以Zigbee發展之多通道無線肌電圖量測系統

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姓名

莊威宏(Wei-Hung Chuang) 查詢紙本館藏

畢業系所

電機工程學系

論文名稱

以Zigbee發展之多通道無線肌電圖量測系統
(Zigbee-based multi-channel wireless Electromyography measurement system)

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摘要(中)

中風病患的復健過程中，測量肌電訊號不僅能評估患者肌耐力回復程度的指標，而且能藉由多通道肌電訊號的量測觀察不同肌肉間的出力模式評估出病人的協調性。本論文的研究目的即是利用低耗能小體積的無線傳輸模組發展一套輕便的多通道無線肌電訊號量測系統，以便病患量測。在研究一開始先利用NI公司生產的NI9205與WLS9163達到肌電訊號的數位化與資料傳輸進而完成一套初步的肌電量測系統，然而類比肌電訊號在經過長距離導線傳輸到NI9205轉成數位資料之間受到許多雜訊干擾影響訊號品質，因此本研究接著利用Xbee傳輸模組與SIOC開發板結合開發一套可多通道量測的無線肌電量測系統，並且利用Labview設計一套觀測與分析介面，將量測完成儲存下來的肌電訊號分析出有用的數據，並且可在之後利用Labview進行介面的調整與需求功能的追加。為了測試感測器的訊號品質，我們對四位無疾病正常的受試者(3男1女, 年齡23-25)進行同一個案的不同量測與不同個案間量測的差異性測試。本研究使用了Shimmer公司的無線肌電量測系統當成參照，與本研究利用Zigbee傳輸協定完成的測量系統和利用NI公司的無線傳輸產品完成的測量系統三者比較，將各個系統單顆感測器測得的肌電訊號進行比較分析，同時也進行了肌肉疲勞指數的測量，結果顯示同一個案的訊號品質比較中，SNR平均值較高的系統其標準差較大，其因為高訊雜比的量測系統其SNR值對於肌電訊號的波動較為敏感；而在不同個案間的量測系統訊號品質比較中，Zigbee的訊號品質與Shimmer訊號品質在個案間平均SNR值表現為22.7 1.38 dB與23.6 1.98 dB及變動量分別為6% 與 8.3%，表示訊號品質相近，疲勞指數的量測也均顯示出頻率指數可明顯的觀察到疲勞現象；在多通道測量之前，先行測試多顆感測器訊號品質是否一致，結果顯示感測器的訊號品質SNR均在24dB左右，接著進行的多通道肌電訊號量測，同樣為四位無疾病正常的受試者進行測量，並藉由比較肌肉共同收縮係數(Co-contraction index, CI)整理出不同動作肌肉間的出力關係，在最後探討三位病患的肌電訊號中，觀察出患者肌電數據異常處，並且可看出復健前與復健後明顯的肌肉出力模式回歸正常與肌力增強的現象，總結以上結果可看出藉由肌電訊號的量測，不僅可利用Vpp與RMS得知正常人的出力情形、計算median frequeny得知肌肉疲勞程度，也能藉由CI參數的運算得知肌群的出力模式，進而利用這些參數成為病患復健過程中肌力與動作協調性的參考指標。

摘要(英)

In the course of stroke patient rehabilitation, electromyography (EMG) provides not only an indicator of muscle strength and endurance but also an observation on co-ordination of muscles. The purpose of this study is to develop a small-sized wireless EMG measurement system for patient’s convenience. In this study, commercial available data acquisition (NI9205) and wireless transmission (WLS9163) devices were first used for acquiring and transmitting EMG signal to test the function of the signal conditioning circuits. However, the noise interference induced by the long lead wire between the electrode and NI9205 contaminated the EMG signal. This study used Zigbee module and embedded system board to develop a better wireless electromyography measurement system with a Labview-based software tool to monitor, display and analyze the signal. The function of the software tool could be expanded with Labview. In order to verify the signal quality of sensor, intra- and inter-subject variability of EMG signals were measured on four healthy subjects (3 males and 1 female, age 23-25) with three different wireless EMG measurement systems ( i.e., Shimmer research Inc., our Zigbee-based and NI-based measurement systems) in terms of signal-to-noise ratio (SNR) and spectral fatigue index. The results show that both Zigbee-based system (22.7 1.38 dB) and Shimmer system (23.6 1.98 dB) were with similar inter-subject SNRs and variation (6% vs. 8.3%). Spectral fatigue indices of both systems also indicated obvious appearance of muscle fatigue. Before multi-channel EMG measurement, whether the signal quality of sensors is identical has been examined. The results show that sensors’ signal SNRs are all about 24 1.5dB. We then conducted the multi-channel EMG measurement on the same four healthy subjects with three different movements (i.e., shoulder flexion, abduction, and extention). By calculating Co-contraction index (CI), characteristics in different movements were identified. Finally, the EMG signals of three stroke patients were measured and analyzed. Based on the CI calculation, the EMG signals before and after rehabilitation could show muscular recovery from abnormal waveform before rehabilitation and muscular power enforced after rehabilitation. These results imply that EMG signals could be used as an index of efficacy for the rehabilitation.

