具感知訊號視覺回饋及客觀評量之復健無線傳輸儀器研發

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：22

、訪客IP：3.145.32.238

姓名

彭志軒(Zhi-Xuan Peng) 查詢紙本館藏

畢業系所

電機工程學系

論文名稱

具感知訊號視覺回饋及客觀評量之復健無線傳輸儀器研發
(Study and Development of Rehabilitation Assistive Devices with the Functions of Wireless Sensing Feedback and Objective Assessment)

相關論文

★ 獨立成份分析法於真實環境中聲音訊號分離之探討	★ 口腔核磁共振影像的分割與三維灰階值內插
★ 數位式氣喘尖峰氣流量監測系統設計	★ 結合人工電子耳與助聽器對中文語音辨識率的影響
★ 人工電子耳進階結合編碼策略的中文語音辨識成效模擬--結合助聽器之分析	★ 中文發聲之神經關聯性的腦功能磁振造影研究
★ 利用有限元素法建構3維的舌頭力學模型	★ 以磁振造影為基礎的立體舌頭圖譜之建構
★ 腎小管之草酸鈣濃度變化與草酸鈣結石關係之模擬研究	★ 口腔磁振影像舌頭構造之自動分割
★ 微波輸出窗電性匹配之研究	★ 以軟體為基準的助聽器模擬平台之發展-噪音消除
★ 以軟體為基準的助聽器模擬平台之發展-回饋音消除	★ 模擬人工電子耳頻道數、刺激速率與雙耳聽對噪音環境下中文語音辨識率之影響
★ 用類神經網路研究中文語音聲調產生之神經關聯性	★ 教學用電腦模擬生理系統之建構

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

[檢視]

[下載]

本電子論文使用權限為同意立即開放。
已達開放權限電子全文僅授權使用者為學術研究之目的，進行個人非營利性質之檢索、閱讀、列印。
請遵守中華民國著作權法之相關規定，切勿任意重製、散佈、改作、轉貼、播送，以免觸法。

摘要(中)

近年國人飲食習慣西化，中風病患的數量逐漸增長，復健成為醫院主要項目之一，而復健的過程中需要可量化的評估指標來呈現病患的復健情況，因此希望藉由肌電訊號(Electromyography, EMG)觀察病人復健時肌肉的出力情形，透過肌電訊號可得知肌肉收縮強度的大小，進一步分析將能提供治療師重要的量化評估指標。研究首先利用以NI儀器所建構的肌電感測系統測量27位中風病患復健前、後的肌電訊號，其中11人以傳統方式進行復健，另外16人透過虛擬實境進行復健，把肌電訊號的結果與慣性感測和醫師復健評估量表的結果互相比對，驗證肌電訊號RMS與CI指標之可靠性，在實驗結果中發現對於發病超過6個月後以虛擬實境方式進行復健的8位病患，有7位皆呈現進步的情形，顯示虛擬情境對於復健療程的效果有一定程度上的幫助，並將27位中風病患的復健前後肌電訊號與復健前後沃夫動作功能評量分數進行比對，發現其中18位有相同進步或退步的趨勢。本研究以XBee傳輸模組搭配STM32系統板發展一套輕量化多通道的無線肌電感測系統，體積從原本舊系統的10.5cm × 5cm × 6cm縮小為6cm × 2.5cm × 3cm，重量從101.3g降為30.6g，以LabVIEW設計即時分析的觀測介面，透過圖形顯示呈現每個通道即時的肌力強度，即時CI指標讓使用者瞭解肌肉間的收縮關係，並以燈號回饋告知使用者是否使用正確的肌肉區塊。系統開發完成後，為測試訊號品質，利用本系統與Shimmer公司已商業化生產的無線肌電量測系統和以SIOC實驗板搭配Zigbee為基礎的舊系統進行量測二頭肌的肌電訊號SNR比較實驗，結果顯示本系統的SNR為28.69dB，舊系統的SNR為28.10dB，而Shimmer肌電量測儀器的SNR為28.97dB，顯示本系統訊號品質比舊系統好，且與Shimmer肌電量測儀器相近，並對三套系統輸入相同的正弦波訊號，分別計算各自的SNR，本系統的SNR為21.39dB，舊系統的SNR為19.41dB，Shimmer系統的SNR為22.65dB，結果與二頭肌的肌電訊號SNR比較實驗呈現相同的趨勢，另外也進行肌肉疲勞指數的測量；在八顆感測器穩定度實驗中，感測器的訊號SNR均在28dB左右，變動率最高僅0.63%，說明訊號不會隨不同顆感測器而有太大差異。接著對5位健康個案(5男, 年齡23-25歲)和5位中風(4男1女, 年齡分別為56、28、64、45與28歲)病患進行多顆感測器的量測，針對前舉、外展與手肘彎曲90度動作量測手臂與肩頸的肌電訊號，並比較健康個案與病患的差異；另一方面比較同一病患於本系統與NI系統的CI指標分析結果，顯示以本系統量測時病患的手臂抬高角度較高；最後將5位病患透過新系統與NI系統所量測到的肌電訊號RMS最大值進行樣本T檢定，結果顯示五位病患使用兩種不同系統所量測到的資料沒有顯著差異(p > 0.05)，說明以新系統量測能在不影響實驗結果的情況下提升使用者的動作流暢度與行動力。以上在本系統的量測過程中使用者皆可透過觀測介面中即時CI指標與燈號得知肌群出力模式，總結以上結果顯示肌電訊號不僅能以RMS評估病患肌耐力回復程度，計算中位數頻率得知肌肉疲勞情況，且經由多通道肌電訊號的量測並分析CI指標可觀察不同動作肌肉間的出力模式，評估病患的協調性。

