Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
169 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
45 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

CycloWatt: An Affordable, TinyML-enhanced IoT Device Revolutionizing Cycling Power Metrics (2403.07915v1)

Published 27 Feb 2024 in cs.NI and eess.SP

Abstract: Cycling power measurement is an indispensable metric with profound implications for cyclists' performance and fitness levels. It empowers riders with real-time feedback, supports precise training regimen planning, mitigates injury risks, and enhances muscular development. Despite these advantages, the widespread adoption of cycling power meters has been hampered by their prohibitive cost and deployment complexity. This paper pioneers a groundbreaking approach to power measurement in cycling, prioritizing affordability and user-friendliness. To achieve this goal, we introduce a cutting-edge Internet of Things (IoT) device that seamlessly integrates force signals with inertial sensor data while leveraging the power of edge machine learning techniques. In-field experimental evaluations demonstrate that our prototype can estimate power with remarkable accuracy, boasting a Mean Absolute Error (MAE) of only 12.29 Watts (4.1\%). Notably, our design emphasizes energy efficiency, operating in a low-power mode that consumes a mere 50 milliwatts and offers an exceptional battery life of up to 25.8 hours in always-on active mode. With an ultra-low latency of 4.33 milliseconds for data processing and inference, our system ensures real-time power estimation during cycling activities. Incorporating IoT concepts and devices, this paper marks a significant milestone in developing cost-effective and accurate cycling power meters.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (21)
  1. C. E. Broeder, “Power meter principles for optimizing testing, training and performance strategies in cycling,” Routledge Handbook of Ergonomics in Sport and Exercise, pp. 247–262, 2013.
  2. P. Leo, J. Spragg, T. Podlogar, J. S. Lawley, and I. Mujika, “Power profiling and the power-duration relationship in cycling: a narrative review,” European journal of applied physiology, pp. 1–16, 2022.
  3. L. Passfield, J. G. Hopker, S. Jobson, D. Friel, and M. Zabala, “Knowledge is power: Issues of measuring training and performance in cycling,” Journal of sports sciences, vol. 35, no. 14, pp. 1426–1434, 2017.
  4. V. Rodríguez-Rielves, J. R. Lillo-Beviá, Á. Buendía-Romero, A. Martínez-Cava, A. Hernández-Belmonte, J. Courel-Ibáñez, and J. G. Pallarés, “Are the assioma favero power meter pedals a reliable tool for monitoring cycling power output?” Sensors, vol. 21, no. 8, p. 2789, 2021.
  5. C. Granier, C. Hausswirth, S. Dorel, and Y. Le Meur, “Validity and reliability of the stages cycling power meter,” The Journal of Strength & Conditioning Research, vol. 34, no. 12, pp. 3554–3559, 2020.
  6. AIEndurance, “Calculate cycling power without a power meter,” [Online; accessed 16-January-2023]. [Online]. Available: https://aiendurance.com/blog/calculate-cycling-power-without-a-power-meter
  7. R. Mcainsh, “Design and engineering of an accurate bicycle power meter,” 12 2014. [Online]. Available: https://www.researchgate.net/publicatio/303517617_Design_and_Engineering_of_an_Accurate_Bicycle_Power_Meter
  8. G. Lemaître and C. Lemaitre, “Estimate power without measuring it: a machine learning application,” 07 2018. [Online]. Available: https://www.researchgate.net/publication/327200753_Estimate_Power_without_Measuring_it_a_Machine_Learning_Application
  9. A. Hilmkil, O. Ivarsson, M. Johansson, D. Kuylenstierna, and T. van Erp, “Towards machine learning on data from professional cyclists,” 08 2018. [Online]. Available: https://arxiv.org/pdf/1808.00198.pdf
  10. T. Maier, L. Schmid, B. Müller, T. Steiner, and J. P. Wehrlin, “Accuracy of cycling power meters against a mathematical model of treadmill cycling,” International Journal of Sports Medicine, vol. 38, no. 06, pp. 456–461, 2017.
  11. D. Hu and B. Krishnamachari, “Fast and accurate streaming cnn inference via communication compression on the edge,” in 2020 IEEE/ACM Fifth International Conference on Internet-of-Things Design and Implementation (IoTDI).   IEEE, 2020, pp. 157–163.
  12. P. Bonazzi, S. Bian, G. Lippolis, Y. Li, S. Sheik, and M. Magno, “A low-power neuromorphic approach for efficient eye-tracking,” arXiv preprint arXiv:2312.00425, 2023.
  13. H. Zhou, T. Lu, Y. Liu, S. Zhang, R. Liu, and M. Gowda, “One ring to rule them all: An open source smartring platform for finger motion analytics and healthcare applications,” in Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation, 2023, pp. 27–38.
  14. S. Bian and P. Lukowicz, “Capacitive sensing based on-board hand gesture recognition with tinyml,” in Adjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2021 ACM International Symposium on Wearable Computers, 2021, pp. 4–5.
  15. M. Giordano, L. Piccinelli, and M. Magno, “Survey and comparison of milliwatts micro controllers for tiny machine learning at the edge,” in 2022 IEEE 4th International Conference on Artificial Intelligence Circuits and Systems (AICAS).   IEEE, 2022, pp. 94–97.
  16. P. Bonazzi, T. Ruegg, S. Bian, Y. Li, and M. Magno, “Tinytracker: Ultra-fast and ultra-low-power edge vision for in-sensor gaze estimation,” arXiv preprint arXiv:2307.07813, 2023.
  17. A. S. Gardner, S. Stephens, D. T. Martin, E. Lawton, and D. J. Hamilton Lee, “Accuracy of srm and power tap power monitoring systems for bicycling,” in Medicine Science in Sports Exercise, July 2004, pp. 1252–1258.
  18. S. Bian, V. F. Rey, P. Hevesi, and P. Lukowicz, “Passive capacitive based approach for full body gym workout recognition and counting,” in 2019 IEEE International Conference on Pervasive Computing and Communications (PerCom.   IEEE, 2019, pp. 1–10.
  19. S. Mekruksavanich and A. Jitpattanakul, “Multimodal wearable sensing for sport-related activity recognition using deep learning networks,” Journal of Advances in Information Technology, 2022.
  20. S. Bian, X. Wang, T. Polonelli, and M. Magno, “Exploring automatic gym workouts recognition locally on wearable resource-constrained devices,” in 2022 IEEE 13th International Green and Sustainable Computing Conference (IGSC).   IEEE, 2022, pp. 1–6.
  21. StagesCycling, “Stages power l shimano 105 r7000 left crank arm cycling power meter,” [Online; accessed 18-March-2023]. [Online]. Available: https://stagescycling.com/en_us/gen-3-stages-power-l-shimano-105-r7000-power-meter

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now