本研究之目的在於發展一套針對偏癱患者下肢運動之布氏動作階段(Brunnstrom recovery stage)自動分級系統。本研究藉由自製無線九軸慣性感測器，結合市售無線生理表面肌電圖擷取裝置所架構之量測系統，測量患者之下肢動作訊號；隨後，本研究對所量測之訊號進行分析，萃取出對於布氏動作階段分級有用之特徵，用以訓練本研究所提出基於決策規則之分類系統，分別比較k最佳鄰居法、人工類神經網路、以及支撐向量機等分類演算法之分類能力。交叉驗證則是使用更客觀的leave subjects out法取代過去常用之leave one out法，用以計算分類之成功率。實驗結果顯示使用支撐向量機演算法來分類布氏動作階段，相較於k最佳鄰居法、以及人工類神經網路能得到最高的正確率。本研究所提出之分類系統的穩健性則是透過訓練不同患者人數的資料進行驗證；並且根據其分類之結果，可以歸納出本研究所提出之自動分類演算法，具有準確預測偏癱患者下肢運動之布氏動作階段分類的潛力。

The purpose of this study is to develop an automatic system for classifying lower limb Brunnstrom stages of hemiparetic patients. In this study, the measurement system was employed both IMU and sEMG to acquire the lower limb motion signals from patients. Afterward, this study extracted some useful features for the proposed rule-based classification system and compared different classification algorithms such as k-nearest neighbor, artificial neural network and support vector machine.
Instead of leave one out cross validation, the leave subjects out cross validation was used to calculate the successful rate of classification. The result of the experiment shows that SVM has the highest accuracy to classify Brunnstrom stage than k-NN and ANN. The robustness of this classification system was verified by training different number of subject data. According to the classification result, it can be concluded that the proposed classification system has the potential to predict the lower limb Brunnstrom stage for hemiparetic patients.

[1] V. L. Feigin, M. H. Forouzanfar, R. Krishnamurthi, G. A. Mensah, M. Connor and D. A. Bennett, “Global and regional burden of stroke during 1990-2010: findings from the global burden of disease study 2010,” The Lancet, Vol. 383, Issue 9913, pp. 245-255, 2014.
[2] N. F. Gordon, M. Gulanick, F. Costa, G. Fletcher, B. A. Franklin and E. J. Roth, “Physical activity and exercise recommendations for stroke survivors,” Circulation, Vol. 109, pp. 2031-2041, 2004.
[3] T. Giorgino, P. Tormene, G. Maggioni, D. Capozzi, S. Quaglini and C. Pistarini, “Assessment of sensorized garments as flexible support to self-administered post-stroke physical rehabilitation,” European Journal of Physical and Rehabilitation Medicine, Vol. 45, No. 1, pp. 75-84, 2009.
[4] J. Langan, K. DeLave, L. Phillips, P. Pangilinan and S. H. Brown, “Home-based telerehabilitation shows improved upper limb function in adults with chronic stroke: a pilot study,” Journal of Rehabilitation Medicine, Vol. 45, No. 2, pp. 217-220, 2013.
[5] S. Brunnstrom, “Motor testing procedures in hemiplegia: based on sequential recovery stages,” Physical Therapy, Vol. 46, No. 4, pp. 357-375, 1966.
[6] U. B. Flansbjer, A. M. Holmback, D. Downham, C. Patten and J. Lexell, “Reliability of gait performance tests in men and women with hemiparesis after stroke,” Journal of Rehabilitation Medicine, Vol. 37, No. 2, pp. 75-82, 2005.
[7] M. D. Bland, A. Sturmoski, M. Whitson, L. T. Connor, R. Fucetola and T. Huskey, “Prediction of discharge walking ability from initial assessment in a stroke inpatient rehabilitation facility population,” Archives of Physical Medicine and Rehabilitation, Vol. 93, No. 8, pp. 1441-1447, 2012.
[8] H. Zhou and H. Hu, “Human motion tracking for rehabilitation-a survey,” Biomedical Signal Processing and Control, Vol. 3, Issue 1, pp. 1-18, 2008.
[9] S. Allin, N. Baker, E. Eckel and D. Ramanan, “Robust tracking of the upper limb for functional stroke assessment,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 18, No. 5, pp. 542-550, 2010.
[10] R. Schmidt, C. Disselhorst-Klug, J. Silny and G. Rau, “A marker-based measurement procedure for unconstrained wrist and elbow motions,” Journal of Biomechanics, Vol. 32, No. 6, pp. 615-621, 1999.
[11] P. S. Lum, C. G. Burgar, D. E. Kenney and H. F. M. Van der Loos, “Quantification of force abnormalities during passive and active-assisted upper-limb reaching movements in post-stroke hemiparesis,” IEEE Transactions on Biomedical Engineering, Vol. 46, No. 6, pp. 652-662, 1999.

