Objectives With the increasing demand for location-based service(LBS) applications and the wide popularity of multi-mounted micro electro mechanical system(MEMS) navigation sensors, pedestrian dead reckoning (PDR) algorithm has attracted more and more attention and has become one of the mainstream algorithms in pedestrian navigation research. However, the low-cost MEMS sensor has high measurement noise and serious accumulation of PDR solution error. Moreover, the universality of PDR algorithm is poor, and the availability of constraints of MEMS navigation sensors with different wearing statte is significantly different.
Methods A multi-mounted PDR algorithm based on wearable MEMS sensor state recognition is proposed. Firstly, support vector machine is used for fully supervised training to realize the accurate recognition of five wearing modes (hand, leg, waist, foot and stationary state). Then the applicability of PDR algorithm in different wearing states is analyzed, and a comprehensive solution strategy of multi position PDR is proposed based on the applicability analysis results.
Results The measured results show that the wear recognition accuracy of MEMS sensor is more than 97%. The foot PDR can achieve high-precision solution, and the cumulative error is 0.74%, while the solution effect of other positions (hand, leg and waist) has been significantly improved, and the cumulative error has been reduced from 6.76%—21.19% before recognition to 2.92%—5.62% after recognition.
Conclusions Therefore, the proposed algorithm can dynamically and accurately realize the state recognition of wearable MEMS sensors. After applying the PDR comprehensive solution strategy, the solution accuracy of PDR is significantly improved.