簡易檢索 / 詳目顯示

研究生: 張文穎
Chang, Wen-Ying
論文名稱: 投射式電容於健康照護之開發
The Development of Projected Capacitive Array Sensing in Healthcare
指導教授: 楊慶隆
Yang, Chin-Lung
學位類別: 博士
Doctor
系所名稱: 電機資訊學院 - 電機工程學系
Department of Electrical Engineering
論文出版年: 2015
畢業學年度: 103
語文別: 英文
論文頁數: 63
中文關鍵詞: 可撓性投射式電容床墊充電時間閘環身體異動呼吸偵測多重生理參數檢測儀
外文關鍵詞: flexible projected capacitive sensing mattress, charge-timing, guard ring, body movement, respiration detection, polysomnography
相關次數: 點閱:120下載:0
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報
  • 本研究採二階段進行投射式電容於健康照護開發。第一階段為建立一可撓性投射式電容床墊 (flexible projected capacitive sensing mattress, FPCSM) 供睡姿辨識,其次為經由此床墊達到健康照護輔助。以高靈敏且精確的電容數位轉換 ( Capacitance-to-digital conversion, CDC) 手法來量測電極的投射電容,透過開發過程掌握了大面積的感測電極特性。為了在大面積應用下電極電容能有更好的精確量測,電極的面積、箳蔽的使用、傳輸線的長度等等都需留意。為了提供使用者臥躺的舒適性,選用了可撓性基材。在FPCSM床墊中含括了16 × 20個感測電極,與文獻的靜電容感測床墊 (static charge sensitive bed, SCSB) 及非侵入式呼吸監視系統相較,本文FPCSM床墊有更多的電極可供臥姿辨識用;與商業化的體壓系統 (body pressure system, BPS) 相較,本文系統有低成本、不易老化、接近感測等諸多特性。藉由閘環(guard ring)概念的設計引入,用它來吸收雜訊與阻隔漏電流途徑,使投射電容電極更具接近感測特性與臥姿辨識。本文電容檢測結果與商業化精密電錶相較,高達0.997相關係數,確認電容數位轉換量測手法可行性。
    有別於呼吸量測常見的多重生理參數檢測儀(polysomnography, PSG) 與相關系統,本文的FPCSM藉由投射式電容感測特性,使得它不用穿戴在身上。FPCSM 由許多電極感測陣列組合而成,不僅提供了臥姿與行為觀測能力,更可用作為睡眠時呼吸偵測使用。在長時間監測時會有身體異動等特性,FPCSM 系統以感測陣列優勢搭配具較佳感測訊號電極的選用、異動來因應。其中具較佳感測訊號電極的評選,係採用極低運算量的反折計數演算邏輯當作評選量化指標。此演算邏輯可簡單的於時域上實現。經由實驗過程蒐集多組在不同臥姿狀況下的同步量測數據,進行FPCSM系統與商業化的PSG系統的比對,它們的相關係數高達0.88,確認FPCSM系統的可行性。

    The study uses two stages to develop projected capacitor for healthcare. The first stage builds up a flexible projected capacitive sensing mattress (FPCSM) that can classify the sleeping posture of an individual. The second stage is proposed using a FPCSM for personal health care and assessment. The capacitance-to-digital conversion (CDC) method was used to sensitively and accurately measure the capacitance of the projected electrodes. The required characteristics of the projected capacitor were identified to develop large-area applications for sensory mattresses. The area of the electrodes, the use of shielding, and the increased length of the transmission line were calibrated to more accurately measure the capacitance of the electrodes in large-size applications. To offer the users comfort in the prone position, a flexible substrate was selected and covered with 16 × 20 electrodes. Compared with the static charge sensitive bed (SCSB) and non-invasive respiratory monitoring system (NIRMS) of literature, our proposed system- FPCSM comes with more electrodes to increase the resolution of posture identification. As for the commercial product of body pressure system (BPS), the FPCSM has advantages such as lower cost, higher aging-resistance capability, and the ability to sense the capacitance of the covered regions without physical contact. The proposed guard ring design effectively absorbs the noise and interrupts leakage paths. The projected capacitive electrode is suitable for proximity-sensing applications and succeeds at quickly recognizing the sleeping pattern of the user. Compared with using a precision digital multi-meter, a correlation coefficient of up to 0.997 can demonstrate the feasibility of the CDC method.
