Come Fl. Run with Me: Understanding the Utilization of Drones to Support Recreational Runners' Well Being
Pages 700 - 705
Abstract
The utilization of drones to assist runners in real-time and post-run remains a promising yet unexplored field within human-drone interaction (HDI). Hence, in my doctoral research, I aim to delve into the concepts and relationships surrounding drones in the context of running, than focusing solely on one specific application. I plan on accomplishing this through a three-stage research plan: 1) investigate the feasibility of drones to support outdoor running research, 2) empathize with runners to assess their preferences and experiences running with drone, and 3) implement and test an interactive running with drone scenario. Each stage has specific objectives and research questions aimed at providing valuable insights into the utilization of drones to support runners. This paper outlines the work conducted during my Ph.D. research along with future plans, with the goal of advancing the knowledge in the field of runner drone interaction.
References
[1]
Fereshteh Amini, Khalad Hasan, Andrea Bunt, and Pourang Irani. 2017. Data representations for in-situ exploration of health and fitness data. In Proceedings of the 11th EAI International Conference on Pervasive Computing Technologies for Healthcare. ACM, New York, NY, USA, 163–172. https://doi.org/10.1145/3154862.3154879
[2]
Mauro Avila, Markus Funk, and Niels Henze. 2015. DroneNavigator: Using Drones for Navigating Visually Impaired Persons. In Proceedings of the 17th International ACM SIGACCESS Conference on Computers & Accessibility - ASSETS '15. Association for Computing Machinery, New York, NY, USA, 327–328. https://doi.org/10.1145/2700648.2811362
[3]
Christoph Bartneck, Dana Kulić, Elizabeth Croft, and Susana Zoghbi. 2008. Measurement Instruments for the Anthropomorphism, Animacy, Likeability, Perceived Intelligence, and Perceived Safety of Robots. International Journal of Social Robotics 1, 1 (Nov. 2008), 71–81. https://doi.org/10.1007/s12369-008-0001-3
[4]
Alisha Bevins and Brittany A. Duncan. 2021. Aerial Flight Paths for Communication: How Participants Perceive and Intend to Respond to Drone Movements. In Proceedings of the 2021 ACM/IEEE International Conference on Human-Robot Interaction (Boulder, CO, USA) (HRI ’21). Association for Computing Machinery, New York, NY, USA, 16–23. https://doi.org/10.1145/3434073.3444645
[5]
Julie Borgers, Bart Vanreusel, Steven Vos, Peter Forsberg, and Jeroen Scheerder. 2016. Do light sport facilities foster sports participation? A case study on the use of bark running tracks. International journal of sport policy and politics 8, 2 (2016), 287–304.
[6]
Virginia Braun and Victoria Clarke. 2021. Thematic Analysis: A Practical Guide (first ed.). SAGE Publications, Thousand Oaks, California, USA. https://uwe-repository.worktribe.com/output/9004204
[7]
Zhe Cao, Gines Hidalgo, Tomas Simon, Shih-En Wei, and Yaser Sheikh. 2021. OpenPose: Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields. IEEE Transactions on Pattern Analysis and Machine Intelligence 43, 1 (Jan. 2021), 172–186. https://doi.org/10.1109/tpami.2019.2929257
[8]
Jessica R. Cauchard, Jane L. E, Kevin Y. Zhai, and James A. Landay. 2015. Drone & Me: An Exploration into Natural Human-Drone Interaction. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing (Osaka, Japan) (UbiComp ’15). Association for Computing Machinery, New York, NY, USA, 361–365. https://doi.org/10.1145/2750858.2805823
[9]
Jessica R. Cauchard, Alex Tamkin, Cheng Yao Wang, Luke Vink, Michelle Park, Tommy Fang, and James A. Landay. 2019. Drone.io: A Gestural and Visual Interface for Human-Drone Interaction. In 2019 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI). IEEE, New York, NY, USA, 153–162. https://doi.org/10.1109/hri.2019.8673011
[10]
Shannon E. Clark and Diane M. Ste-Marie. 2007. The impact of self-as-a-model interventions on children's self-regulation of learning and swimming performance. Journal of Sports Sciences 25, 5 (March 2007), 577–586. https://doi.org/10.1080/02640410600947090
[11]
Christian A Clermont, Linda Duffett-Leger, Blayne A Hettinga, and Reed Ferber. 2020. Runners’ perspectives on ‘smart’wearable technology and its use for preventing injury. International Journal of Human–Computer Interaction 36, 1 (2020), 31–40.
