مهندسی مکانیک مدرس

مهندسی مکانیک مدرس

طراحی و پیاده‌سازی رابط کنترلی برخط برای پروتز زانو بر پایه سیگنال‌های الکترومایوگرافی

نوع مقاله : پژوهشی اصیل

نویسندگان
دانشکده مهندسی مکانیک، پردیس دانشکده‌های فنی، دانشگاه تهران، تهران، ایران
چکیده
هدف این مقاله، طراحی یک رابط کنترلی برخط برای پروتز زانو بر پایه سیگنال‌های الکترومایوگرافی ماهیچه‌های فعال بخش ران است. با توجه به ماهیت متغیر با زمان سیگنال‌های الکترومایوگرافی، ترجمه این سیگنال‌ها به دستورات مناسب جهت پیاده‌سازی عملی با چالش‌هایی همراه است. نخستین گام طراحی رابط مورد نظر، طراحی و ساخت یک بستر آزمون مناسب برای پیاده‌سازی عملی رابط طراحی‌شده است. بدین منظور پروتز زانویی با استفاده از مکانیزم عملگر سری الاستیک طراحی و ساخته و با مطالعه مبانی آناتومی ماهیچه‌ای و شناسایی ماهیچه‌های فعال در چرخه راه‌رفتن، نمونه‌هایی از سیگنال‌های الکترومایوگرافی اندازه گرفته شده است. سپس، به‌منظور هموارسازی سیگنال‌ها، کاهش سطح نویز و کاهش ابعاد سیگنال‌ها از روش فیلترکردن و تقسیم‌کردن با پنجره‌های زمانی مجاور استفاده شده و شبکه عصبی با تاخیر زمانی به‌منظور نگاشت بین ویژگی‌های منتخب استخراج‌شده از سیگنال‌های الکترومایوگرافی و متغیرهای سینماتیکی مفصل زانو به‌کار رفته است. شبکه عصبی زاویه و سرعت زاویه‌ای مفصل زانو در حالت بدون وزن و در حالت راه‌رفتن به‌صورت خارج از خط با دقت ۰.۸۵ (R۲) تخمین زده می‌شود. جهت پیاده‌سازی تخمین برخط موقعیت زاویه‌ای مفصل زانو، آزمون‌هایی انجام شد که نتایج حاصل بیانگر استفاده از ویژگی‌های حوزه زمان و شبکه عصبی با تعداد ۵۰ نورون در لایه مخفی و میزان تاخیر ۲ ثانیه است. پس از طراحی رابط کنترل برخط برای تخمین موقعیت زاویه‌ای مفصل زانو، روی بستر آزمون ساخته‌شده پیاده‌سازی صورت گرفته که نتایج نشان‌دهنده ردیابی مناسب ساختار کنترلی برخط طراحی شده است.
کلیدواژه‌ها

موضوعات


عنوان مقاله English

Designing and Implementation of an Online Control Interface for Knee Prosthesis Based on Electromyography Signals

نویسندگان English

Sh. Jannati
S.M. Ayati
A. Yousefikoma
School of Mechanical Engineering, College of Engineering, University of Tehran, Tehran, Iran
چکیده English

The goal of this paper is to design an online control interface for knee prosthesis based on the electromyography (EMG) signals of active thigh muscles. According to the time dependent nature of electromyography signals, translating such signals into precise commands in practical applications is a challenge for scientists. First stage for designing an online control interface is to design and implement a test setup for examining the proposed online control interface. To serve this purpose, active knee prosthesis is designed and manufactured using an elastic actuator mechanism. In order to measure the EMG signals, active muscles were detected based on the fundamental of muscles anatomy. In the second stage, filtering and data segmentation were utilized for electromyography signals smoothing, decreasing noises and reducing signal dimensions. Furthermore, time-delay neural network was used in order to map time domain features of EMG signals onto kinematic variables of knee joint. The angle and angular velocity of knee joint were estimated with accuracy of 0.85 (R2) for two locomotion modes including non-weight bearing and ground level walking. To implement online estimation of angular position, time domain features and neural network with 50 hidden layer’s neurons and 2 seconds time delay were used. Finally, online angular position estimation of knee joint was implemented on the designed test setup and results confirm proper tracking of online control interface.

