Shin, Beomju Kim, Chulki Kim, Jaehun Lee, Seok Kee, Changdon Kim, Hyoung Seok Lee, Taikjin 2024-01-20T03:31:24Z 2024-01-20T03:31:24Z 2021-09-05 2016-09-15 1530-437X https://pubs.kist.re.kr/handle/201004/123672 A motion recognition-based 3D pedestrian navigation system that employs a smartphone is presented. In existing inertial measurement unit ( IMU)-based pedestrian dead-reckoning ( PDR) systems, sensor axes are fixed regardless of user motion, because the IMU is mounted on the shoes or helmet. On the other hand, the sensor axes of a smartphone are changed according to the walking motion of the user, because the smartphone is usually carried by hand or kept in the pocket. Therefore, the conventional PDR method cannot apply to the smartphone-based PDR system. To overcome this limitation, the walking status is detected using a motion recognition algorithm with sensor measurements from the smartphone. Then, different PDR algorithms are applied according to the recognized pattern of the pedestrian motion. The height information of the pedestrian is also estimated using the on-board barometric pressure sensor of the smartphone. The 3D position, which consists of the 2D position calculated by the PDR and the height information, is provided to the pedestrian. The proposed system has several advantages in terms of cost and accessibility. It requires no additional peripheral devices except for the smartphone, because smartphones are equipped with all the necessary sensors, such as an accelerometer, magnetometer, gyroscope, and barometric pressure sensor. This paper implements the proposed system as an android-based application. The experimental results demonstrate the performance of the proposed system and reveal a high positioning accuracy. English IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC WI-FI FUSION Motion Recognition-Based 3D Pedestrian Navigation System Using Smartphone Article 10.1109/JSEN.2016.2585655 1 IEEE SENSORS JOURNAL, v.16, no.18, pp.6977 - 6989 IEEE SENSORS JOURNAL 16 18 6977 6989 scie scopus 000382355100022 2-s2.0-84984870584 Engineering, Electrical & Electronic Instruments & Instrumentation Physics, Applied Engineering Instruments & Instrumentation Physics Article WI-FI FUSION Indoor navigation pedestrian dead reckoning motion recognition artificial neural network