[12] N. Jarrasse, M. Tagliabue, J. V. G. Robertson, A. Maiza, V. Crocher, A. Roby-Brami and G. Morel, “A methodology to quantify alterations in human upper limb movement during co-manipulation with an exoskeleton,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 18, No. 4, pp. 389-397, 2010.
[13] H. Zheng, N. D. Black and N. D. Harris, “Position-sensing technologies for movement analysis in stroke rehabilitation,” Medical & Biological Engineering & Computing, Vol. 43, No. 4, pp. 413-420, 2005.
[14] M. Sekine, Y. Abe, M. Sekimoto, Y. Higashi, T. Fujimoto, T. Tamura and Y. Fukui, “Assessment of gait parameter in hemiplegic patients by accelerometry,” Proceedings of The 22nd Annual Engineering in Medicine and Biology Society International Conference, Vol. 3, pp. 1879-1882, 2000.
[15] T. Liu, Y. Inoue and K. Shibata, “Development of a wearable sensor system for quantitative gait analysis,” Measurement, Vol. 42, Issue 7, pp. 978-988, 2009.
[16] D. Staudenmann, K. Roeleveld, D. F. Stegeman and J. H. van Dieen, “Methodological aspects of SEMG recordings for force estimation--a tutorial and review,” Journal of Electromyography and Kinesiology, Vol. 20, No. 3, pp. 375-387, 2010.
[17] F. Moosavi, A. Pasdar, H. Ehsani and M. Rostami, “An EMG-driven musculoskeletal model to predict muscle forces during performing a weight training exercise with a dumbbell,” Proceedings of The 19th Iranian Conference on Biomedical Engineering, pp. 79-84, 2012.
[18] C. Fleischer and G. Hommel, “A human--exoskeleton interface utilizing electromyography,” IEEE Transactions on Robotics, Vol. 24, No. 4, pp. 872-882, 2008.
[19] O. Tunçel, K. Altun and B. Barshan, “Classifying human leg motions with uniaxial piezoelectric gyroscopes,” Sensors, Vol. 9, No. 11, pp. 8508-8546, 2009.
[20] H. Y. Lau, K. Y. Tong and H. Zhu, “Support vector machine for classification of walking conditions of persons after stroke with dropped foot,” Human Movement Science, Vol. 28, No. 4, pp. 504-514, 2009.
[21] Q. L. Li, Y. Song and Z. G. Hou, “Estimation of lower limb periodic motions from sEMG using least squares support vector regression,” Neural Processing Letters, Vol. 41, Issue 3, pp. 371-388, 2015.
[22] C. J. De Luca, “Surface electromyography: detection and recording,” Available: http://www.delsys.com/Attachments_pdf/WP_SEMGintro.pdf
[23] E. A. Clancy, E. L. Morin and R. Merletti, “Sampling, noise-reduction and amplitude estimation issues in surface electromyography,” Journal of Electromyography and Kinesiology, Vol. 12, No. 1, pp. 1-16, 2002.

[24] A. R. Webb and K. D. Copsey, Statistical Pattern Recognition, 3rd ed., John Wiley & Sons, 2011.
[25] S. Haykin, Neural Networks: A Comprehensive Foundation, 2nd ed., Macmillan, 1994.
[26] V. Vapnik, The Nature of Statistical Learning Theory, 2nd ed., Springer, 2000.
[27] N. Cristianini and J. Shawe-Taylor, An Introduction to Support Vector Machines and Other Kernel-based Learning Methods, Cambridge University Press, 2000.
[28] C.-C. Chang and C.-J. Lin, “LIBSVM: a library for support vector machines,” ACM Transactions on Intelligent Systems and Technology, Vol. 2, No. 3, pp. 1-27, 2011.
[29] J. Yousefi and A. Hamilton-Wright, “Characterizing EMG data using machine-learning tools,” Computers in Biology and Medicine, Vol. 51, pp. 1-13, 2014.
[30] S. D. Jush, C. H. Wang, C. L. Hsieh, M. H. Chen, and C. L. Chen, “The Brunnstrom Recovery Scale: Its Reliability and Concurrent Validity,” Journal of Occupational Therapy Association R.O.C., Vol. 14, No. 1, pp. 1-12, 1996.