傳統的中風後復健評估方式中，臨床上大多使用例如傅格－梅爾評估量表、沃夫動作功能評量、及坦帕手功能量表等李克特量表，傳統評估量表雖已使用多年且評估結果被各界所接受，但由於評估結果多為治療師主觀認定，故評估給分會因治療師而異，且李克特量表是依級距給分，會因為個案狀況介於兩級距間造成給分無法精確描述個案復健成效的情況。
本研究首先利用九軸慣性感測元件及ZigBee無線傳輸模組，發展一套中風後復健評估使用的無線慣性感測系統；然後基於傳統評估量表的評估原則，從手部動作所量測的慣性感測訊號，提取三個顯示復健成效的客觀量化指標；至於量化指標之權重決定，利用中風病患案例肩屈動作所量測慣性感測訊號，參考沃夫動作功能評量分數，以最小平方法經線性迴歸統計而獲得，進而計算個案的客觀評估分數，作為個案復健成效結果。由指標權重可得知三個量化指標在傳統量表評估準則中所佔的重要程度；又客觀評估分數描述個案復健成效，使中風患者能了解上肢復原程度，增加其持續復健的意願。

In clinical settings, traditional stroke rehabilitation evaluation methods typically include FMA, WMFT, TEMPA, and other Likert-type assessment scales. Although traditional assessment scales have been used for many years and the evaluation results are accepted widely in various fields, they are scored by occupational therapist subjectively, and the variations of assessment results depend on individual directly. Furthermore, Likert scales give scale scores based on numerical ranges. An individual case’s score between two scales may be inaccurate to describe the rehabilitation result.
In this study, first we employ nine-axis inertial measurement unit and ZigBee wireless transmission module to construct a wireless inertial measurement system for stroke rehabilitation evaluation. Then, we acquire inertial signals from upper extremity and extract three significant indicators reflecting rehabilitation performance during stroke patients’ movement exam, i.e. shoulder flexion. As to the decision on the weightings of three indicators, we relate WMFT scores to the significant indicators of stroke patients’ inertial signals measured from shoulder flexion movement, and employ the least squares method through linear regression. Therefore, an individual rehabilitation performance can be obtained by an objective evaluation score. From the indicator weights, we can realize the significant of the three quantitative indicators in traditional evaluation criteria, and can also describe the rehabilitation performance of each patient objectively. The objectively evaluated scores are able to indicate rehabilitation performance, and to enhance the intention of rehabilitation tasks.

[1] A. Fugl-Meyer, L. Jaasko, I. Leyman, S. Olsson, and S. Steglind, “The post-stroke hemiplegic patient. 1. a method for evaluation of physical performance.,” Scand J Rehabil Med, vol. 7, no. 1, pp. 13–31, 1975.
[2] S. L. Wolf, P. A. Catlin, M. Ellis, A. L. Archer, B. Morgan, and A. Piacentino, “Assessing Wolf Motor Function Test as Outcome Measure for Research in Patients After Stroke,” Stroke, vol. 32, no. 7, pp. 1635–1639, 2001.
[3] J. Desrosiers, R. Hébert, E. Dutil, and G. Bravo, “Development and Reliability of an Upper Extremity Function Test for the Elderly: The TEMPA,” Can J Occup Ther, vol. 60, no. 1, pp. 9–16, 1993.
[4] 董憲奇, “肘關節神經復健用機器人之改進與臨床研究,” 國立成功大學, 碩士論文, 2002.
[5] H. Zheng, N.D. Black, and N.D. Harris, “Position-Sensing Technologies for Movement Analysis in Stroke Rehabilitation,” Med. Biol. Eng. Comput, vol. 43, pp. 413-420, 2005.
[6] J. Kim, S. Yang, and M. Gerla, “StrokeTrack: Wireless Inertial Motion Tracking of Human Arms for Stroke Telerehabilitation,” mHealthSys '11, no. 4, 2011.
[7] G. Uswatte, W. H. R. Miltner, B. Foo, M. Varma, S. Moran, and E. Taub, “Objective Measurement of Functional Upper-Extremity Movement Using Accelerometer Recordings Transformed With a Threshold Filter,” Stroke, vol. 31, no. 3, pp. 662–667, 2000.
[8] G. Uswatte, W. L. Foo, H. Olmstead, K. Lopez, A. Holand, and L. B. Simms, “Ambulatory Monitoring of Arm Movement Using Accelerometry: An Objective Measure of Upper-Extremity Rehabilitation in Persons With Chronic Stroke,” Archives of Physical Medicine and Rehabilitation, vol. 86, no. 7, pp. 1498–1501, 2005.
[9] G. Thrane, N. Emaus, T. Askim, and A. Anke, “Arm Use in Patients with Subacute Stroke Monitored by Accelerometry: Association with Motor Impairment and Influence on Self-Dependence,” Journal of Rehabilitation Medicine, vol. 43, no. 4, pp. 299–304, 2011.
[10] S. Beeby, G. Ensell, M. Kraft, and N. White, Mems Mechanical Sensors. Artech House, 2004.
[11] 李世鴻, 微機電系統工程. 五南圖書出版股份有限公司, 2003.
[12] J. Lenz, and A. D. Edelstein, “Magnetic Sensors and Their Applications,” IEEE Sensors Journal, vol. 6, no. 3, pp. 631–649, 2006.
[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.” .
[16] 廖健興, “無線個人區域網路(WPAN)技術發展與應用概論,” ICEQ報導第五十期, 2006.
[17] E. Criswell, Introduction to Surface Electromyography, 2nd ed. Jones and Bartlett Publishers, 2011.
[18] InvenSense, “MPU-6050 Product Specification Rev 3.3.”
[19] AsahiKASEI, “AK8975 Electronic Compass.”
[20] “SIOC實驗板硬體手冊.” 浯陽科技有限公司, 2010.
[21] “XBee/XBee-Pro RF Modules Product Manual.” DIGI, 2012.
[22] NXP Semiconductors, “I2C-bus specification and user manual Rev 4.” 2012.
[23] S. C. Ergrn, “ZigBee/IEEE 802.15.4 Summary.” 2004.
[24] 張靜, 金志華, 田蔚風, “無航向基準時數字式磁羅盤的自差校正,” 上海交通大學學報, vol. 38, no. 10, pp. 1757–1760, 2004.
[25] 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.
[26] 陳柏全, “應用於中風後肩關節復健之慣性量測系統開發與新量化評估方法,” 國立中央大學, 碩士論文, 2012.