連續被動運轉設備及主動式可控運轉設備兩者均為物裡治療設備，一般常使用來促進關節潤滑液之產生，可以減少腫漲造成的痛苦並促進關節附近血液之循環以達到復健之效果。連續被動運動設備係使用馬達帶動，來幫助無法作關節活動的患者，作適度範圍內之關節活動，傳統的主動式可控運轉設備係使用一般可調式彈簧及連桿作阻力。本文實現一組複合式復健設備，保留原先被動運轉設備之優點再加上主動式運轉設備之長處使兩者結合。系統使用原先的驅動馬達，經設計之控制回路參數的演算，轉換為具有類似彈性阻力的主動式運轉設備。系統在踏板上安裝力量感測器並且利用復健設備機構轉動關節之電位計來作為位置回授感測器，借此偵測病人之施力及移動位置的即時情況，作為控制系統的輸入運算參數，使得設備產生一個模擬彈性系統的阻力來反應病人所加的外力。系統使用Artromot復健設備，經改良用來實現此一概念及實驗原型，搭配標準型電腦來完成循環糢糊類神經網路控制，控制參數中之初始參數設定如彈性系數及阻尼因數可作為系統參數，用來設定驅動控制馬達移動難易程度，配合即時之回饋資料，隨著控制系統的參數設定可改變阻力大小，經測試顯示能平穩且自然的呈現出模擬彈性系統運轉。
實驗結果展示使用者施加力量的大小、移動位置及馬達驅動力量大小的圖形，使用者整個練習活動的資料均可被記錄，如接上網路遠距離監看亦能作為遠距醫療之使用。配合外加生物訊號偵測器，諸如肌電信號、心跳、人體溫度等之訊息資料亦可作進一步的治療及研究用。本文即攫取肌電訊號，對肌肉張力之電位訊號作小波系數的分解，並以此系數大小之量化，用來作為肌肉疲勞評估之參考，此一測試可作為進一步個案研究用及病患治療情形中肌電訊號大量改變時之警示，得以避免危險情況發生及造成非預期之傷害。因此主動式復健設備對於膝關節的活動範圍，能即時的顯示阻力及出力強弱情況，可針對個別病患之療程需要，作適當的參數設定及修改，此一設計概念對病患之診斷、治療、研究能夠推廣到膝關節以外其它關節來使用。

Continuous passive motion (CPM) and controllable active motion (CAM) physiotherapy devices are commonly used to promote rehabilitation of damaged synovial joints. CPM devices use an electric motor to move the joint through a range of motion (ROM) without muscle activity of the patient. Traditional CAM devices use some form of spring or frictional resistance to oppose the patient’s own muscle activity. This dissertation presents a combined CPM/CAM device with no resistive CAM-type components. The system’s electric motor functions conventionally in the CPM mode. However, we embed the CPM’s pulse width modulation (PWM) electric motor in a feedback loop with a force sensor which can monitor the patient’s applied force and an angle sensor which can monitor the position of the knee. Then, in the CAM mode, the motor is driven to exert a force opposing the muscle activity of the patient, thus simulating the effects of a traditional spring loaded CAM device. A proof-of-concept prototype is implemented by modifying a commercial Artromot knee-therapy CPM device. In our laboratory model, a standard personal computer (PC) is used to host a recurrent fuzzy neural network (RFNN) control algorithm which uses the initial preset parameters (spring constant, spring damping factor) together with the momentary feedback data and the system model to produce a control signal which drives the PWM motor control circuit.
Operation of the simulated spring-load is smooth and natural. The PC provides control interface and graphic display of the patient’s momentary muscular force, knee position and motor force. Data can be captured over the time course of the patient’s exercise cycle. The PC provides full recording capability, LAN/Internet capabilities for remote monitoring and telemedicine, and extensive further analytic abilities. The PC is capable of accepting additional biometric input data (e.g. heart rate, body temperature) for advanced research and therapy. As an example, we implement an additional sensor, namely a surface electromyography (SEMG), which senses muscle tension and can be used to evaluate fatigue of the measured muscles. This can be a valuable research tool and also can be used to alert the patient or therapist that a dangerous fatigue level has been reached. Experimental results demonstrate the basic features of the system. Discussion includes possible development of physiotherapeutic ROM, momentary resistance (in CAM mode) and speed patterns (in CPM mode) that are customized for individual patients, being sensitive to the patient’s development, special needs and momentary status. The presented CPM/CAM design is of use for diagnosis, therapy, research, and is readily adaptable to joints other than the knee.

