11th International Scientific Conference Technics, Informatics and Education – TIE 2026, (pp. 261-265)
AUTOR(I) / AUTHOR(S): Uroš Pešović 
, Slađana Đurašević Pešović , Saša Cvetković and Pietro Picerno
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DOI: https://doi.org/10.46793/TIE26.261P
SAŽETAK /ABSTRACT:
Cycling cadence or pedaling rate is one of the most commonly used parameters for analyzing cycling performance. This paper presents a smartphone-based application which measures the cycling cadence using the wireless internal measurement unit (IMU) sensor. Cadence is measured from the foot acceleration using a CC2650 SensorTag wireless sensor placed onto the rider’s ankle and acquired data is transmitted to a smartphone via Bluetooth Low Energy (BLE). A custom experiment for Phyphox smartphone-based application is developed which provides data acquisition and visualization, peak detection, and cadence estimation. Proposed system provides a simple and low-cost solution for real-time cadence monitoring and storage of acceleration data for offline analysis and is suitable for educational demonstrations, preliminary cycling analysis, and further development of smartphone-based cycling measurement systems.
KLJUČNE REČI / KEYWORDS:
cycling cadence; inertial measurement unit; accelerometer; Bluetooth Low Energy; Phyphox
PROJEKAT / ACKNOWLEDGEMENT:
The presented research is the result from cooperation on Serbia – Italy bilateral researcher’s mobility project for the period 2025-2027 titled „Smartphone-based motion tracking system for the self-assessment of motor capacity“. This study was supported by the Ministry of Science, Technological Development and Innovation of the Republic of Serbia, and these results are parts of the Grant No. 451-03-34/2026-03/ 200132, with University of Kragujevac – Faculty of Technical Sciences Čačak.
LITERATURA / REFERENCES:
- Charvatova, H., Prochazka, A., Vysata, O., Suarez-Araujo, C. P., & Smith, J. H. (2021). Evaluation of Accelerometric and Cycling Cadence Data for Motion Monitoring. IEEE Access, 9, 129256–129263.
DOI: 1109/access.2021.3111323 - Stancin S., Tomažic S. (2015). Smartphone MEMS Accelerometer for Cycling. ICIST 2015 5th International Conference on Information Society and Technology, 200–203, March 8-11 2015, Kopaonik, Serbia.
- Farjadian A. B., Kong Q., Gade V. K., Deutsch J. E. & Mavroidis C. (2013). VRACK: Measuring Pedal Kinematics During Stationary Bike Cycling. 2013 IEEE International Conference on Rehabilitation Robotics, June 24-26. 2013, Seattle, Washington, USA.
- Okugawa R., Murao K., Terada T. & Tsukamoto M. (2015). Training system of bicycle pedaling using auditory feedback. 12th International Conference on Advances in Computer Entertainment Technology, New York, NY, USA, DOI: 10.1145/2832932.2832972
- Mihaldinec H., Klaić L. & Džapo H. (2023). Estimation of the Cycling Cadence Using Low-Cost Inertial Sensors Mounted on a Bicycle. 6th Conference on Cloud and Internet of Things, March 20-22. 2023, Lisbon, Portugal.
- Hollaus, B., Volmer, J. C., & Fleischmann, T. (2022). Cadence Detection in Road Cycling Using Saddle Tube Motion and Machine Learning. Sensors, 22(16), 6140. DOI: 3390/s22166140
- Abbiss C. R., Peiffer J. J. & Laursen P. B. (2009). Optimal cadence selection during cycling: review article. International SportMed Journal, 10(1), pp. 1-15.
DOI: 10.10520/EJC48370 - Texas Instruments. (n.d.). CC2650 SensorTag user’s guide [User guide].
- Staacks, S., Hütz, S., Heinke, H., & Stampfer, C. (2018). Advanced tools for smartphone-based experiments: Phyphox. Physics Education, 53(4), 045009.
DOI: 1088/1361-6552/aac05e