کلیدواژه‌ها English

Knee Prosthesis
Electromyography Signals
Online Control Interface
Time-Delayed Neural Network
Popovic D, Tomovic R, Tepavac D, Schwirtlich L. Control aspects of active above-knee prosthesis. International Journal of Man-Machine Studies. 1991;35(6):751-767. [Link] [DOI:10.1016/S0020-7373(05)80159-2]
Aeyels B, Peeraer L, Vander Sloten J, Van der Perre G. Development of an above-knee prosthesis equipped with a microcomputer-controlled knee joint: first test results. Journal of Biomedical Engineering. 1992;14(3):199-202. [Link] [DOI:10.1016/0141-5425(92)90052-M]
Liu M, Datseris P, Huang HH. A Prototype for smart prosthetic legs-analysis and mechanical design. Advanced Materials Research. 2011;403-408:1999-2006. [Link] [DOI:10.4028/www.scientific.net/AMR.403-408.1999]
Wu SK, Shen X. Lower-limb robotic devices: Controls and design. University of Alabama Libraries. 2012;3511205:113. [Link]
Wu SK, Waycaster G, Shen X. Electromyography-based control of active above-knee prostheses. Control Engineering Practice. 2011;19(8):875-882. [Link] [DOI:10.1016/j.conengprac.2011.04.017]
Waycaster G, Wu S, Shen X. Design and control of a pneumatic artificial muscle actuated above-knee prosthesis. Journal of Medical Devices. 2011;5(3):1-9. [Link] [DOI:10.1115/1.4004417]
Martinez-villalpando EC, Mooney L, Elliott G, Herr H. Antagonistic active knee prosthesis . A metabolic cost of walking comparison with a variable-damping prosthetic knee. 33rd Annual International Conference of the IEEE EMBS; 2011 Aug 30; Boston, Massachusetts USA: IEEE (Institute of Electrical and Electronics Engineers); 2011. pp. 8519-8522. [Link] [DOI:10.1109/IEMBS.2011.6092102]
Borjian R, Lim JJ, Khamesee MB, Melek WW. The design of an intelligent mechanical active prosthetic knee. 34th Annual Conference of IEEE Industrial Electronics; 2008 Dec; Boston: IEEE (Institute of Electrical and Electronics Engineers); 2008. pp. 3016-3021 [Link] [DOI:10.1109/IECON.2008.4758441]
Brescianini D, Andrea RD. Design, modeling and control of an omni-directional aerial vehicle. International Conference on Robotics and Automation (ICRA); 2016 May 16-21; Stockholm, Sweden: IEEE (Institute of Electrical and Electronics Engineers); 2016. pp. 3261-3266. [Link] [DOI:10.1109/ICRA.2016.7487497]
Sup F, Bohara A, Goldfarb M. Design and control of a powered transfemoral prosthesis. The International Journal of Robotics Research. 2008;27(2):263-273. [Link] [DOI:10.1177/0278364907084588]
Goldfarb M, Varol HA, Goldfarb M. Volitional control of a prosthetic knee using surface electromyography. IEEE Transactions on Biomedical Engineering. 2011;58(1):144-151. [Link] [DOI:10.1109/TBME.2010.2070840]
Sup F, Varol HA, Goldfarb M. Upslope walking with a powered knee and ankle prosthesis: Initial results with an amputee subject. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2011;19(1):71-78. [Link] [DOI:10.1109/TNSRE.2010.2087360]
Kamnik R, Vitiello N, Lefeber D, Pasquini G, Munih M, Brussel VU. Online Phase Detection Using Wearable Sensors for Walking with a Robotic Prosthesis. 2014;2776-2794. [Link] [DOI:10.3390/s140202776]
Sun J, Voglewede PA. Powered transtibial prosthetic device control system design, implementation, and bench testing. Journal of Medical Devices. 2016; 8(1): 011004. [Link] [DOI:10.1115/1.4025851]
Young AJ, Smith LH, Rouse EJ, Hargrove LJ. A comparison of the real-time controllability of pattern recognition to conventional myoelectric control for discrete and simultaneous movements. Journal of NeuroEngineering and Rehabilitation. 2014;11:5. [Link] [DOI:10.1186/1743-0003-11-5]
