Year: 2025
Type: article
Abstract: 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.
DOI: 10.1145/3749539
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Awards: Collaborative Research: CSR:Small:Sustainable Wearable Sensing System for Continuous Tree Health Monitoring, Collaborative Research: SaTC: CORE: Small: Securing IoT and Edge Devices under Audio Adversarial Attacks, Collaborative Research: CCSS: Continuous Facial Sensing and 3D Reconstruction via Single-ear Wearable Biosensors, Collaborative Research: High-precision monitoring of foodborne pathogens in food manufacturing facilities
OpenAlex ID: W4413968563
