3D pedestrian dead reckoning and activity classification using waist-mounted inertial measurement unit

Author(s):  
Federica Inderst ◽  
Federica Pascucci Marco Santoni
Author(s):  
Thomas Smith ◽  
Vidya K. Nandikolla

In the sport of basketball, it is important to practice shooting the ball to develop the skill of making the shot in the basket at a high efficiency. Making shots at a high efficiency allows the player to succeed at a high level in the sport. The main focus of the paper describes the design and development of an automatic basketball rebound (ABR) system. The developed ABR provides a system that will launch the ball back to the player at any position on the court within a 50-foot radius. This is accomplished by a variable spring loaded launching mechanism that will compress a spring, depending on the players location, to generate the appropriate force required to launch the ball back to the player. The novel launching mechanism developed is mounted to a rotary table that ensures the launching mechanism is in the correct orientation with the player once the ball is launched. The player is outfitted with an inertial measurement unit to track their position using a method known as dead reckoning. This information is relayed back to a microcontroller that determines the system response. The ABR system is made from lightweight materials and is compact such that it is easy to move around compared to its predecessors.


Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1791
Author(s):  
Ruihui Zhu ◽  
Yunjia Wang ◽  
Hongji Cao ◽  
Baoguo Yu ◽  
Xingli Gan ◽  
...  

This paper presents an evaluation of real-time kinematic (RTK)/Pseudolite/landmarks assistance heuristic drift elimination (LAHDE)/inertial measurement unit-based personal dead reckoning systems (IMU-PDR) integrated pedestrian navigation system for urban and indoor environments. Real-time kinematic (RTK) technique is widely used for high-precision positioning and can provide periodic correction to inertial measurement unit (IMU)-based personal dead reckoning systems (PDR) outdoors. However, indoors, where global positioning system (GPS) signals are not available, RTK fails to achieve high-precision positioning. Pseudolite can provide satellite-like navigation signals for user receivers to achieve positioning in indoor environments. However, there are some problems in pseudolite positioning field, such as complex multipath effect in indoor environments and integer ambiguity of carrier phase. In order to avoid the limitation of these factors, a local search method based on carrier phase difference with the assistance of IMU-PDR is proposed in this paper, which can achieve higher positioning accuracy. Besides, heuristic drift elimination algorithm with the assistance of manmade landmarks (LAHDE) is introduced to eliminate the accumulated error in headings derived by IMU-PDR in indoor corridors. An algorithm verification system was developed to carry out real experiments in a cooperation scene. Results show that, although the proposed pedestrian navigation system has to use human behavior to switch the positioning algorithm according to different scenarios, it is still effective in controlling the IMU-PDR drift error in multiscenarios including outdoor, indoor corridor, and indoor room for different people.


2020 ◽  
Vol 9 (1) ◽  
pp. 7-13
Author(s):  
Marcin Uradzinski ◽  
Hang Guo

Abstract. With the continuous improvement of the hardware level of the inertial measurement unit (IMU), indoor pedestrian dead reckoning (PDR) using an inertial device has been paid more and more attention. Typical PDR system position estimation is based on acceleration obtained from accelerometers to measure the step count, estimate step length and generate the position with the heading received from angular sensors (magnetometers and gyroscopes). Unfortunately, collected signals are very responsive to the alignment of sensor devices, built-in instrumental errors and distortions from the surrounding environment. In our work, a pedestrian positioning method using step detection based on a shoe-mounted inertial unit is arranged and put to the test, and the final results are analyzed. The extended Kalman filter (EKF) provides estimation of the errors which are acquired by the XSENS IMU sensor biases. The EKF is revised with acceleration and angular rate computations by the ZUPT (zero velocity update) and ZARU (zero angular rate update) algorithms. The step detection associated with these two solutions is the perfect choice to calculate the current position and distance walked and to estimate the IMU sensors' collected errors by using EKF. The test with a shoe-mounted IMU device was performed and analyzed in order to check the performance of the recommended method. The combined PDR final results were compared to GPS/Beidou postprocessing kinematic results (outdoor environment) and to a real route which was prepared and calculated for an indoor environment. After the comparison, the results show that the accuracy of the regular-speed walking under ZUPT and ZARU combination in the case of outdoor positioning did not go beyond 0.19 m (SD) and for indoor positioning accuracy did not exceed 0.22 m (SD). The authors are conscious that built-in drift errors coming from accelerometers and gyroscopes, as well as the final position obtained by XSENS IMU, are only stable for a short time period. Based on this consideration, our future work will be focused on supporting the methods presented with radio technologies (WiFi) or image-based solutions to correct all IMU imperfections.


Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3808
Author(s):  
Ran Wei ◽  
Hongda Xu ◽  
Mingkun Yang ◽  
Xinguo Yu ◽  
Zhuoling Xiao ◽  
...  

In the field of pedestrian dead reckoning (PDR), the zero velocity update (ZUPT) method with an inertial measurement unit (IMU) is a mature technology to calibrate dead reckoning. However, due to the complex walking modes of different individuals, it is essential and challenging to determine the ZUPT conditions, which has a direct and significant influence on the tracking accuracy. In this research, we adopted an adaptive zero velocity update (AZUPT) method based on convolution neural networks to classify the ZUPT conditions. The AZUPT model was robust regardless of the different motion types of various individuals. AZUPT was then implemented on the Zynq-7000 SoC platform to work in real time to validate its computational efficiency and performance superiority. Extensive real-world experiments were conducted by 60 different individuals in three different scenarios. It was demonstrated that the proposed system could work equally well in different environments, making it portable for PDR to be widely performed in various real-world situations.


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