關鍵字(中)

★ 肌電訊號
★ 無線量測
★ Zigbee
★ 微控制器應用

關鍵字(英)

★ Electromyography
★ Wireless measurement
★ Zigbee
★ Microcontroller imply

論文目次

摘要 I
ABSTRACT III
致謝 IV
目錄 V
圖目錄 VIII
表目錄 XII
第一章緒論 1
1.1 研究動機 1
1.2 文獻探討 7
1.2.1 肌電訊號量測與處理應用 7
1.2.2 生理訊號無線量測 11
1.3 研究目的 14
1.4 論文架構 14
第二章多通道肌電量測儀 16
2.1 肌電訊號簡介 16
2.2 肌電量測前置測試硬體架構 17
2.2.1 肌電訊號前置濾波放大電路 18
2.2.2 肌電訊號類比數位轉換與無線傳輸模組 20
2.3 肌電量測系統軟體架構 21
2.4 測試量測結果 24
第三章以ZIGBEE為基底之無線量測系統 26
3.1 無線個人網路(WIRELESS PERSONAL AREA NETWORK) 26
3.2 無線肌電訊號量測系統硬體架構 27
3.3 無線肌電訊號量測系統軟體架構 33
第四章實驗結果與討論 37
4.1 無線肌電訊號量測系統比較(實驗一) 37
4.2 肌肉疲勞度量測(實驗二) 45
4.3 多通道肌電訊號量測(實驗三) 46
4.4 多通道肌電量測儀病患量測 52
第五章結論與未來展望 65
5.1 結論 65
5.2 未來展望 67
參考文獻 68
附錄A 72
附錄B 76
附錄C 80
附錄D 82

