Pedestrian location estimation method by Kalman filter based on surface electromyography–average velocity mapping model for well-oriented indoor environment

Abstract

Aiming at the problem that Global Positioning System (GPS) cannot be used in the indoor environment, a pedestrian location estimation method by Kalman filter based on sEMG (surface Electromyography)–average velocity mapping model is proposed for well-oriented indoor environment. First, sEMG–average velocity mapping model is established according to the features extracted from sEMG sensors. Second, the inertial navigation algorithm is carried out by the outputs of MIMU (Micro Inertial Measurement Unit). Third, the pedestrian location estimation model is constructed by the Kalman filter. And the estimated sEMG–average velocity is used as the observations; the inertial navigation results obtained by MIMU are used as the state vector. Finally, pedestrian location estimation experiments are carried out. Well-oriented indoor environment test shows that the maximum location estimation error is about 1.84 m and the maximum relative location estimation error for the total trajectory is about 1.71%. To further analyze the performance of the proposed method, an outdoor environment test is carried out. It shows that the average location estimation error is 2.39 m and the relative location estimation error for the total trajectory is about 0.60%, which is close to the location result based on GPS observations. The proposed pedestrian location method can provide a technical support for the application of well-oriented indoor environment.