[1]M. Bowditch, “Anterior cruciate ligament rupture and management,” Trauma, vol. 3, no. 4, pp. 249-261, 2001
[2]C. Mikkelsen, S. Werner and E. Eriksson, “Close kinetic chain alone compared to combined open and closed kinetic chain exercises for quadriceps strengthening after anterior cruciate ligament reconstruction with respect to return to sports: a prospective matched follow-up study,” Knee Surgery, Sports Traumatology, Arthroscopy, vol. 8, no. 6, pp. 337-342, 2000.
[3]K. D. Shelbourne and M. D. Dersam, The evaluation of rehabilitation for ACL reconstruction, In R. J. Williams, and D. P. Johnson, Controversies in knee surgery. Oxford: Oxford University Press, pp. 151-166, 2004
[4]B. S. Delay, R. J. Smolinski, W. M. Wind and D. S. Bowman, “Current practices and opinions in ACL reconstruction and rehabilitation: results of a survey of the American orthopaedic Society for Sports Medicine,” Am J Knee Surg., vol. 14, no. 2, pp. 85-91, 2001.
[5]J. A. Feller, R. Cooper and K. E. Webster, “Current Australian trends in rehabilitation following anterior cruciate ligament reconstruction,” The Knee, vol. 9, no. 2, pp. 121-126, 2002.
[6]K. D. Shelbourne and M. D. Dersam, The evaluation of rehabilitation for ACL reconstruction. In R. J. Williams, and D. P. Johnson (Eds.), Controversies in knee surgery. Oxford: Oxford University Press, pp. 151–166, 2004.
[7]K. D. Shelbourne, J. H. Wilckens, A. Mollabashy and M. De Carlo, “Arthrofibrosis in acute anterior cruciate ligament reconstruction: The effect of timing of reconstruction and rehabilitation,” American Journal of Sports Medicine, vol. 19, no. 4, pp. 332-336, 1991.
[8]R. C. Schenck, M. J. Blaschak, E. D. Lance, T. C. Turturro and M. S. Holmes, “A Prospective Outcome Study of Rehabilitation Programs and Anterior Cruciate Ligament Reconstruction,” The Journal of Arthroscopic and Related Surgery, vol. 13, no. 3, pp. 285-290, 1997.
[9]B. D. Beynnon, R. J. Johnson, J. A. Abate, B. C. Fleming and C. E. Nichols, “Treatment of Anterior Cruciate Ligament Injuries, Part 2,” American Journal of Sports Medicine, vol. 33, no. 11, pp. 1751-1767, 2005.
[10]R. W. Fremerey, P. Lobenhoffer, J. Zeichen, M. Skutek and U. Bosch, “ Propriception after rehabilitation and reconstruction in knees with deficiency of the anterior cruciate ligament,” J Bone Joint Surg Br, vol. 82, no. 6, pp. 801-806, 2000.
[11]J. E. Rivera, “Open Versus Closed Kinetic Chain Rehabilitation of the Lower Extremity: A Functional and Biomechanical Analysis,” J Sport Rehabil, vol. 3, no. 2, pp. 154-167, 1994.
[12]S. W. O’Driscoll and N. J. Giori, “Continuous passive motion (CPM): Theory and principles of clinical application,” J. Rehabil. Res. Dev., vol. 37, no. 2, pp. 179-188, 2000.
[13]L. A. Bennett, S. C. Brearley, J. A. Hart and M. J. Bailey, “A Comparison of 2 Continuous Passive Motion Protocols After Total Knee Arthroplasty: A Controlled and Randomized Study,” J. Arthroplasty., vol. 20, no. 2, pp. 225-233, 2005.
