Abstract
Microsoft Kinect v.1 and inertial measurement units (IMU) became very popular and broadly available depth and inertia estimating devices, which allow home users to detect and track human limbs motion. Due to their working characteristics both of these devices are sufficient for casual scenarios, where precision is not a crucial factor. In the following paper a detailed review of their characteristics, verified by experiments of both devices, is presented, as well as the method of their imprecisions compensation. Comparing with other authors, the obtained limbs tracking accuracy improvement (by 12 %) has proved that elaborated method outperforms other solutions.
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References
Asteriadis, S., et al.: Estimating human motion from multiple Kinect sensors. In: Proceedings of 6th International Conference on Computer Vision/Computer Graphics Collaboration Techniques and Applications, MIRAGE 2013 (2013)
Bo, A.P., Lanari,et al.: Joint angle estimation in rehabilitation with inertial sensors and its integration with Kinect. In: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS/2011 (2011)
Caron, F., et al.: GPS/IMU data fusion using multisensor Kalman filtering: introduction of contextual aspects. Inf. Fusion 7, 221–230 (2006)
Chang, Y.-J., et al.: A Kinect-based system for physical rehabilitation: a pilot study for young adults with motor disabilities. Res. Dev. Disabil. 32(6), 2566–2570 (2011)
Destelle, F., et al.: Low-cost accurate skeleton tracking based on fusion of kinect and wearable inertial sensors. In: Proceedings of the 22nd European IEEE Signal Processing Conference (EUSIPCO), pp. 371–375 (2014)
DiFilippo, N.M., Jouaneh, M.K.: Characterization of different microsoft kinect sensor models. IEEE Sens. J. 15(8), 4554–4564 (2015)
Fofi, D., Sliwa, T., Voisin, Y.: A comparative survey on invisible structured light. In: SPIE Electron. Imaging Machine Vision Applications in Industrial Inspection, XII, San José (2004)
Freedman, B., et al.: Depth mapping using projected patterns. Patent: 20100118123
Gebhardt, S., Scheinert, G., Uhlmann, F.H.: Temperature influence on capacitive sensor structures. In: Information Technology and Electrical Engineering - Devices and Systems, Materials and Technologies for the Future (2006)
Grigorie, M., de Raad, C., Krummenacher, F., Enz, C.: Analog temperature compensation for capacitive sensor interfaces (1996)
Jayalath, S., Murray, I.: A gyroscope based accurate pedometer algorithm. In: International Conference on Indoor Positioning and Indoor Navigation, p. 31, October 2013
iFixIt:Xbox 360 Kinect Teardown - iFixit. https://www.ifixit.com/Teardown/Xbox+360+Kinect+Teardown/4066. Accessed 12 Apr 2016
Kalkbrenner, C., et al.: Motion capturing with inertial measurement units and kinect - tracking of limb movement using optical and orientation information. In: Proceedings of the International Conference on Biomedical Electronics and Devices (2014)
Kitsikidis, A., et al.: Dance analysis using multiple kinect sensors (2011)
Lange, B., et al.: Interactive game-based rehabilitation using the Microsoft Kinect. In: 2012 IEEE Virtual Reality (VRW), pp. 171–172, March 2012
Madgwick, S.O.H.: An efficient orientation filter for inertial and inertial/magnetic sensor arrays (2010)
Mccarron, B.: Low-Cost IMU implementation via sensor fusion algorithms in the arduino environment (2013)
Feng, S., Murray-Smith, R.: Fusing kinect sensor and inertial sensors with multi-rate Kalman filter. In: IET Conference on Data Fusion Target Track. 2014 Algorithms Application (2014)
Reichinger, A.: Kinect pattern uncovered. https://azttm.wordpress.com/. Accessed 12 Apr 2016
Rzeszotarski, D., Strumiłło, P., et al.: System Obrazowania Stereoskopowego Sekwencji Scen Trójwymiarowych. Zesz. Nauk, Elektron (2006)
Schröder, Y., et al.: Multiple Kinect Studies Technical report (2011)
Shotton, J., et al.: Semantic texton forests for image categorization and segmentation. In: Proceedings of IEEE CVPR (2008)
Shotton, J., et al.: Real-time human pose recognition in parts from single depth images. In: Proceedings of IEEE CVPR (2011)
Shpunt, A., Zalevsky, Z.: Depth-varying light fields for three dimensional sensing. Patent: 20080106746
Shpunt, A.: Depth mapping using multi-beam illumination. Patent: 20100020078
Stackoverfow community: precision of the kinect depth camera. Accessed 12 Apr 2016
Suarez, J., Murphy, R.R.: Using the Kinect for search and rescue robotics. In: 2012 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR) (2012)
Tian, Y., et al.: Upper limb motion tracking with the integration of IMU and Kinect. Neurocomputing 159, 207–218 (2015)
Walklogger. https://play.google.com/store/apps/details?id=com.walklogger.pedometer. Accessed 17 Apr 2016
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Glonek, G., Wojciechowski, A. (2016). Kinect and IMU Sensors Imprecisions Compensation Method for Human Limbs Tracking. In: Chmielewski, L., Datta, A., Kozera, R., Wojciechowski, K. (eds) Computer Vision and Graphics. ICCVG 2016. Lecture Notes in Computer Science(), vol 9972. Springer, Cham. https://doi.org/10.1007/978-3-319-46418-3_28
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DOI: https://doi.org/10.1007/978-3-319-46418-3_28
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