在這篇論文中我們提出了一支基於低成本慣性測量單元的數位筆，該數位筆相較於其他手寫軌跡重建裝置有以下優點：無書寫範圍限制、便於攜帶、省電。我們採用的慣性測量單元其包含了三軸加速計與三軸陀螺儀，其中加速計可以用來量測空間中三個維度的加速度，因此我們可以用來得知數位筆移動的方向與軌跡。而陀螺儀可以量測空間中三個維度的角速度，可以計算數位筆在進行書寫時產生的旋轉量以求得正確的加速度。然而，慣性測量單元俱有高雜訊的本質，這些雜訊可能來自慣性測量單元內部的熱或波動，也受到環境影響。再加上使用者握筆時必定會有些微的顫抖產生，這些都將影響慣性測量單元所量測的結果。因此我們發展了一套抑制其高雜訊之手寫軌跡重建系統，我們提出了使用機器學習的技術來對慣性測量單元的進行移動狀態判別。目前其他研究都採用單一特徵值來設定門檻值進行移動狀態的判定，我們則採用多樣的特徵值透過線性判別分析進行訓練找出更適合的移動狀態判別模型。另外，加速計的誤差經過累積將會對軌跡造成莫大的影響，因此我們提出一個重置開關的機制。該機制可以在進行位移計算時有效地抑制加速度誤差的累積。

In this paper, we purpose to develop a digital pen based on low-cost IMU (Inertial Measurement Unit). There are some advantages of using IMU to design a digital pen: 1) Unlimited writing space, 2) portable, and 3) power saving. The IMU used in our digital pen contains a 3-axis accelerometer and a 3-axis gyroscope. The accelerometer is utilized to measure the acceleration when the digital pen is moving, and we can know how the digital pen moves based on acceleration information. The gyroscope can measure the rotation of the digital pen, and the rotation information is used to improve the precision of acceleration. However, there are intrinsic bias and random noise in the IMU signals. The noise maybe caused by the internal thermal, mechanical fluctuations, environment temperature, and the user's unconscious trembles. Therefore, we propose a handwriting trajectory reconstruction method which can be applied to IMU sensors with high-degree noise. To more accurately reconstruct the trajectory, we apply the machine learning technique to detect the movement state of the digital pen. In previous related works, they set a simple threshold to detect the pen movement, which is not robust and general for all IMU sensors. We extract several features from IMU signals and train a model for movement detection based on LDA (Linear Discriminant Analysis). We also propose a mechanism named "reset switch" to more effectively restrain the accumulated error of the accelerometer.

[1] S. Agrawal, I. Constandache, S. Gaonkar, and R. R. Choudhury. Phonepoint pen: Using mobile phones to write in air. In Proceedings of the 1st ACM Workshop on Networking, Systems, and Applications for Mobile Handhelds, MobiHeld '09, pages 1--6, New York, NY, USA, 2009. ACM.
[2] S. Agrawal, I. Constandache, S. Gaonkar, R. Roy Choudhury, K. Caves, and F. DeRuyter. Using mobile phones to write in air. In Proceedings of the 9th International Conference on Mobile Systems, Applications, and Services, Mo- biSys '11, pages 15--28, New York, NY, USA, 2011. ACM.
[3] Z. Dong, U. Wejinya, and W. Li. An optical-tracking calibration method for mems-based digital writing instrument. Sensors Journal, IEEE, 10(10):1543- -1551, Oct 2010.
[4] B. K. P. Horn. Closed-form solution of absolute orientation using unit quater- nions. Journal of the Optical Society of America A, 4(4):629--642, 1987.
[5] J. S. Hunter. The exponentially weighted moving average. Journal of Quality Technology, 18(4):203--207, 1986.
[6] X. Jin, O. Kanzo, and S. X. Bing. A new positioning scheme for pen-like handwriting input devices. In Consumer Communications and Networking Conference, 2005. CCNC. 2005 Second IEEE, pages 166--171, Jan 2005.
[7] Livescribe. Echo smartpen. http://www.livescribe.com/en-us/smartpen/ echo/.
[8] M. Pedley. High precision calibration of a three-axis accelerometer. http:// cache.freescale.com/files/sensors/doc/app_note/AN4399.pdf.
[9] M. Sperber, M. Klinkigt, K. Kise, M. Iwamura, B. Adrian, and A. Dengel. Handwriting reconstruction for a camera pen using random dot patterns. In
43
Frontiers in Handwriting Recognition (ICFHR), 2010 International Confer- ence on, pages 160--165, Nov 2010.
[10] H. Tu, X. Ren, and S. Zhai. A comparative evaluation of finger and pen stroke gestures. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI '12, pages 1287--1296, New York, NY, USA, 2012. ACM.
[11] Wacom. Inkling. http://www.wacom.com/en/us/en/creative/inkling.
[12] J.-S. Wang and F.-C. Chuang. An accelerometer-based digital pen with a tra- jectory recognition algorithm for handwritten digit and gesture recognition. Industrial Electronics, IEEE Transactions on, 59(7):2998--3007, July 2012.
[13] J.-S. Wang, Y.-L. Hsu, and C.-L. Chu. Online handwriting recognition us- ing an accelerometer-based pen device. 2nd International Conference on Ad- vances in Computer Science and Engineering, pages 229--232, 2013.
[14] J.-S. Wang, Y.-L. Hsu, and J.-N. Liu. An inertial-measurement-unit-based pen with a trajectory reconstruction algorithm and its applications. Industrial Electronics, IEEE Transactions on, 57(10):3508--3521, Oct 2010.
[15] J. Yang, W. Chang, W.-C. Bang, E.-S. Choi, K.-H. Kang, S.-J. Cho, and D.- Y. Kim. Analysis and compensation of errors in the input device based on inertial sensors. In Information Technology: Coding and Computing, 2004. Proceedings. ITCC 2004. International Conference on, volume 2, pages 790- -796 Vol.2, April 2004.