Hargrove LJ, Huang H, Schultz AE, Lock BA, Lipschutz R, Kuiken TA. Toward the development of a neural interface for lower limb prosthesis control. Annual International Conference of the IEEE Engineering in Medicine and Biology Society; 2009 Sep 3-6; Minneapolis, MN: IEEE (Institute of Electrical and Electronics Engineers); 2011. [Link] [DOI:10.1109/IEMBS.2009.5334303]
Spanias JA, Perreault EJ, Hargrove LJ. Detection of and compensation for EMG disturbances for powered lower limb prosthesis control. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2015; 24(2): 226-234. [Link] [DOI:10.1109/TNSRE.2015.2413393]
Young AJ, Hargrove AJ. A classification method for user-independent intent recognition for transfemoral amputees using powered lower limb prostheses. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2015; 24(2): 217-225. [Link] [DOI:10.1109/TNSRE.2015.2412461]
Huang H, Kuiken TA, Lipschutz RD. A strategy for identifying locomotion modes using surface electromyography. IEEE Transactions on Biomedical Engineering. 2009;56(1):65-73. [Link] [DOI:10.1109/TBME.2008.2003293]
Peeraer L, Aeyels B, Van der Perre G. Development of EMG-based mode and intent recognition algorithms for a computer-controlled above-knee prosthesis. Journal of Biomedical Engineering. 1990;12(3):178-182. [Link] [DOI:10.1016/0141-5425(90)90037-N]
Jin D, Zhang R, Zhang J, Wang R, Gruver WA. An intelligent above-knee prosthesis with EMG-based terrain identification. Smc 2000 conference proceedings. 2000 ieee international conference on systems, man and cybernetics. 'cybernetics evolving to systems, humans, organizations, and their complex interactions'; 2000 Oct 8-11; Nashville, TN: IEEE (Institute of Electrical and Electronics Engineers); 2000. []
Meng M, Luo Z, She Q, Ma Y. Automatic recognition of gait mode from emg signals of lower limb. The 2nd International Conference on Industrial Mechatronics and Automation; 2010 May 30-31; Wuhan, China: IEEE (Institute of Electrical and Electronics Engineers); 2010. [Link]
Ceseracciu E, Reggiani M, Sawacha Z, Sartori M, Spolaor F, Cobelli C, et al. SVM classification of locomotion modes using surface electromyography for applications in rehabilitation robotics. 19th International Symposium in Robot and Human Interactive Communication; 2010 Sep 13-15; Viareggio, Italy: IEEE (Institute of Electrical and Electronics Engineers); 2010. [Link] [DOI:10.1109/ROMAN.2010.5598664]
Varol HA, Sup F, Goldfarb M. Multiclass real-time intent recognition of a powered lower limb prosthesis. IEEE Transactions on Biomedical Engineering. 2010;57(3):542-551. [Link] [DOI:10.1109/TBME.2009.2034734]
Young AJ, Kuiken TA, Hargrove LJ. Analysis of using EMG and mechanical sensors to enhance intent recognition in powered lower limb prostheses. Journal of Neural Engineering. 2014;11(5):056021. [Link] [DOI:10.1088/1741-2560/11/5/056021]
Winter DA. Biomechanics and motor control of human Movement. 4th edition. New Jersey: John Wiley & Sons; 2009. [Link] [DOI:10.1002/9780470549148]
Robinson DW. Design and analysis of series elasticity in closed-loop actuator force control [Dissertation]. Massachusetts: Massachusetts Institute of Technology; 2000. [Link]
Hermens HJ, Freriks B, Disselhorst-Klug C, Rau G. Development of recommendations for SEMG sensors and sensor placement procedures. Journal of Electromyogrphy and Kinesiology. 2000;10(5):361-374. [Link] [DOI:10.1016/S1050-6411(00)00027-4]
Hakonen M, Piitulainen H, Visala A. Current state of digital signal processing in myoelectric interfaces and related applications. Biomedical Signal Processing and Control. 2015;18:334-359. [Link] [DOI:10.1016/j.bspc.2015.02.009]