Self-Adaptive Step Counting on Smartphones under Unrestricted Stepping Modes

Pedometer apps on smartphones and wearable electronics are increasingly popular nowadays, as they are widely used for health monitoring and location-based systems. Most pedometer apps are based on inertial sensors and each step counting algorithm works precisely under restricted stepping modes because steps are detected and validated after comparing their parameters to pre-determined optimal values related to particular conditions. In this paper, we propose self-adaptive step counting in order to improve step counting accuracy under unrestricted stepping modes on smartphones. Based on our human stepping model, we propose self-adaptive method that can detect new steps by monitoring vertical acceleration and validate new steps by comparing to self-adaptive values, which are adjusted dynamically after each step occurs. We show the flexibility of the proposed approach by incorporating it into two existing step counting algorithms WPD and DTW. We also propose a new stepping cycle recognition (SCR) algorithm that is self-adaptive and performs the best under variant stepping modes. With experiments under different stepping modes including fixed and variant modes, we show that self-adaptive methods perform significantly better compared to original methods using fixed optimal values.