MTCF-Net: Leveraging Large Models for Multimodal Time Series Analysis in Sports and Fitness Consumer Electronics

Abstract

The convergence of consumer electronics and sports has revolutionized how physical activities are monitored and analyzed. Devices such as smartwatches and fitness trackers collect extensive time series data for applications including activity recognition and personalized training. Large Models have emerged as powerful tools for processing such complex data. However, effectively applying these models to the temporal complexity and multimodal heterogeneity of sports data remains challenging. This paper introduces the Multi-Scale Temporal Dependency and Cooperative Feature Fusion Network (MTCF-Net), a framework leveraging the capabilities of Large Models to process multimodal time series data in sports and fitness consumer electronics. MTCF-Net integrates key components: Temporal Shift Module (TSM) and Temporal Dependency Modeling (TDM) for capturing short- and long-term dependencies, Multimodal Cooperative Feature Interaction (MCFI) for dynamic cross-modal integration, and Adaptive Feature Fusion (ADF) to prioritize task-relevant features dynamically. Extensive evaluations on the UCI-HAR and PAMAP2 datasets demonstrate MTCF-Net’s state-of-the-art performance, achieving accuracy scores of 96.44% and 98.31%, respectively. Ablation studies validate its modular design, showcasing how Large Models can enhance consumer electronics for smarter and more efficient sports applications. The model’s improved accuracy and ability enable more precise performance analysis, real-time feedback, and personalized training, thereby providing tangible benefits for both athletes and fitness enthusiasts in real-world scenarios.