[14]D. M. Davies, D. W. Johnston, L. A. Beaupre and D. A. Lier, “Effect of adjunctive range-of-motion therapy after total knee arthroplasty on the use of health services after hospital discharge,” Can. J. Surg., vol. 46, no. 1, pp. 30-36, 2003.
[15]Th. Mittlmeier, A. Weiler, T. Sohn, L. K. Hans, S. Mollbach, G. Duda and N. P. Sudkamp, “Function monitoring during rehabilitation following anterior cruciate ligament reconstruction,” Clin. Biomech., vol. 14, no. 8, pp. 576-584, 1999.
[16]R. Riener, M. Frey, T. Proll, F. Regenfelder and R. Burgkar, “Phantom-based multimodal interactions for medical education and training: the Munich knee joint simulator,” IEEE Trans. Inf. Technol. Biomed., vol. 8, no. 2, pp. 208-216, 2004.
[17]R. K. Eastlack, A. R. Hargens, E. R. Groppo, G. C. Steinbach, K. K. White and R. A. Pedowitz, “Lower Body Positive-pressure Exercise after Knee Surgery,” Clin. Orthop. Rel. Res., vol. 431, pp. 213-219, 2005.
[18]D. E. Toutoungi, T.W. Lu, A. Leardini, F. Catani and J. J. O’Connor, “Cruciate ligament forces in the human knee during rehabilitation exercises,” Clin. Biomech., vol. 15, no. 3, pp. 176-187, 2000.
[19]G. M. Ginty, J. J. Jrrgang and D. Pezzullo, “Biomechanical considerations for rehabilitation of the knee,” Clin. Biomech. vol. 15, pp. 160-166, 2000.
[20]T. Yanagawa, K. Shelburne, F. Serpas and M. Pandy, “Effect of hamstrings muscle action on stability of the ACL-deficient knee in isokinetic extension exercise,” Clin. Biomech. vol. 17, no. 9, pp. 705-712, 2002.
[21]S. Zaffagnini, S. Martelli and F. Acquaroli, “Computer investigation of ACL orientation during passive range of motion,” Comput. Biol. Med., vol. 34, no. 2, pp. 153-163, 2004.
[22]J. C. Upper, B. L. Ramage, D. T. Corr, D. A. Hart and J. L. Ronsky, “Measure knee joint laxity: A review of applicable models and the need for new approaches to minimize variability,” Clin. Biomech., vol. 22, no. 1, pp. 1-13, 2007.
[23]H. Kucuk, “The effect of modeling cartilage on predicted ligament and contact forces at the knee,” Comput. Biol. Med., vol. 36, no. 4, pp. 363-375, 2006.
[24]R. R. Neptune and S. A. Kautz, “Knee joint loading in forward versus backward pedaling: implications for rehabilitation strategies,” Clin. Biomech., vol. 15, no. 7, pp. 528-535, 2000.
[25]B. C. Fleming, P. A. Renstrom, B. D. Beynnon, B. Engstrom, G. D. Peura, G. J. Badger and R. J. Johnson, “The effect of weightbearing and external loading on anterior cruciate ligament strain,” J. Biomech., vol. 34, no. 2, pp. 163-170, 2001.
[26]F. V. Lubken, R. Schmidt, C. Jouini, H. Gerngross and B. Friemert, “The effect of a controlled active motion device on proprioception after anterior cruciate ligament plasty,” Unfallchirurg, vol. 109, pp. 22-29, 2006.
[27]F. Da, “Decentralized Sliding Mode Adaptive Controller Design Based on Fuzzy Neral Networks for Interconnected Uncertain Nonlinear Systems,” IEEE Trans. Neural Networks, vol. 11, no. 6, pp. 1471-1480, 2000.
[28]C. M. Lin and C. F. Hsu, “Adaptive Fuzzy Sliding-Mode Control for Induction Servomotor Systems,” IEEE Trans. Energy Convers., vol. 19, no. 2, pp. 362-368, 2004.
