本研究之目的在於開發多通道無線慣性感測系統及建構出上肢軌跡機重建演算法。本研究藉由九軸慣性感測器及ZigBee無線傳輸模組架構量測裝置並存取動作訊號，並利用經驗模態分解法、傅立葉分析與四元素法結合開發出軌跡演算重建方法演算上肢運動軌跡。此演算法利用經驗模態分解法與傅立葉轉換降低量測訊號之雜訊並藉由頻譜將角速度訊號轉換為角位移訊號，再利用四元素及空間旋轉矩陣轉換相對坐標系及絕對坐標系演算人體上之於空間座標之位置。
在演算法驗證部分，本研究將實驗室所開發之多通道無線慣性感測器貼附於機械手臂及人體上肢量測所設計的上肢動作，再藉由本研究所開發之軌跡演算法還原其運動軌跡。實驗結果顯示本研究所開發之無線慣性感測系統及軌跡演算法可重建任意起始位置之多關節的上肢動作軌跡並計算出關節於三維空間中的旋轉角。

The purpose of this study is developing a wireless inertial sensing system and limb motion reconstruction algorithm. In this study, we employ two of nine-axis inertial measurement units and ZigBee wireless transmission module to consist a wireless inertial sensing device for acquiring motion signals. Then, combine EMD, spectral analysis and quaternion to develop algorithm for reconstructing the trajectory of limb motion. The algorithm employ quaternion as rotation matrix used to translate the coordinate between global coordinate and relative coordinate. EMD and spectral analysis are considered as filter to reduce noise of inertial signals and used to obtain angular displacement of each joint. After obtaining the angular displacement of each joint, the kinematic model will be applied for trajectory reconstruction.
As the verification of developed algorithms, we employ developed device and applied algorithm to reconstruct trajectories of designed motion of robotic arm and limb. The result of the experiment shows that the algorithm can be used to reconstruct the limb motion trajectory of multiple joints with different initial position and attitude, and estimate the rotation angles of multiple joints in 3-D space.

[1] H. Zhou, and H. Hu, “Human motion tracking for rehabilitation - A survey,” Biomedical Signal Processing and Control 3, pp. 1-18, 2008.
[2] S. Allin, N. Baker, E. Eckel, D. Ramanan,”Robust Tracking of the Upper Limb for Functional Stroke Assessment,” TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, VOL. 18, NO. 5, pp. 542-550, IEEE, 2010
[3] E. Davaasambuu, C.-C. Chiang, J. Y. Chiang, Y.-F. Chen, S. Bilgee, “A Microsoft Kinect based virtual rehabilitation system,” The 5th International Conference on the Frontiers of Information Technology, pp. 44-50 2012.
[4] Y. Zhang, Z. Wang, L. Ji , B. Sheng, ”The Clinical Application of Upper Extremity Compound Movements Rehabilitation Training Robot,” proceeding of 9th International Conference on Rehabilitation Robotics, pp.91-94, IEEE,2005
[5] J. Li, R. Zhang, Y. Zhang, J. Yao,”iHandRehab: Interactive Hand Exoskeleton for Active and Passive Rehabilitation,” International Conference on Rehabilitation Robotic, pp.1-6, IEEE,2011
[6] H. Zhou, and H. Hu, “Inertial motion tracking of human arm movements in stroke rehabilitation,” International Conference on Mechatronics & Automation, pp. 1306-1311, IEEE, 2005.
[7] J. Bae, K. Haninger, D. Wai, X. Garcia, M. Tomituka,”A Network-Based Monitoring System for Rehabilitation,” International Conference on Advanced Intelligent Mechatronic, pp. 232-237, IEEE, 2012
[8] J. K. Lee and E. J. Park, "A fast Gauss-Newton optimizer for estimating human body orientation," International Engineering in Medicine and Biology Society, pp. 1679-1682, IEEE, 2008
[9] S. Sun, X. Meng, L. Ji, Z. Huang, and J. Wu, "Adaptive Kalman filter for orientation estimation in micro-sensor motion capture," Proceedings of the 14th International Information Fusion Conference ,pp. 1-8, IEEE, 2011.
[10] Y. Tian, H. Wei, and J. Tan, "An adaptive-gain complementary filter for real-time human motion tracking with MARG Sensors in Free-Living environments," Neural Systems and Rehabilitation Engineering ,Volume:21, Issue 2,pp. 254-264, IEEE, 2012
[11] V. Kempe, “Inertial MEMS Principles and Practice,” Cambridge University Press, 2011
[12] J. P. Bentley, “Principles of Measurement System,”4th Ed., Pearson Prentice Hall, 2005
[13] E. Ramsden, “Hall-Effect Sensors Theory and Application,” 2nd Ed., Newnes, 2006.
[14] M. S. Grewal, L. R. Weill, and A. P. Andrews, “Global Positioning Systems, Inertial Navigation, and Integration”, 2nd Ed., Wiley-Interscience, 2007
[15] C. Konvalin, “Compensating for Tilt, Hard Iron, and Soft Iron Effects,” Rev 1.2, MEMSense, 2008
[16] H. Labiod, H. Afifi, C. De Santis, “Wi-Fi™ , Bluetooth™, ZigBee™, and WiMax™,” Springer,2007
[17] M. D. Shuster, “Survey of attitude representations,” Journal of the Astronautical Sciences, vol. 41, no. 4, pp. 439-517, 1993.
[18] R. M. Brannan “Rotation: A review of useful theorems involving proper orthogonal matrices referenced to three-dimensional physical space,” 2002
[19] S. O. H. Madgwick, “Quaternions”, 2011
[20] N. E. Huang, Z. Shen, S. R. Long, M. C. Wu, E. H. Shih, Q. Zheng, N. C. Yen, C. C. Tung and H. H. Liu, "The empirical mode decomposition method and the Hilbert spectrum for non-stationary time series analysis," Proc. R. Soc. London A, 454, 1998.
[21] InvenSense, ” MPU-6050 Product Specification Rev 3.3.”
[22] AsahiKASEI, “AK8975 Electronic Compass.”
[23] “XBee/XBee-Pro RF Modules Product Manual.” DIGI, 2012.
[24] W. T. Fong, S. K. Ong, and A. Y. C. Nee, “Methods for in-field user calibration of an inertial measurement unit without external equipment,” Measurement Science and Technology, vol. 19, no. 8, 085202, 2008
[25] H. Zhou, and H. Hu, “Human motion tracking for rehabilitation - A survey,” Biomedical Signal Processing and Control 3, pp. 1-18, 2008
[26] G. Cooper, I. Sheret, L. McMillian, K. Siliverdis, N. Sha, D. Hodgins, L. Kenney, D. Howard, “Inertial sensor-based knee flexion/ extension angle estimation, ” Journal of Biomechanics 42, pp. 2678–2685, 2009
[27] J. –S. Wang, Y.-L. Hsu, and J. –N. Liu, “An Inertial Measurement Unit Based Pen with a Trajectory Reconstruction Algorithm and Its Applications,” IEEE Transactions on Industrial Electronics, Vol. 57, No. 10, pp. 3508-3521, 2010