[12]
Jane L. E, Ilene L. E, James A. Landay, and Jessica R. Cauchard. 2017. Drone & Wo: Cultural Influences on Human-Drone Interaction Techniques. In Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems (Denver, Colorado, USA) (CHI ’17). Association for Computing Machinery, New York, NY, USA, 6794–6799. https://doi.org/10.1145/3025453.3025755
[13]
RM Eime, N Sawyer, JT Harvey, MM Casey, Hans Westerbeek, and WR Payne. 2015. Integrating public health and sport management: Sport participation trends 2001–2010. Sport management review 18, 2 (2015), 207–217.
[14]
Hao-Shu Fang, Shuqin Xie, Yu-Wing Tai, and Cewu Lu. 2017. RMPE: Regional Multi-person Pose Estimation. In ICCV. IEEE, New York, NY, USA.
[15]
Karl B Fields, Jeannie C Sykes, Katherine M Walker, and Jonathan C Jackson. 2010. Prevention of running injuries. Current sports medicine reports 9, 3 (2010), 176–182.
[16]
Mafalda Gamboa, Mehmet Aydın Baytaş, Sjoerd Hendriks, and Sara Ljungblad. 2023. Wisp: Drones as Companions for Breathing. In Proceedings of the Seventeenth International Conference on Tangible, Embedded, and Embodied Interaction. Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/3569009.3572740
[17]
Antonio Gomes, Calvin Rubens, Sean Braley, and Roel Vertegaal. 2016. BitDrones: Towards Using 3D Nanocopter Displays as Interactive Self-Levitating Programmable Matter. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery, New York, NY, USA, 770–780. https://doi.org/10.1145/2858036.2858519
[18]
Eberhard Graether and Florian Mueller. 2012. Joggobot: a flying robot as jogging companion. In CHI '12 Extended Abstracts on Human Factors in Computing Systems. Association for Computing Machinery, New York, NY, USA, 1063–1066. https://doi.org/10.1145/2212776.2212386
[19]
Viviane Herdel, Lee J. Yamin, and Jessica R. Cauchard. 2022. Above and Beyond: A Scoping Review of Domains and Applications for Human-Drone Interaction. In CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/3491102.3501881
[20]
Keita Higuchi, Tetsuro Shimada, and Jun Rekimoto. 2011. Flying sports assistant: external visual imagery representation for sports training. In Proceedings of the 2nd Augmented Human International Conference. Association for Computing Machinery, New York, NY, USA. https://doi.org/10.1145/1959826.1959833
[21]
Bas Van Hooren, Jos Goudsmit, Juan Restrepo, and Steven Vos. 2019. Real-time feedback by wearables in running: Current approaches, challenges and suggestions for improvements. Journal of Sports Sciences 38, 2 (Dec. 2019), 214–230. https://doi.org/10.1080/02640414.2019.1690960
[22]
Eldar Insafutdinov, Leonid Pishchulin, Bjoern Andres, Mykhaylo Andriluka, and Bernt Schiele. 2016. DeeperCut: A Deeper, Stronger, and Faster Multi-person Pose Estimation Model. In Computer Vision – ECCV 2016. Springer International Publishing, New York, NY, USA, 34–50. https://doi.org/10.1007/978-3-319-46466-4_3
[23]
Mark A. Janssen, Carine Lallemand, Kevin Hoes, and Steven B. Vos. 2020. Which app to choose? An online tool that supports the decision-making process of recreational runners to choose an app. In Design 4 Health (1 ed.)(Design4Health, Vol. 2). Lab4Living, Sheffield Hallam University, Sheffield, UK, 32–38.
[24]
Mark Janssen, Jeroen Scheerder, Erik Thibaut, Aarnout Brombacher, and Steven Vos. 2017. Who uses running apps and sports watches? Determinants and consumer profiles of event runners’ usage of running-related smartphone applications and sports watches. PloS one 12, 7 (2017), e0181167.
[25]
John Chris Jones. 1992. Design Methods. John Wiley & Sons, Hoboken, NJ, USA.
