Developing of the human motion  tracking system

Abstract

This paper discusses the main aspects necessary for developing a human motion tracking system. Such kind of system is widely used in the media industry for animating 3D-models. This technology also finds application in robotic engineering and medicine, however, less than in media. From the two main types of movement tracking systems - optical and non-optical – the latter was chosen because of its relatively low price and less demand for free space. MEMS-based sensors were selected for non-optical system development because of their affordable price, small size and market demand. Relevant sources analysis was conducted to define the technologies typically used for movement tracking and to find examples of similar systems. The theoretical part of the paper briefly describes the process of calibration of the inertial acceleration sensors (accelerometers) and angular velocity sensors (gyroscopes). The Kalman filter works with data filtration and processing because of its flexibility and low demands for calculating memory, which is important for on-line data processing. The general diagram of the system shows the essential parts of the wireless motion tracking system. Also, the possibility of integrating the system into popular software packages for working with 3D graphics was taken into consideration.

Keywords

References

1. Z. B. Wang, L. Yang, Z. P. Huang, J. K. Wu, Z. Q. Zhang, and L. X. Sun, “Human motion tracking based on complementary Kalman filter,” 2017 IEEE 14th Int. Conf. Wearable Implant. Body Sens. Networks, BSN 2017, pp. 55–58, 2017, DOI: 10.1109/BSN.2017.7936006
2. M. Kok, J. D. Hol, and T. B. Schön, “An optimization-based approach to human body motion capture using inertial sensors,” IFAC Proc. Vol., vol. 19, pp. 79–85, 2014, DOI: 10.3182/20140824-6-ZA-1003.02252
3. S. Shen, M. Gowda, and R. Roy Choudhury, “Closing the Gaps in Inertial Motion Tracking,” in 24th Annual International Conference on Mobile Computing and Networking, 2018, pp. 429–444, DOI:10.1145/3241539.3241582
4. V. V. Avrutov, P. M. Bondar, and V. V. Meleshko, Mikroakselerometry ta yikh vyprobuvannya: Navchalʹnyy posibnyk [Microaccelerometers and their tests: A manual]. Kyiv: Korniychuk, 2008, ISBN: 978-966-7599-51-5
5. V. V. Avrutov, Ispytaniya inertsial’nykh priborov: Uchebnoye posobiye [Tests of inertial instruments: Tutorial]. Kyiv: NTUU “KPI,” 2016.
6. R. E. Kalman, “A New Approach to Linear Filtering and Prediction Problems,” J. Basic Eng., vol. 82, no. 1, p. 35, 2011, DOI: 10.1115/1.3662552
7. G. Welch, “An introduction to the Kalman filter,” Univ. North Carolina Chapel Hill, Chapel, pp. 1–16, 1995
8. L. E. Ott, “Kalman filter with complementary constraint and integrated navigation systems applications,” Iowa State University, 1971.
9. “Plugins,” Unreal Engine 4 documentation. [Online]. Available: https://docs.unrealengine.com/en-us/Programming/Plugins.
10. MPU-9250 Product Specification, PS-MPU-9250A-01, 2016. URL: https://www.invensense.com/wp-content/uploads/2015/02/PS-MPU-9250A-01-v1.1.pdf
11. D. V. Shelaev, “Comparison of MEMS and Optical-fiber Gyroscopes,” Geoprofi, no. 6, pp. 49–54, 2012.
12. O. I. Nesterenko and V. Y. Balanchuk, “novitni prylady oriyentatsiyi ta navihatsiyi: Laboratornyy praktykum [new instruments of orientation and navigation: Laboratory Workshop].” NTUU “KPI,” Kyiv, p. 74, 2012.
13. V. V. Meleshko and O. I. Nesterenko, Besplatformennyye inertsial’nyye navigatsionnyye sistemy. Uchebnoye posobiye [Free inertial navigation systems. Tutorial]. Kirovograd: POLIMED - Service, 2011.
14. R. McGhree, E. Bachman, X. Yun, and M. Zyda, Real-Time Tracking and Display of Human Limb Segment Motions Using Sourceless Sensors and a Quaternion-Based Filtering Algorithm - Part I: Theory. Monterey: Naval Postgraduate School, 2000.
15. D. N. Lavrov and A. A. Kondyurina, “Opredeleniye trayektorii dvizheniya priyomnika sistemy obnaruzheniya nesanktsionirovanno ustanovlennoy tochki besprovodnogo dostupa [Determination of the motion path of the receiver of the detection system of an unauthorized wireless access point],” Math. Struct. Model., vol. 41, no. 1, pp. 75–87, 2017.
16. “Blue Gecko BGM121 Bluetooth Module - SiP.” [Online]. Available: https://www.silabs.com/products/wireless/bluetooth/bluetooth-low-energy-modules/bgm121-bluetooth-sip-module#table.
17. UE4Duino: Unreal Engine 4 plugin for Arduino (COM port) communication on Windows. GitHub. URL: https://github.com/RVillani/UE4Duino

License

Copyright (c) 2019 Максим Олександрович Ярошенко (Автор)