摘要(英)

In recent years, our westernized eating habits have led to the growth of stroke patient numbers. Therefore, the stroke rehabilitation has become one of the main important facilities in the hospital. The rehabilitation of a stroke patient can be presented by quantitative assessment index. Thus, it is desired to obtain the patient′s rehabilitation condition by measuring the electromyography (EMG) of the patients. Further, through the analysis of EMG, we can provide a quantitative indicator of muscle strength which is intended for the physical therapist. In this study, the EMG measurement system constructed by NI instruments was first used to measure a total of twenty-seven stroke patients′ EMG signals which were obtained before and after rehabilitation. Sixteen of them conducted the rehabilitation by using virtual reality (VR). Then we compared the EMG analysis result with the inertial measurement results and also with the Wolf motor function test (WMFT) to find the same trend in order to verify the reliability of EMG indicators. The experimental results showed that the patients who have more than six months to conduct virtual reality rehabilitation have excellent results. The 18 out of 27 patients presented the same trend in the EMG results and the Wolf motor function test. This study used XBee module and STM32 system board to develop a small-sized and multi-channel wireless EMG measurement system with a LabVIEW-based software interface to monitor, display and analyze the signal. The new system (6cm × 2.5cm × 3cm, 30.6g) has significantly reduced size and weight compared to the old system (10.5cm × 5cm × 6cm, 101.3g). The interface can display the muscle strength with bars and analyze the real-time co-contraction index to inform the users whether they used the correct muscles with the green light. In order to examine the signal quality of the sensors, EMG signals were first measured from five healthy subjects (5 males, age 23-25) with three different wireless EMG measurement systems (i.e., New system, Old system and Shimmer system) in terms of signal-to-noise ratio (SNR). The results showed that both the new system (28.69dB) and the Shimmer system (28.10dB) were better than the old system (28.94dB). For comparison purpose, the same sinusoidal signal was selected as input for three different systems (i.e., New system, Old system and Shimmer system) to obtain 21.39dB, 19.41dB, and 22.65dB, respectively. The spectral fatigue indices of three systems also indicated obvious appearance of muscle fatigue. Before performing the multi-channel EMG measurement, the signal quality of the sensors had been examined whether they were identical or not. The results showed that the sensors’ signal SNR were about 28dB with 0.63% variation. We then conducted the multi-channel EMG measurement on the same five healthy subjects and five stroke patients (4 male and 1 female, age 56, 28, 64, 45 and 28) with three different movements (i.e., shoulder flexion, shoulder abduction and elbow flexion) and compared the differences between the healthy subjects and stroke patients. We also compared the CI index of EMG measured by the new system and the NI-based system. Finally, the EMG signals of five patients were measured with the new system and NI-based system and analyzed with the Paired-Samples T Test. The result showed that there was no significant difference (p > 0.05) between the data measured with the two systems. This means that the new system could streamline the movement and mobility of the body without affecting the experimental results. Through the real-time RMS, CI index, and green light indicator on the interface, the subjects can immediately know the situation of their muscles. The above results showed that EMG signals were not only able to be used to assess the degree of muscle strength by RMS and muscle fatigue by using median frequency, but also able to be used to observe the contribution mode of muscles through multi-channel EMG measurement.

