 30-Dec-2020
https://doi.org/10.3390/app11010313
Feely CCaulfield BLawlor ASmyth B(2020)Providing Explainable Race-Time Predictions and Training Plan Recommendations to Marathon RunnersProceedings of the 14th ACM Conference on Recommender Systems10.1145/3383313.3412220(539-544)Online publication date: 22-Sep-2020
https://dl.acm.org/doi/10.1145/3383313.3412220
Strohmeier PGüngör SHerres LGudea DFruchard BSteimle JIqbal SMacLean KChevalier FMueller S(2020)bARefootProceedings of the 33rd Annual ACM Symposium on User Interface Software and Technology10.1145/3379337.3415828(579-593)Online publication date: 20-Oct-2020
https://dl.acm.org/doi/10.1145/3379337.3415828
Monteiro-Guerra FRivera-Romero OFernandez-Luque LCaulfield B(2020)Personalization in Real-Time Physical Activity Coaching Using Mobile Applications: A Scoping ReviewIEEE Journal of Biomedical and Health Informatics10.1109/JBHI.2019.294724324:6(1738-1751)Online publication date: Jun-2020
https://doi.org/10.1109/JBHI.2019.2947243
Feely CCaulfield BLawlor ASmyth B(2020)Using Case-Based Reasoning to Predict Marathon Performance and Recommend Tailored Training PlansCase-Based Reasoning Research and Development10.1007/978-3-030-58342-2_5(67-81)Online publication date: 3-Oct-2020
https://doi.org/10.1007/978-3-030-58342-2_5
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

An e-coaching ecosystem: design and effectiveness analysis of the engagement of remote coaching on athletes

Exploring Motivations of Young Adults to Participate in Physical Activities

Flow is Not Enough: Understanding the Needs of Advanced Amateur Runners to Design Motivation Technology