CycloWatt: An Affordable, TinyML-enhanced IoT Device Revolutionizing Cycling Power Metrics
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arXiv
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| Format: | Preprint |
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2024
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| author | Luder, Victor Bian, Sizhen Magno, Michele |
| author_facet | Luder, Victor Bian, Sizhen Magno, Michele |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_07915 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CycloWatt: An Affordable, TinyML-enhanced IoT Device Revolutionizing Cycling Power Metrics Luder, Victor Bian, Sizhen Magno, Michele Networking and Internet Architecture Signal Processing 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. |
| title | CycloWatt: An Affordable, TinyML-enhanced IoT Device Revolutionizing Cycling Power Metrics |
| topic | Networking and Internet Architecture Signal Processing |
| url | https://arxiv.org/abs/2403.07915 |