    Unlike the interfaces of conventional measurement systems for polysomnography (PSG) and other alternative contemporary systems, the proposed FPCSM uses projected capacitive sensing capability that is not worn or attached to the body. The FPCSM is composed of a multi-electrode sensor array that can not only observe gestures and motion behaviors but also enables the FPCSM to function as a respiration monitor during sleep using the proposed approach. To improve long-term monitoring when body movement is possible, the FPCSM enables the selection of data from the sensing array, and the FPCSM methodology selects the electrodes with the optimal signals after the application of a channel reduction algorithm that counts the reversals in the capacitive sensing signals as a quality indicator. The simple algorithm is implemented in the time domain. The FPCSM system is used in experimental tests and is simultaneously compared with a commercial PSG system for verification. Multiple synchronous measurements are performed from different locations of body contact, and parallel data sets are collected. The experimental comparison yields a correlation coefficient of 0.88 between FPCSM and PSG. These results demonstrate the feasibility of the system design.

    摘要 I Abstract III Acknowledgement V Contents VI List of Figures VIII List of Tables X List of Acronyms XI Chapter 1 Introduction 1 1.1 Motivation 1 1.2 Review of Related Work 3 1.3 Dissertation Organization 7 Chapter 2 Method and Systems 8 2.1 Capacitive-sensing technology 8 2.2 Capacitive Properties 10 2.2.1 The validation of capacitance measurement method 10 2.2.2 Shielding against the differences of capacitance value 13 2.2.3 Transmission Line Length Influences the Primary Capacitance Values 14 2.2.4 The Gap of Guard Ring Influences Capacitance Primary Values 16 2.2.5 The Variation of Capacitance with Pressure 17 2.2 FPCSM build up 20 2.3.1 Sensory Electrodes 20 2.3.2 CDC Sensing Modules 21 2.3.3 Coordinator 22 2.4 The mechanism of sensing respiration 23 2.5 Pre-processing of sensing raw data 25 2.6 The signal quality indicator of time domain 26 2.7 Features of PSG 28 2.8 The experimental platform of respiration 30 Chapter 3 Experiments and Results 32 3.1 Primary capacitance values of FPCSM 32 3.2 Posture measurement 34 3.3 Insight the respiratory wave of PSG sensing 37 3.4 Insight the respiratory wave of FPCSM sensing 40 3.5 Experimental procedures and protocols 42 3.6 The activities of the body movements during experiment 42 3.7 Experiment of Respiration sensing 46 3.8 Experiment of Respiration dependent on patterns 48 Chapter 4 Discussion 50 4.1 The control variables of FPCSM 50 4.2 Interference in open environments 51 4.3 Effect of body movement 52 4.4 Verification with referred system 54 Chapter 5 Conclusion and Future Development 56 5.1 Conclusion 56 5.2 Future Work and Development 57 Reference 59 Curriculum Vitae 62 Publication List 63

    [1] Wells, M. E.; Vaughn, B. V. Sleep technologists educational needs assessment: a survey of polysomnography, electroneurodiagnostic technology, and respiratory therapy education program directors. J. Clin. Sleep Med. 2013, 9, 1081-1086.
    [2] Doufas, A. G.; Tian, L.; Padrez, K. A.; Suwanprathes, P.; Cardell, J. A.; Maecker, H. T.; Panousis, P. Experimental pain and opioid analgesia in volunteers at high risk for obstructive sleep apnea. PLoS One. 2013, 8, e54807.
    [3] Tan, H. L.; Gozal, D.; Ramirez, H. M.; Bandla, H. P.; Kheirandish-Gozal, L. Overnight polysomnography versus respiratory polygraphy in the diagnosis of pediatric obstructive sleep apnea. Sleep. 2014, 37, 255-260.
    [4] Wartzek, T.; Weyer, S.; Leonhardt, S. A differential capacitive electrical field sensor array for contactless measurement of respiratory rate. Physiol. Meas. 2011, 32, 1575-1590.
    [5] Ahmadi, N.; Shapiro, G. K.; Chung, S. A.; Shapiro, C. M. Clinical diagnosis of sleep apnea based on single night of polysomnography vs. two nights of polysomnography. Sleep Breath. 2009, 13, 221-226.
