Pedestrian positioning with physical activity classification for indoors

This paper presents a wearable Inertial Measurement Unit pedestrian positioning system for indoors. Hidden Markov Model (HMM) is introduced to pre-process the sensor data and classify common activities. HMM also complements local minimum angular rate value for capturing the onset/end of each step. ZUPT algorithm are implemented to correct the walking velocity at step stance phase when errors existed. A novel acceleration-based approach combined with gyroscope data is developed to achieve a better heading estimation. Proposed method is able to reduce drift errors from gyroscopes and avoid electromagnetic perturbance to magnetometers when estimate subject´s position. Experiment results show the positioning system achieves approximately 99% accuracy.