Abstract
Thanks to wearable sensor technologies, it has become feasible to quantify human kinematics cheaply and comprehensively during sports. However, it is often left to the user to infer any qualitative information from the data, leaving them confused about their performance and what actions to take next. This paper presents a high-level process to transform sensor data into immediate expert feedback in the form of coaching instructions. Individual aspects of process and software design are discussed based on an example implementation for Alpine skiing. In detail, this paper aims to (1) describe the transformation from raw sensor data into coaching instructions from a software engineering and data-centric perspective; (2) propose a high-level software design for coaching applications in sports that facilitates historical as well as immediate data analytics; (3) decompose the task of develo** coaching applications into independent, manageable research subtasks; and (4) show software engineers which data structures and interactions to implement.
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References
Bondaronek, P., Alkhaldi, G., Slee, A., Hamilton, F.L., Murray, E.: Quality of publicly available physical activity apps: Review and content analysis. JMIR mHealth uHealth 6(3), e53 (2018)
Kranz, M., Möller, A., Hammerla, N., Diewald, S.: The mobile fitness coach: towards individualized skill assessment using personalized mobile devices. Pervasive Mob. Comput. 9(2), 203–215 (2012)
Roggen, D., Magnenat, S., Waibel, M., Troester, G.: Wearable computing - designing and sharing activity-recognition systems across platforms. IEEE Robot. Autom. Mag 18(2) (2011)
Yu, G., Jang, Y.J., Kim, J., Kim, J.H., Kim, H.Y., Kim, K., Panday, S.B.: Potential of IMU sensors in performance analysis of professional alpine skiers. Sensors 16(4), 463 (2016)
Fasel, B., Spörri, J., Schütz, P., Lorenzetti, S., Aminian, K.: An inertial sensor-based method for estimating the athlete’s relative joint center positions and center of mass kinematics in alpine ski racing. Front. Physiol. 8, 850 (2017)
Martinez, A., Jahnel, R., Buchecker, M., Snyder, C., Brunauer, R., Stöggl, T.: Development of an automatic alpine skiing turn detection algorithm based on a simple sensor setup. Sensors 19(4), 902 (2019)
Cugola, G., Margara, A.: Processing flows of information: From data stream to complex event processing. J. ACM Comput. Surv. 44(3), 1–62 (2012)
Gamma, E.: Design Patterns: Elements of Reusable Object-Oriented Software. Pearson Education (1995)
ReactiveX. http://reactivex.io. Accessed 22 Mar 2019
Maglie, A.: ReactiveX and RxJava. In: Reactive Java Programming, Apress (2016)
Acknowledgements
This work was partly funded by the Austrian Federal Ministry for Transport, Innovation and Technology, the Austrian Federal Ministry for Digital and Economic Affairs, and the federal state of Salzburg.
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Brunauer, R., Kremser, W., Stöggl, T. (2020). From Sensor Data to Coaching in Alpine Skiing – A Software Design to Facilitate Immediate Feedback in Sports. In: Lames, M., Danilov, A., Timme, E., Vassilevski, Y. (eds) Proceedings of the 12th International Symposium on Computer Science in Sport (IACSS 2019). IACSS 2019. Advances in Intelligent Systems and Computing, vol 1028. Springer, Cham. https://doi.org/10.1007/978-3-030-35048-2_11
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DOI: https://doi.org/10.1007/978-3-030-35048-2_11
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