[29]N. Hogan, “Impedance control: An approach to manipulation part I: Theory; part II: Implementation; part III: Application,” J. Dyn. Control Syst., vol. 107, pp. 1-23, 1985.
[30]R. Kamnik, R. Matko and T. Bajd, “Application of Model Reference Adaptive Control to Industrial Robot Impedance Control,” J. Intell. Robot. Syst., vol. 22, pp. 153-163, 1998.
[31]C. Y. Lee, P. C. Tung and W. H. Chu, “Adaptive fuzzy sliding mode control for an automatic arc welding system,” Int. J. Adv. Manuf. Technol., vol. 29, pp. 481-489, 2006.
[32]F. J. Lin and R. J. Wai, “Adaptive and fuzzy neural network sliding-mode controllers for motor-quick-return servomechanism,” Mechatronics, vol. 13, pp. 477-506, 2003.
[33]H. J. Ho and T. C. Chen, “Implementation of CAM/CPM physiotherapy device with a virtual spring,” Comput. Biol. Med., vol. 38, pp. 923-930, 2008.
[34]D. E. Rivera, M. Morari and S. Skogestad, “Internal Model Control: PID control design,” Ind. Eng. Chem. Press Des. Dev., vol. 25, no. 1, pp. 252-265, 1986.
[35]T. C. Chen and T. T. Sheu, “Model Reference Robust Speed Control for Induction-Motor Drive with Time Delay Based on Neural Network,” IEEE Trans. Syst. Man Cybern. Part A-Syst. Hum., vol. 31, pp. 746-752, 2001.
[36]S. Jung and T. C. Hsia, “Neural Network Impedance Force Control of Robot Manipulator,” IEEE Trans. Ind. Electron., vol. 45, pp. 451-461, 1998.
[37]T. J. Ren and T. C. Chen, “Robust speed-controlled induction motor drive based on recurrent neural network,” Electr. Power Syst. Res., vol. 76, pp. 1064-1074, 2006.
[38]C. H. Wang, H. L. Liu and T. C. Lin, “Direct Adaptive Fuzzy-Neural Control With State Observer and Supervisory Controller for Unknown Nonlinear Dynamical Systems,” IEEE Trans. Fuzzy Syst., vol. 10, pp. 39-49, 2002.
[39]C. J. De Luca, “The Use of Surface Electromyography in Biomechanics,” J Appl Biomech, vol. 13, pp. 135-163, 1997.
[40]D. R. Ankrum, “Question to ask when interpreting surface electromyography (SEMG) research,” Proceedings of the IEA 2000/HFES congress, vol. 5, pp. 530-533, 2000.
[41]G. Amara, “An Introduction to Wavelet,” IEEE Comput. Sci. Eng., vol. 2, No. 2, pp. 1-18, 1995.
[42]C. K. Chui, Wavelets: a tutorial in theory and application. Academic Press Professional, Inc. San Diego, CA, USA, 1992.
[43]H. Dickhaus and H. Heinrich, “Identification of High Risk Patients in Cardiology by Wavelet Networks,” 18th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 3, pp. 923–924, 1996.
[44]C. S. Pattichis, M. S. Pattichis and C. N. Schizas, “Wavelet Analysis of Motor Unit Action Potentials,” 18th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 4, pp. 1493-1495, 1996.
[45]T. C. Chen and T. T. Sheu, “Model Reference Robust Speed Controlled for Induction-Motor Drive With Time Delay Based on Neural Network,” IEEE Trans. Syst. Man Cybern. Part A Syst. Hum., vol. 31, no. 6, pp. 746-753, 2000.
[46]G. N. Reising and M. H. Daniels, “A Study of Hogan’s Model of Counselor Development and Supervision,” Journal of Counseling psychology, vol. 30, No. 2, pp. 235-244, 1983.
[47]N. Hogan, “Impedance Control: An Approach to Manipulation,” Proceedings of the American Control Conference, vol. 1, pp. 304-313, 1984.
[48]R. Kamnik, R. Matko and T. Bajd, “Application of Model Reference Adaptive Control to Industrial Robot Impedance Control,” J. Intell. Robot. Syst., vol. 22, pp. 153-163, 1998.