參考文獻

Antfolk, C., Cipriani, C., Controzzi, M., Carrozza, M. C., Lundborg, G., Rosén, B., and Sebelius, F., (2010). “Using EMG for Real-time Prediction of Joint Angles to Control a Prosthetic Hand Equipped with a Sensory Feedback System.” Journal of Medical and Biological Engineering 30, 399-406.
Bonato , P., D’Alessio ,T., and Knaflitz, M., (1998).” A Statistical Method for the Measurement of Muscle Activation Intervals from Surface Myoelectric Signal During Gait.” IEEE Transactions on Biomedical Engineering 45, 287-299.
Boquete, L., Ascariz, J. M. R., Cantos, J., Barea, R.,Miguel, J. M., Ortega, S., and Peixoto, N., (2012).” A portable wireless biometric multi-channel system.” Measurement 45, 1587-1598.
Criswell,E., (2011). Cram’s Introduction to Surface Electromyography. Jones & Bartlett Learning.Burnie,D., 1995. The concise encyclopedia of the human body. DK CHILDREN.
Disselhorst-Klug , C., Silny, J., and Rau, G., (1997).” Improvement of Spatial Resolution in Surface-EMG: A theoretical and experimental comparison of different spatial filters.” IEEE Transactions on Biomedical Engineering 44, 567-574.
ExRx.net, (2012) Exercise & Muscle Directory
http://exrx.net/Lists/Directory.html
Farina, D., Cescon, C., (2001). “Concentric-Ring Electrode Systems for Noninvasive Detection of Single Motor Unit Activity.” IEEE Transactions on Biomedical Engineering 48,1326-1334.
Ghasemzadeh, H., Jafari, R., and Prabhakaran, B., (2010). “A Body Sensor Network With Electromyogram and Inertial Sensors: Multimodal Interpretation of Muscular Activities.” IEEE Transactions on Information Technology in Biomedicine 14, 198-206.
Georgakis , A. , Stergioulas, L. K., and Giakas G., (2003). “Fatigue Analysis of the Surface EMG Signal in Isometric Constant Force Contractions Using the Averaged Instantaneous Frequency.” IEEE Transactions on Biomedical Engineering 50, 262-265.
Holden, M.K., Dyar, T.A., and Dayan-Cimadoro, L., (2007). “Telerehabilitation Using a Virtual Environment Improves Upper Extremity Function in Patients With Stroke.” IEEE Transactions on Neural Systems and Rehabilitation Engineering 15, 36-42.
Hidler, J. M., Carroll, M., and Federovich, E. H., (2007). “Strength and Coordination in the Paretic Leg of Individuals Following Acute Stroke.” IEEE Transactions on Neural Systems and Rehabilitation Engineering 15, 526-534.
Hsieh, Y.W., , Lin, J.H., , Wang, C.H., Sheu, C.F., Hsueh, I. P., and Hsieh, C.L., (2007). “Discriminative, Predictive and Evaluative Properties of the Simplified Stroke Rehabilitation Assessment of Movement Instrument in Patients with Stroke.” Journal of Rehabilitation Medicine 39, 454-460.
Inbar G.F., Noujaim, A.E., (1984). “On Surface EMG Spectral Characterization and Its Application to Diagnostic Classification.” IEEE Transactions on Biomedical Engineering 31, 597-604.
IEEE standards association, (2012). 802.15.4 802.15.1 http://standards.ieee.org/findstds/standard/802.15.1-2005.html
Morris D.M., Uswatte, G., Crago , J. E., Cook III, E.W., and Taub , E., (2001). “The reliability of the Wolf Motor Function Test for assessing upper extremity function after stroke.” Physical Medicine and Rehabilitation 82, 750-755.
Merlo, A.,Farina, D., and Merletti, R., (2003). “A Fast and Reliable Technique for Muscle Activity Detection From Surface EMG Signals.” IEEE Transactions on Biomedical Engineering 50, 316-323.
Merletti, R.,Parker, P., (2004). ELECTROMYOGRAPHY Physiology, Engineering, and Noninvasive Applications. Institute for Electrical and Electronics Engineers, Inc.
Nishihara , K., Kawai , H., Gomi, T., Terajima, M., and Chiba, Y., (2010). “Investigation of Optimum Electrode Locations by Using an Automatized Surface Electromyography Analysis Technique.” IEEE Transactions on Biomedical Engineering 55, 636-642.
Park ,E. , Meek, S. G., (1993). “Fatigue Compensation of the Electromyographic Signal for Prosthetic Control and Force Estimation.” IEEE Transactions on Biomedical Engineering 40, 1019-1023.
Thexton, A.J., (1996). “A randomisation method for discriminating between signal and noise in recordings of rhythmic electromyographic activity.” Journal of Neuroscience Methods 66, 93-98.
Terrier, A., Reist, A., Voge, A., and Farron, A., (2007). “Effect of supraspinatus deficiency on humerus translation and glenohumeral contact force during abduction.” Clinical Biomechanics 22, 645-651.
Timmermans, A.A.A., Seelen, H.A. M., Geers, R. P. J., Saini , P. K., Winter , S.,Vrugt , J., and Kingma , H., (2010). “Sensor-Based Arm Skill Training in Chronic Stroke Patients: Results on Treatment Outcome, Patient Motivation, and System Usability.” IEEE Transactions on Neural Systems and Rehabilitation Engineering 18, 284-292.
Vehkaoja, A. T., Verho, J. A., Puurtinen, M. M., Nöjd, N. M., Lekkala, J. O., and Hyttinen, J. A., (2005). “Wireless Head Cap for EOG and Facial EMG Measurements.” IEEE Engineering in Medicine and Biology 27, 310-328.
Vergari, F., Auteri, V., Corsi, C., and Lamberti, C., (2009). “A ZigBee-based ECG transmission for a low cost solution in home care services delivery.” Mediterranean Journal of Pacing and Electrophysiology.
Kobayashi, H., (2011). “A ZigBee based wireless EMG/ECG streaming system for the universal interface.” IEEE Industrial Electronics Society 37, 2094-2099.
行政院衛生署統計室, (2010). “主要死因統計” http://www.doh.gov.tw/statistic/eBAS/%E7%94%9F%E5%91%BD%E7%B5%B1%E8%A8%88/%E4%B8%BB%E8%A6%81%E6%AD%BB%E5%9B%A0%E7%B5%B1%E8%A8%88/%E4%B8%BB%E8%A6%81%E6%AD%BB%E5%9B%A0%E7%B5%B1%E8%A8%88.xls
慈愛中風資訊網, (2012). “穩定期的復健治療” http://stroke.tw/page/6-1.html
林立民 (2010).”以PDA為平台的生理訊號測量系統,”中央大學電機工程學系碩士論文.
陳家禾 (2007).”整合式無線肌電圖及位置偵測系統用於評估動態控制之下之骨盆肌肉,” 國立成功大學醫學工程學系碩士論文.
吳思穎 (2003). “上肢復健機器臨床試驗與改良,” 國立成功大學機械工程學系碩士論文.
王致中 (2011). “使用加速度計和陀螺儀之跌倒偵測系統,” 大同大學資訊工程研究所碩士論文.

指導教授

吳炤民(Chao-Min Wu)

審核日期

2012-12-4

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