    [6] Deutsch, P. A.; Simmons, M. S.; Wallace, J. M. Cost-effectiveness of split-night polysomnography and home studies in the evaluation of obstructive sleep apnea syndrome. J. Clin. Sleep Med. 2006, 2, 145-153.
    [7] Sanders, M. H.; Black, J.; Costantino, J. P.; Kern, N.; Studnicki, K.; Coates, J. Diagnosis of sleep-disordered breathing by half-night polysomnography. Am. Rev. Respir. Dis. 1991, 144, 1256-1261.
    [8] Liu, G. Z.; Guo, Y. W.; Zhu, Q. S.; Huang, B. Y.; Wang, L. Estimation of respiration rate from three-dimensional acceleration data based on body sensor network. Telemed. J. E. Health. 2011, 17, 705-711.
    [9] Al-Khalidi, F. Q.; Saatchi, R.; Burke, D.; Elphick, H.; Tan, S. Respiration rate monitoring methods: a review. Pediatr. Pulmonol. 2011, 46, 523-529.
    [10] Albright, R. K.; Goska, B. J.; Hagen, T. M.; Chi, M. Y.; Cauwenberghs, G.; Chiang, P. Y. OLAM: A wearable, non-contact sensor for continuous heart-rate and activity monitoring. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2011, doi:10.1109/IEMBS.2011.6091361.
    [11] Lorussi, F.; Galatolo, S.; De Rossi, D. E. Textile-Based Electrogoniometers for Wearable Posture and Gesture Capture Systems. IEEE Sensors Journal. 2009, 9, 1014-1024.
    [12] Pacelli, M.; Caldani, L.; Paradiso, R. Textile piezoresistive sensors for biomechanical variables monitoring. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2006, doi:10.1109/IEMBS.2006.259287.
    [13] Farre, R.; Montserrat, J. M.; Navajas, D. Noninvasive monitoring of respiratory mechanics during sleep. Eur. Respir. J. 2004, 24, 1052-1060.
    [14] Lowne, D.R.; Tarler, M. Designing a low-cost mattress sensor for automated body position classification. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2005, 6, 6437–6440.
    [15] Donati, M.; Cecchi, F.; Bonaccorso, F.; Branciforte, M.; Dario, P.; Vitiello, N. A modular sensorized mat for monitoring infant posture. Sensors 2014, 14, 510–531.
    [16] Krejcar, O.; Jirka, J.; Janckulik, D. Use of mobile phones as intelligent sensors for sound input analysis and sleep state detection. Sensors 2011, 11, 6037–6055.
    [17] Bruyneel, M.; Libert, W.; Ninane, V. Detection of bed-exit events using a new wireless bed monitoring assistance. Int. J. Med. Inform. 2011, 80, 127–132.
    [18] Yang, C.C.; Hsu, Y.L. A review of accelerometry-based wearable motion detectors for physical activity monitoring. Sensors 2010, 10, 7772–7788.
    [19] Watanabe, K.; Watanabe, T.; Watanabe, H.; Ando, H.; Ishikawa, T.; Kobayashi, K. Noninvasive measurement of heartbeat, respiration, snoring and body movements of a subject in bed via a pneumatic method. IEEE Trans. Biomed. Eng. 2005, 52, 2100–2107.
    [20] Hernandez, L.; Waag, B.; Hsiao, H.; Neelon, V. A new non-invasive approach for monitoring respiratory movements of sleeping subjects. Physiol. Measur. 1995, 16, 161–167.
    [21] Krejcar, O.; Jirka, J.; Janckulik, D. Use of mobile phones as intelligent sensors for sound input analysis and sleep state detection. Sensors 2011, 11, 6037–6055.
    [22] Hsia, C.C.; Liou, K.J.; Aung, A.P.; Foo, V.; Huang, W.; Biswas, J. Analysis and comparison of sleeping posture classification methods using pressure sensitive bed system. Conf. Proc. IEEE Eng. Med. Biol. Soc. 2009, doi:10.1109/IEMBS.2009.5334694.
    [23] Lee, H.J.; Hwang, S.H.; Lee, S.M.; Lim, Y.G.; Park, K.S. Estimation of body postures on bed using unconstrained ECG measurements. IEEE J. Biomed. Health Inform. 2013, 17, 985–993.