[49]S. Jung and T. C. Hsia, “Neural Network Impedance Force Control of Robot Manipulator,” IEEE Trans. Ind. Electron., vol. 45, pp. 451-462, 1998.
[50]S. Jung, T. C. Hsia and R. G. Bonitz, “Force tracking impedance control of robot manipulators under unknown environment,” IEEE Trans. Control Syst. Technol., vol. 12, no. 3, pp. 474-483, 2004.
[51]L. Tian and L. G. Gilbertson, “The study of control methods for the robotic testing system for human musculoskeletal joints,” Comput. Methods Programs Biomed., vol. 74, no. 3, pp. 211-220, 2004.
[52]Y. P. Meau, F. L. Ibrabim, S. A. Narainasamy and R. Omar, “Intelligent classification of electrocardiogram (ECG) signal using extended Kalman Filter (EKF) based neuro fuzzy system,” Comput. Methods Programs Biomed., vol. 82, no. 2. pp. 157-168, 2006.
[53]M. Zerikat, M. bendjebbar and N. Benouzza, “Dynamic Fuzzy-neural Network Controller for induction Motor Drive,” Word Academy of Science, Engineering and Technology, vol. 10, pp. 278-283, 2005.
[54]L. Jin, P. N. Nikiforuk and M. Gupta, “Approximation of Discrete-Time State-Space Trajectories Using Dynamic Recurrent Neural Networks,” IEEE Trans. Automat. Contr., vol. 40, no. 7. pp. 1266-1270, 1995.
[55]C. C. Ku and K. Y. Lee, “Diagonal recurrent neural networks for dynamic systems control,” IEEE Trans. Neural Networks, vol. 6, no. 1. pp. 144-156, 1995.
[56]R. Merletti, A. Rainoldi and D. Farina, Myoelectric manifestations of muscle fatigue. In: R. Merletti, P. Parker,(Eds.), Electromyography-Physiology, Engineering, and Noninvasive Applications, first ed. John Wiley and Sons, Inc., Hoboken, New Jersey, pp. 233-258, 2004.
[57]R. H. T. Edwards, Human muscle function and fatigue. In: Human Muscle Fatigue: Physiological Mechanisms. edited by R. Porter, J. Whelan, London: Pitman Medical, pp. 1-18, 1981.
[58]S. Heimer, Fatigue. In: R. Medved, (Ed.), Sports Medicine, second ed. pp. 147-151 (in Croatian), 1987.
[59]N. K. Vollestad, “Measurement of human muscle fatigue,” J. Neurosci. Meth., vol. 74, no. 2, pp. 219-227, 1997.
[60]C. J. De Luca, “Myoelectrical manifestations of localized muscular fatigue in humans,” Crit. Rev. Biomed. Eng., vol. 11, no. 4, pp. 251-279, 1984.
[61]D. Farina, R. Merletti, B. Indino, M. Nazzaro and M. Pozzo, “Surface EMG crosstalk between knee extensor muscles: experimental and model results” Muscle Nerve, vol. 26, no. 5, pp. 681-695, 2002.
[62]G. C. Knowlton, R. L. Bennett and R. McClure, “Electromyography of fatigue,” Arch. Phys. Med. Rehabil., vol. 32, no. 10, pp. 648-652, 1951.
[63]K. Kogi and T. Hakamada, “Frequency analysis of the surface electromyogram in muscle fatigue,” J. Sci. Labour (Tokyo) ,vol. 38, pp. 519-528, 1962.
[64]C. J. De Luca, Spectral compression of the EMG signal as an index of muscle fatigue. In: Neuromuscular Fatigue, edited by A. J. Sargeant, D. Kernell, Royal Netherlands Academy of Arts and Sciences, Amsterdam, The Netherlands, pp. 44–51, 1992.
[65]E. Kwatny, D. H. Thomas and H. G. Kwatny, “An Application of Signal Processing Techniques to the Study of Myoelectric Signals,” IEEE Trans. Biomed. Eng., vol. 17, no. 4, pp. 303-313, 1970.
