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Accurate and real-time gait analysis is essential for enhancing performance and reducing injury risks in treadmill running. In this paper, we introduce VibRun, an unobtrusive gait analysis system that estimates key physiological metrics, such as cadence, ground contact time, stride time, center of pressure, and plantar pressure distribution, through footstep vibrations captured by low-cost treadmill-mounted sensors. Leveraging advanced multi-task transformer models, our system offers a robust, real-time solution to monitor and analyze running biomechanics without requiring intrusive wearable devices. This approach enables seamless integration into virtual sports, gaming platforms, and immersive exercise environments, enhancing the running experience by providing personalized feedback. By offering precise biomechanical insights in real-time, VibRun paves the way for future applications in virtual sports, gamified fitness, and interactive training programs, empowering users to engage more effectively in their training sessions while improving overall performance and reducing injury risks. Extensive evaluations with 17 participants across varied treadmill running scenarios demonstrate VibRun's accuracy in real-time gait analysis. For instance, VibRun achieves a mean error of 28.8 ms in ground contact time and a distance of 13.66 mm in the center of pressure, among other measured metrics, highlighting its precise performance across multiple gait parameters.
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CaNRun: Non-Contact, Acoustic-based Cadence Estimation on Treadmills using Smartphones
Running with a consistent cadence (number of steps per minute) is important for runners to help reduce risk of injury, improve running form, and enhance overall bio-mechanical efficiency. We introduce CaNRun, a non-contact and acoustic-based system that uses sound captured from a mobile device placed on a treadmill to predict and report running cadence. CaNRun obviates the need for runners to utilize wearable devices or carry a mobile device on their body while running on a treadmill. CaNRun leverages a long short-term memory (LSTM) network to extract steps observed from the microphone to robustly estimate cadence. Through an 8-person study, we demonstrate that CaNRun achieves cadence detection accuracy without calibration for individual users, which is comparable to the accuracy of the Apple Watch despite being non-contact.
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- Award ID(s):
- 1704899
- PAR ID:
- 10416036
- Date Published:
- Journal Name:
- CPS-IoT Week '23: Proceedings of Cyber-Physical Systems and Internet of Things Week 2023
- Page Range / eLocation ID:
- 272 to 277
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3576914.3589561
