SpeedFi: Fine-grained Speed Estimation for Bodyweight Exercise with WiFi

Exercise speed is a crucial indicator in bodyweight exercise assessment, directly reflecting muscular strength, energy expenditure, and exercise power. Benefiting from ubiquitous infrastructure and non-intrusive properties, WiFi-based sensing has emerged as a promising technique for motion speed estimation. However, existing WiFi sensing systems are significantly influenced by user diversity, positional variations, and orientation changes, which limit their applicability in real-world scenarios. In this work, we propose a learning-based framework, named SpeedFi , to estimate exercise speed from WiFi Channel State Information (CSI). An encoder-decoder network is designed to infer body speed, while temporal–frequency constraints are introduced to model consistent speed dynamics. To enhance user generalization, a domain adversarial strategy is adopted to extract user-invariant features. Moreover, two orthogonal WiFi links are deployed to capture complementary spatial perspectives, and an attention-based fusion module is employed for effective feature integration. A physics-inspired simulation model is further developed to synthesize CSI frequency variations, guiding the design of a diversified data collection strategy covering multiple positions, orientations, and environmental configurations. We conduct extensive experiments involving 10 participants performing 3 types of bodyweight exercises across 5 distinct environments. In controlled settings, SpeedFi achieves an average speed estimation error of 0.113 m/s for peak speeds around 1 m/s. Real-time analysis indicates that the system runs at over 40 FPS on a mid-range GPU, supporting its potential for real-time deployment. Additional tests under multi-person scenarios, complex multipath conditions, and varying WiFi link configurations are conducted to assess the system’s operational boundaries and robustness, validating its applicability across diverse settings.