[66]J. W. Cooley and J. W. Tukey, “An Algorithm for Machine Calculation of Complex Fourier Series,” Mathematics of Computation, vol. 19, no. 90, pp. 297-301, 1965.
[67]V. Medved, “Computer algorithm for an EMG-based muscular fatigue assessment method. In: J. R. Wilson, E. N. Corlett, I. Manenica, New Methods in Applied Ergonomics,” The Proceedings of the Second International Occupational Ergonomics Symposium. Taylor and Francis, London, pp. 71-75, 1987.
[68]O. Paiss and G. F. Inbar, “Autoregressive modeling of surface EMG and its spectrum with application to fatigue,” IEEE Trans. Biomed. Eng., vol. 34, no. 10, pp. 761-770, 1987.
[69]R. Merletti and L. R. Lo Conte, “Advances in processing of surface myoelectric signals: part 1,” Med. Biol. Eng. Comput., vol. 33, no. 3, pp. 362-372, 1995.
[70]C. G. S. Kramer, T. Hagg and B. Kemp, “Real-time measurement of muscle fatigue related changes in surface EMG,” Med. Biol. Eng. Comput., vol. 25, no. 6, pp. 627-630, 1987.
[71]R. Seroussi, M. H. Krag, P. Wilder and M. H. Pope, “The design and use of a microcomputerized real-time muscle fatigue monitor based on the medial frequency-shift in the electromyographic signal,” IEEE Trans. Biomed. Eng., vol. 36, no. 2, pp. 284-286, 1989.
[72]A. J. Pratt, R. E. Gander and B. R. Brandell, “Real-Time Digital Median Frequency Estimator for Surface Myoelectric Signals,” IEEE Trans. Biomed. Eng., vol. 38. no. 3, pp. 306-309, 1991.
[73]E. A. Clancy and D. Farina, G. Filligoi, Single-channel techniques for information extraction from the surface EMG signal. In: R. Merletti, P. A. Parker,(Eds.), Electromyography-Physiology, Engineering, and Noninvasive Applications, first ed. John Wiley and Sons, Inc., Hoboken, New Jersey, pp. 133-168, 2004.
[74]G. M. Paoli, Estimating certainty in classification of motor unit action potentials. Master Thesis, University of Waterloo., Waterloo, Ontario, Canada, 1993.
[75]D. W. Stashuk, “Simulation of electromyographic signals,” J. Electromyogr. Kines., vol. 3, no. 3, pp. 157-173, 1993.
[76]J. C. King, D. Dumitru and S. Nandedkar, “Concentric and Single Fiber Electrode Spatial Recording Characteristics,” Muscle Nerve, vol. 20, pp. 1525-1533, 1997.
[77]S. D. Nandedkar, E. V. Stalberg and D. B. Sanders, “Simulation Techniques in Electromyography,” IEEE Trans. Biomed. Eng., vol. 32, no. 10, pp. 775-785. 1985.
[78]K. K. Dinesh, D. P. Nemuel and B. Alan, “Wavelet Analysis of Surface Electromyography to Determine Muscle Fatigue,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 11, no. 4, pp. 400-406. 2003.
[79]J. V. Basmajian and C. J. De Luca, Muscle Alive: Their Functions Revealed by Eletromyography. 5th ed. Baltimore MD: William and Wilkins, 1985.
[80]J. C. King, D. Dumitru and S. Nandedkar, “Concentric and single fiber electrode spatial recording characteristics,” Muscle and Nerve, vol. 20, no. 12, pp. 1525-1533, 1997.
[81]S. G. Mallat, “A Theory for Multiresolution Signal Decomposition: The Wavelet Representation,” IEEE Trans. Pattern Anal. Machine Intell., vol. 11, no. 7, pp. 674-693, 1989.
[82]D. K. Kumar, N. D. Pah and A. Bradley, “Wavelet Analysis of Surface Electromyography to Determine Muscle Fatigue,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 11, no. 4, pp. 400-406, 2003.