    [24] Varady, P.; Micsik, T.; Benedek, S.; Benyo, Z. A novel method for the detection of apnea and hypopnea events in respiration signals. IEEE Trans. Biomed. Eng. 2002, 49, 936–942.
    [25] Tenhunen, M.; Elomaa, E.; Sistonen, H.; Rauhala, E.; Himanen, S.L. Emfit movement sensor in evaluating nocturnal breathing. Respir. Physiol. Neurobiol. 2013, 187, 183–189.
    [26] Scully, C. G.; Lee, J.; Meyer, J.; Gorbach, A. M.; Granquist-Fraser, D.; Mendelson, Y.; Chon, K. H. Physiological Parameter Monitoring from Optical Recordings With a Mobile Phone. IEEE Trans. Biomed. Eng. 2012, 59, 303-306.
    [27] Nakajima, K.; Matsumoto, Y.; Tamura, T. Development of real-time image sequence analysis for evaluating posture change and respiratory rate of a subject in bed. Physiol. Meas. 2001, 22, 21-28.
    [28] Dziuda, L.; Skibniewski, F. W.; Krej, M.; Lewandowski, J. Monitoring Respiration and Cardiac Activity Using Fiber Bragg Grating-Based Sensor. IEEE Trans. Biomed. Eng. 2012, 59, 1934-1942.
    [29] Hirooki, A.; Yasuhiro, T.; Kazuhiro, M.; Masato, N. Development of Non-restrictive Sensing System for Sleeping Person Using Fiber Grating Vision Sensor. Conf Proc ISMHS. 2001, doi: 10.1109/MHS.2001.965238.
    [30] Vinci, G.; Lindner, S.; Barbon, F.; Mann, S.; Hofmann, M.; Duda, A.; Weigel, R.; Koelpin, A. Six-Port Radar Sensor for Remote Respiration Rate and Heartbeat Vital-Sign Monitoring. IEEE Trans. Microw. Theor. Tech. 2013, 61, 2093-2100.
    [31] Xiao, Y. M.; Li, C. Z.; Lin, J. S. A portable noncontact heartbeat and respiration monitoring system using 5-GHz radar. IEEE Sensor J. 2007, 7, 1042-1043.
    [32] Lindqvist, A.; Pihlajamäki, K.; Jalonen, J.; Laaksonen, V.; Alihanka, J. Static-charge-sensitive bed ballistocardiography in cardiovascular monitoring. Clin. Physiol. 1996, 16, 23–30.
    [33] Tekscan. Body Pressure Measurement System. Available online: http://www.tekscan.com/body-pressure-measurement.
    [34] Carlson, B. W.; Neelon, V. J.; Hsiao, H. Evaluation of a non-invasive respiratory monitoring system for sleeping subjects. Physiol. Meas. 1999, 20, 53-63.
    [35] Chen, W.; Zhu, X.; Nemoto, T.; Kanemitsu, Y.; Kitamura, K.; Yamakoshi, K. Unconstrained detection of respiration rhythm and pulse rate with one under-pillow sensor during sleep. Med. Biol. Eng. Comput. 2005, 43, 306-312.
    [36] Baxter, L.K. Capacitive Sensors Design and Applications; Wiley Press: Berlin, Germany, 1997; pp. 7–43.
    [37] Diamond, J.M. A proposed capacitance bridge using a modern inductance standard. IEEE Trans. Instrum. Measur. 2006, 55, 1573–1575.
    [38] Texas Instruments. NE555 Precision Timers. Available online: http://www.ti.com/lit/ds/symlink/ne555.pdf.
    [39] Silicon Lab. Charge-Timing CDC Sensing Technology. Available online: http://www.silabs.com/products/mcu/capacitivesense/Pages/QuickSenseCharge-TimingCDC.aspx
    [40] Silicon Lab. Understanding Capacitive Sensing Signal to Noise Ratios. Available online: https://www.silabs.com/Support%20Documents/TechnicalDocs/AN367.pdf.
    [41] Zhang, Z. B.; Zheng, J. W.; Wu, H.; Wang, W. D.; Wang, B. Q.; Liu, H. Y. Development of a Respiratory Inductive Plethysmography Module Supporting Multiple Sensors for Wearable Systems. Sensors. 2012, 12, 13167-13184.

    無法下載圖示 校內:2020-02-13公開
    校外:不公開
    電子論文尚未授權公開,紙本請查館藏目錄
    QR CODE