gms | German Medical Science

64. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e. V. (GMDS)

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie

08. - 11.09.2019, Dortmund

A preliminary investigation into the relationship between athletes’ physical stress and strain parameters as a function of rally success in squash

Meeting Abstract

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  • Christopher Brumann - University of Applied Sciences and Arts Dortmund, Dept. of Computer Science, Dortmund, Germany
  • Markus Kukuk - University of Applied Sciences and Arts Dortmund, Dept. of Computer Science, Dortmund, Germany

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie. 64. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e.V. (GMDS). Dortmund, 08.-11.09.2019. Düsseldorf: German Medical Science GMS Publishing House; 2019. DocAbstr. 248

doi: 10.3205/19gmds016, urn:nbn:de:0183-19gmds0160

Veröffentlicht: 6. September 2019

© 2019 Brumann et al.
Dieser Artikel ist ein Open-Access-Artikel und steht unter den Lizenzbedingungen der Creative Commons Attribution 4.0 License (Namensnennung). Lizenz-Angaben siehe



Introduction: We regard sport in general as one aspect of an all-embracing healthy lifestyle and we aim at using computer science techniques to support training and competition. As the requirements vary according to the type of sport [1], [2], [3], [4], a structured training management system specifically for each type of sport is desired. In this work, we focus on the sport of squash due to its favorable constraints such as the compact playing field of about 62m² and the fact that there are usually only two athletes on the court at the same time. We measure and analyze the players’ stress and strain parameters in order to support the training process in a controlled and targeted manner.

Method: To measure physiological strain, we have developed a camera-based tracking system using computer vision techniques that allows us to calculate distances travelled on court by each player separately [5]. Contrary to other approaches which use a ceiling mounted camera [6], we use a static camera located behind the court which also serves as a calibration object. Our algorithm uses mixture-based Gaussian background subtraction with clustering for player detection and dominant color matching for identification.

The stress parameter is measured by using a chest strap (Zephyr Bioharness), worn continuously during the entire match. It writes reliable [7] heart rate (HR) measurements on an internal storage. The belt also contains an integrated acceleration sensor which is used to synchronize the HR with the video and thus with our tracking data.

After synchronization, the resulting data is then classified into rallies and breaks in-between rallies by manual scoring annotation. Afterwards the classified data parts are integrated into a relational database schema. During the integration, our data is further enriched with meta-information such as linear regression parameters for HR during individual phases. This allows us to query, filter and evaluate the recorded data up to the level of individual rallies.

Results: Currently, our dataset consists of two matches lasting 1593 and 1602 seconds (s). In total they consist of eight games [347s±186s] (mean±SD) with a total amount of 149 rallies [6.9s±4.4s]. Since we have two players for each rally, our rally table contains a total of 298 entries. The players’ covered distance during rallies is [348m±91.96m] per game. The pearson correlation (PCC) score for rally duration versus covered distance is 0.72. If we consider winning rallies only, a PCC for the rally's preceding HR regression gradient and covered distance shows a negative relation of -0.28, whereas for losing rallies the PCC is 0.03.

Discussion: Our current dataset with n=149 rallies is small and therefore does not allow any significant conclusions to be drawn about the interrelationship regarding the stress-strain model. However, initial analysis showed indications for further investigations. In addition to the necessary database expansion, an inclusion of further parameters such as respiration rate, is planned. This hopefully will lead us to a squash sport specific model for optimizing training on the one hand and provide assistance for a recreational healthy gameplay on the other.

The authors declare that they have no competing interests.

The authors declare that an ethics committee vote is not required.


Zagatto AM, Kondric M, Knechtle B, Nikolaidis PT, Sperlich B. Energetic demand and physical conditioning of table tennis players. A study review. Journal of Sports Sciences. 2017 Jun 5;36(7):724–31.
Stojanović E, Stojiljković N, Scanlan AT, Dalbo VJ, Berkelmans DM, Milanović Z. The Activity Demands and Physiological Responses Encountered During Basketball Match-Play: A Systematic Review. Sports Medicine. 2017 Oct 16;48(1):111–35.
Hausswirth C, Lehénaff D. Physiological Demands of Running During Long Distance Runs and Triathlons. Sports Medicine. 2001;31(9):679–89.
Smekal G, Von Duvillard SP, Rihacek C, Pokan R, Hofmann P, Baron R, et al. A physiological profile of tennis match play. Medicine and Science in Sports and Exercise. 2001 Jun;33(6):999–1005.
Brumann C, Kukuk M. Towards a better understanding of the overall health impact of the game of squash: automatic and high-resolution motion analysis from a single camera view. Current Directions in Biomedical Engineering. 2017;3(2):819-823. DOI: 10.1515/cdbme-2017-0189 Externer Link
Perš J, Vučković G, Kovačič S, Dežman B. A low-cost real-time tracker of live sport events. In: European Association for Signal Processing, et al., editors. Proceedings of the 2nd international symposium on image and signal processing and analysis in conjunction with 23 int'l conference on information technology interfaces, Pula, Croatia, June 19-21, 2001. Zagreb, Croatia: University Computing Center, University of Zagreb; 2001. p. 362-365.
Kim JH, Roberge R, Powell J, Shafer A, Jon Williams W. Measurement Accuracy of Heart Rate and Respiratory Rate during Graded Exercise and Sustained Exercise in the Heat Using the Zephyr BioHarnessTM. International Journal of Sports Medicine. 2012;34:497–501.