關鍵字(中)

★ 肌電訊號
★ 無線量測
★ Zigbee
★ CI指標
★ 中位數頻率

關鍵字(英)

★ Electromyography
★ Wireless measurement
★ Zigbee
★ CI index
★ Median frequency

論文目次

摘要 I
Abstract IV
致謝 IV
目錄 IX
圖目錄 XI
表目錄 XVII
第一章緒論 1
1.1研究動機 1
1.2文獻探討 4
1.2.1肌電訊號之簡介與量測 4
1.2.2肌電訊號的處理與應用 6
1.3研究目的 12
1.4論文架構 13
第二章中風病患之復健評估 14
2.1基於NI儀器所建構之肌電訊號量測系統 14
2.2復健動作與肌電訊號評估參數指標之簡介 15
2.3肌電訊號評估參數指標可靠性之驗證 17
第三章多通道無線量測系統架構 33
3.1肌電訊號量測前置硬體電路 33
3.2肌電訊號類比數位轉換與無線傳輸模組 35
3.3無線肌電訊號量測系統軟體架構 39
第四章實驗結果與討論 43
4.1無線肌電訊號量測系統比較 43
4.2肌肉疲勞度量測 51
4.3多通道肌電訊號量測 53
4.4即時分析介面之感知回饋 62
4.5病患多通道肌電訊號量測 66
第五章結論與未來展望 82
5.1結論 82
5.2未來展望 82
參考文獻 87
附錄A 90
附錄B 94
附錄C 98

參考文獻

Brookham, R. L., Middlebrook, E. E., Grewal T., and Dickerson C. R., (2011). “The utility of an empirically derived co-activation ratio for muscle force prediction through optimization.” Journal of Biomechanics 44, 1582-1587
Brookham, R. L., and Dickerson, C. R., (2013). “Empirical quantification of internal and external rotation muscular co-activation ratios in healthy shoulders.” Journal of Medical and Biological Engineering 50, 257-264.
Criswell,E., (2011), Cram′s Introduction to Surface Electromyography Second Edition, Sudbury: Jones and Bartlett, Part I
Dowling, A., Barzilay, O., Lombrozo,Y., and Wolf, A., (2014). “An Adaptive Home-Use Robotic Rehabilitation System for the Upper Body.” IEEE Journal of Translational Engineering in Health and Medicine 02.
ExRx.net, (2012) Exercise & Muscle Directory
http://exrx.net/Lists/Directory.html
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.
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.
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.
Khokhar, Z. O., Xiao, Z. G., and Menon, C., (2010). “Surface EMG pattern recognition for real-time control of a wrist exoskeleton.” BioMedical Engineering OnLine 09.
Liu J., and Zhou P., (2013) “A Novel Myoelectric Pattern Recognition Strategy for Hand Function Restoration After Incomplete Cervical Spinal Cord Injury.” Transactions on Neural Systems and Rehabilitation Engineering 21, 96-103
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.
Milenkovic, A., Otto, C., and Jovanov, E., (2006). “Wireless sensor networks for personal health monitoring: Issues and an implementation.” Wirelsess Senson Networks and Wired/Wireless Internet Communications 29, 2521-2533.
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.
Ma, S., Varley, M., Shark, L., and Richards, J., (2010). “EMG biofeedback based VR system for hand rotation and grasping rehabilitation.” International Conference Information Visualisation 14, 479-484
Mango, M., Benini, L., Gaggero, L., La Torre Aro, J.P., and Popovici, E., (2013). “A versatile biomedical wireless sensor node with novel drysurface sensors and energy efficient power management.” IEEE International Workshop on Advances in Sensors and Interfaces 05, 217-222
Raikova, R. T., and Aladjov, H. T., (2002). “Hierarchical genetic algorithm versus static optimization-investigation of elbow flexion and extension movements.” Journal of Biomechanics 35, 1123-1135.
Sobahi, N. M., (2011). “Denoising of EMG Signals Based on Wavelet Transform.” Asian Transactions on Engineering 01, 17-23.
Segil, J. L., and Weir, R. F., (2014). “Design and Validation of a Morphing Myoelectric Hand Posture Controller Based on Principal Component Analysis of Human Grasping.” IEEE Transactions on Neural Systems and Rehabilitation Engineering 22, 249-257.
Tsai, A. C., Hsieh, T. H., Luh, J. J., and Lin, T. T., (2014). “A comparison of upper-limb motion pattern recognition using EMG signals during dynamic and isometric muscle contractions.” Biomedical Signal Processing and Control 11, 17-26.
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.
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
Xiong, A., Lin, G., Zhaol, X., Han, J., and Liu, G., (2012).“Feasibility of EMG-Based ANN Controller for a Real-Time Virtual Reality Simulation.” Annual Conference on IEEE Industrial Electronics Society 38, 2699-2704
Xu, L., Rabotti, C., and Mischi, M., (2013). “Novel Vibration-Exercise Instrument With Dedicated Adaptive Filtering for Electromyographic Investigation of Neuromuscular Activation.” IEEE Transactions on Neural Systems and Rehabilitation Engineering 21, 275-282
莊威宏 (2012).“以Zigbee發展之多通道無線肌電圖量測系統.” 中央大學電機工程學系碩士論文.
慈愛中風資訊網, (2012). “穩定期的復健治療” http://stroke.tw/page/6-1.html

指導教授

吳炤民(Chao-Min Wu)

審核日期

2014-8-25

推文