SmartWT: Fitness activity monitoring using wearable devices

抄録

A 2023 survey of over 4,000 regular fitness enthusiasts revealed that wearable devices have remained a prominent topic. Studies have shown that planned fitness activities and long-term progress tracking can enhance motivation and provide accurate fitness evaluations. However, most current fitness tracking applications require manual data entry before and after workouts, potentially distracting users and reducing workout effectiveness. Few applications can automatically record diverse fitness movements for extended periods. To address these challenges, this study aims to develop a system using Android-based smartwatches and smartphones to recognize and quantify users’ fitness-related movements automatically. By eliminating manual operation, the system offers long-term feedback on fitness activities. The research comprises four key components: (1) Training two machine learning models to recognize motion states and fitness movements with feature dimensionality reduction for real-time mobile operation; (2) Proposing the Double-Layer Sliding Window method to recognize and count fitness movements during exercise based on sliding windows and peak detection; (3) Developing an algorithm for automatic fitness movement recognition and counting in long-term exercise environments; (4) Conducting experiments in Python environments and analyzing statistical data. Results demonstrated the potential of this approach in automatic fitness activity recording.