[26]
Łukasz Kidziński, Bryan Yang, Jennifer L. Hicks, Apoorva Rajagopal, Scott L. Delp, and Michael H. Schwartz. 2020. Deep neural networks enable quantitative movement analysis using single-camera videos. Nature Communications 11, 1 (Aug. 2020). https://doi.org/10.1038/s41467-020-17807-z
[27]
Jane Lessiter, Jonathan Freeman, Edmund Keogh, and Jules Davidoff. 2001. A Cross-Media Presence Questionnaire: The ITC-Sense of Presence Inventory. Presence: Teleoperators and Virtual Environments 10, 3 (June 2001), 282–297. https://doi.org/10.1162/105474601300343612
[28]
Alexander Mathis, Pranav Mamidanna, Kevin M. Cury, Taiga Abe, Venkatesh N. Murthy, Mackenzie Weygandt Mathis, and Matthias Bethge. 2018. DeepLabCut: markerless pose estimation of user-defined body parts with deep learning. Nature Neuroscience 21, 9 (Aug. 2018), 1281–1289. https://doi.org/10.1038/s41593-018-0209-y
[29]
Florian 'Floyd' Mueller and Matthew Muirhead. 2015. Jogging with a Quadcopter. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems. Association for Computing Machinery, New York, NY, USA, 2023–2032. https://doi.org/10.1145/2702123.2702472
[30]
Flora Panteli, Charilaos Tsolakis, Dimitris Efthimiou, and Athanasia Smirniotou. 2013. Acquisition of the Long Jump Skill, Using Different Learning Techniques. The Sport Psychologist 27, 1 (March 2013), 40–52. https://doi.org/10.1123/tsp.27.1.40
[31]
Luc G. Pelletier, Meredith A. Rocchi, Robert J. Vallerand, Edward L. Deci, and Richard M. Ryan. 2013. Validation of the revised sport motivation scale (SMS-II). Psychology of Sport and Exercise 14, 3 (May 2013), 329–341. https://doi.org/10.1016/j.psychsport.2012.12.002
[32]
Monika Pobiruchin, Julian Suleder, Richard Zowalla, and Martin Wiesner. 2017. Accuracy and Adoption of Wearable Technology Used by Active Citizens: A Marathon Event Field Study. JMIR mHealth and uHealth 5, 2 (Feb. 2017), e24. https://doi.org/10.2196/mhealth.6395
[33]
Dees B.W. Postma, Robby W. van Delden, Jeroen H. Koekoek, Wytse W. Walinga, Ivo M. van Hilvoorde, Bert Jan F. van Beijnum, Fahim A. Salim, and Dennis Reidsma. 2022. A Design Space of Sports Interaction Technology. Foundations and Trends®in Human–Computer Interaction 15, 2-3 (2022), 132–316. https://doi.org/10.1561/1100000087
[34]
Andrzej Romanowski, Sven Mayer, Lars Lischke, Krzysztof Grudzień, Tomasz Jaworski, Izabela Perenc, Przemysław Kucharski, Mohammad Obaid, Tomasz Kosizski, and Paweł W. Wozniak. 2017. Towards Supporting Remote Cheering during Running Races with Drone Technology. In Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems. Association for Computing Machinery, New York, NY, USA, 2867–2874. https://doi.org/10.1145/3027063.3053218
[35]
James F. Sallis, William L. Haskell, Peter D. Wood, Stephen P. Fortmann, Todd Rogers, Steven N. Blair, and Ralph S. Paffenbarger. 1985. Physical Activity Assessment Methodology in the Five-City Project. American Journal of Epidemiology 121, 1 (Jan. 1985), 91–106. https://doi.org/10.1093/oxfordjournals.aje.a113987
[36]
Jeroen Scheerder, Koen Breedveld, and Julie Borgers. 2015. Running across Europe: the rise and size of one of the largest sport markets. Springer, New York, NY, USA.
[37]
Matthias Seuter, Eduardo Rodriguez Macrillante, Gernot Bauer, and Christian Kray. 2018. Running with drones: desired services and control gestures. In Proceedings of the 30th Australian Conference on Computer-Human Interaction. Association for Computing Machinery, New York, NY, USA, 384–395. https://doi.org/10.1145/3292147.3292156
[38]
Richard Shipway and Immy Holloway. 2010. Running free: Embracing a healthy lifestyle through distance running. Perspectives in public health 130, 6 (2010), 270–276.
[39]
Jan Stenum, Cristina Rossi, and Ryan T. Roemmich. 2021. Two-dimensional video-based analysis of human gait using pose estimation. PLOS Computational Biology 17, 4 (April 2021), e1008935. https://doi.org/10.1371/journal.pcbi.1008935
[40]
Melanie Swan. 2013. The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery. Big Data 1, 2 (June 2013), 85–99. https://doi.org/10.1089/big.2012.0002
[41]
Dante Tezza and Marvin Andujar. 2019. The State-of-the-Art of Human–Drone Interaction: A Survey. IEEE Access 7 (2019), 167438–167454. https://doi.org/10.1109/access.2019.2953900
[42]
Alexander Toshev and Christian Szegedy. 2014. DeepPose: Human Pose Estimation via Deep Neural Networks. In 2014 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, New York, NY, USA. https://doi.org/10.1109/cvpr.2014.214
[43]
Tsai-Hsuan Tsai, Yung-Sheng Chang, Hsien-Tsung Chang, and Yu-Wen Lin. 2021. Running on a social exercise platform: Applying self-determination theory to increase motivation to participate in a sporting event. Computers in Human Behavior 114 (Jan. 2021), 106523. https://doi.org/10.1016/j.chb.2020.106523
[44]
Norbert Tuśnio and Wojciech Wróblewski. 2021. The Efficiency of Drones Usage for Safety and Rescue Operations in an Open Area: A Case from Poland. Sustainability 14, 1 (Dec. 2021), 327. https://doi.org/10.3390/su14010327
[45]
Scott D. Uhlrich, Antoine Falisse, Łukasz Kidziński, Julie Muccini, Michael Ko, Akshay S. Chaudhari, Jennifer L. Hicks, and Scott L. Delp. 2022. OpenCap: 3D human movement dynamics from smartphone videos. Stanford 1, 1 (July 2022). https://doi.org/10.1101/2022.07.07.499061
[46]
Richard W. Willy. 2018. Innovations and pitfalls in the use of wearable devices in the prevention and rehabilitation of running related injuries. Physical Therapy in Sport 29 (Jan. 2018), 26–33. https://doi.org/10.1016/j.ptsp.2017.10.003
[47]
Anna Wojciechowska, Jeremy Frey, Esther Mandelblum, Yair Amichai-Hamburger, and Jessica R. Cauchard. 2019. Designing Drones: Factors and Characteristics Influencing the Perception of Flying Robots. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 3, 3 (Sept. 2019), 1–19. https://doi.org/10.1145/3351269
[48]
Alexander Yeh, Photchara Ratsamee, Kiyoshi Kiyokawa, Yuki Uranishi, Tomohiro Mashita, Haruo Takemura, Morten Fjeld, and Mohammad Obaid. 2017. Exploring Proxemics for Human-Drone Interaction. In Proceedings of the 5th International Conference on Human Agent Interaction (Bielefeld, Germany) (HAI ’17). Association for Computing Machinery, New York, NY, USA, 81–88. https://doi.org/10.1145/3125739.3125773
[49]
Chih-Hung Yu, Cheng-Chih Wu, Jye-Shyan Wang, Hou-Yu Chen, and Yu-Tzu Lin. 2020. Learning Tennis through Video-based Reflective Learning by Using Motion-Tracking Sensors. Journal of Educational Technology & Society 23, 1 (2020), 64–77. https://www.jstor.org/stable/26915407
[50]
Amir Zadeh, David Taylor, Margaret Bertsos, Timothy Tillman, Nasim Nosoudi, and Scott Bruce. 2020. Predicting Sports Injuries with Wearable Technology and Data Analysis. Information Systems Frontiers 23, 4 (May 2020), 1023–1037. https://doi.org/10.1007/s10796-020-10018-3
[51]
Sergej G. Zwaan and Emilia I. Barakova. 2016. Boxing against drones: Drones in sports education. In Proceedings of the The 15th International Conference on Interaction Design and Children. Association for Computing Machinery, New York, NY, USA, 607–612. https://doi.org/10.1145/2930674.2935991
Index Terms
- Come Fl.. Run with Me: Understanding the Utilization of Drones to Support Recreational Runners' Well Being
Recommendations
Drone-Driven Running: Exploring the Opportunities for Drones to Support Running Well-being through a Review of Running and Drone Interaction Technologies
HAI '23: Proceedings of the 11th International Conference on Human-Agent InteractionThere is an underexplored interaction space for drones that can be utilised as running interaction technology, distinct from human drone interaction that warrants foregrounding. This paper consolidates the current state of art in running interaction ...
Exploring Runners' Preferences of Drone Based Feedback to Support their Well-Being
CHIGREECE '23: Proceedings of the 2nd International Conference of the ACM Greek SIGCHI ChapterWhile drones are frequently used to capture aerial footage of runners, their utilization to enhance well-being of runners through real time feedback has not been fully explored. In this paper we investigate runners’ feedback preferences regarding drone-...
Face Recognition on Drones: Issues and Limitations
DroNet '15: Proceedings of the First Workshop on Micro Aerial Vehicle Networks, Systems, and Applications for Civilian UseDrones, as known as unmanned aerial vehicles (UAV), are aircrafts which can perform autonomous pilot. They can easily reach locations which are too difficult to reach or dangerous for human beings and collect images from bird's-eye view through aerial ...
Comments
Please enable JavaScript to view thecomments powered by Disqus.Information & Contributors
Information
Published In
October 2023
858 pages
ISBN:9798400700552
DOI:10.1145/3577190
Copyright © 2023 Owner/Author.
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.
Sponsors
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 09 October 2023
Check for updates
Author Tags
Qualifiers
- Extended-abstract
- Research
- Refereed limited
Funding Sources
- University of Twente EEMCS
Conference
ICMI '23
Sponsor:
Acceptance Rates
Overall Acceptance Rate 453 of 1,080 submissions, 42%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 56Total Downloads
- Downloads (Last 12 months)30
- Downloads (Last 6 weeks)2
Reflects downloads up to 06 Jan 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format