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EMG controlled Prosthetic Arm

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Prosthetic Arm for Amputees
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EMG controlled Prosthetic Arm

  1. 1. Paper ID 39 Design and Implementation of an EMG Controlled 3D Printed Prosthetic Arm Authored by Nazmus Sakib and Md. Kafiul Islam Presented by Md. Kafiul Islam, PhD, SMIEEE Asst. Prof., Dept. of EEE, IUB kafiul_islam@iub.edu.bd 1
  2. 2. Presentation Outline  Introduction Motivation and Objectives Electromyography (EMG) Basics State-of-the-Art Prosthetic Systems  Proposed System Design & Implementation  Analog Front-End (EMG Recorder) Design & PCB Implementation  Signal Analysis in MATLAB  Prosthetic Arm Design & 3D Printing  Control Circuitry  Experiment and Testing  Performance Evaluation & Comparison  Conclusion & Future work 2
  3. 3. Motivation and Objectives 3 Motivation: • > 4000 people get injured in road accident every year and live without one or more body parts. • Rana plaza massacre and many other left many people cripple for life time. • Dependency on imported expensive prosthesis. Goal: Provide an affordable solution of prosthetic limb for our country Objectives: • Design an Analog Frontend circuit to record the electrical activity of muscle (EMG). • Design and 3D print a prosthetic arm • Detect muscle contraction to generate a command signal to control prosthetic arm. 0 2000 4000 2009 2010 2011 2012 2013 2014 2015 2016 (Up to July) ROAD ACCIDENT STATISTICS Accidents Desths Injury
  4. 4. Electromyography (EMG) 4 • Electromyography (EMG) detects electrical potential generated by muscle cells upon voluntary contraction of muscle. • Brain commands in spinal cord transmitted to muscle fiber by Motor neuron. • When motor unit is activated, muscle fibers contract. Motor Unit Action Potential (MUAP)
  5. 5. EMG Signal Characteristics 5 Typical MUAP Waveform Biopotential Frequency Range Signal Amplitude Electrode Electromyogram (EMG) 20 Hz – 1000 Hz Maximum Usable Energy: 50 Hz - 150 Hz 10 µV – 2mV Surface
  6. 6. 6 Currently Available Prosthetic Systems* Touch Bioinics Open Hand Project Bebionics Price range: USD 10K Products: • i-limb revolution • i-limb ultra • i-limb digits • livingskin • Price range: USD 1K • They are using 3D printer technology • Price range: Between USD 11- 60K. • Most advanced prosthetic technology • Human like hand movement system. Price range: USD 6K Products: • Brunel Hand 2.0 • Hero Arm Open Bionics *Ref: [2]-[5]
  7. 7. 7 Proposed System
  8. 8. Electrodes & Connector  Using surface EMG (SEMG), made of Ag/AgCl.  Permits electron conduction from the skin to the wire.  The connectors of these electrodes have three conductor sensor cable with electrode pad leads.  Using electrolytic gel between skin and electrode can reduce electrode impedance.  Multi-useable electrodes, positive, negative and ground, to the skin/surface covering the muscle, in order to detect muscle movement. 8 Connectors & Electrodes used in this Project Electrodes
  9. 9. Dual DC Voltage Supply9 • To provide constant voltage and protect circuit IC components. • 5V Boost converters are used to provide +5V and -5V output. • 3.7V Lipo or Li-ion both type of batteries can be used.
  10. 10. Instrumentation Amplifier 10 • AD620: Low cost, high accuracy with high input impedance, low DC offset, low noise and very high open-loop gain. • High CMRR: >100dB • Amplitude of input: 0.01mV to 1 mV. • Gain = 1+ 49.4𝑘𝞨 10 ≈ 5000 (for Full swing of the ADC) • Takes the difference between two electrodes and amplify it.
  11. 11. Active High-Pass Filter 11 • To remove low frequency noise interference and DC offset. • Cutoff Frequency: 1 2π R1R2C1C2 = 3 Hz • Order of filter: 2nd order • Gain: Unity • Output: Non inverted
  12. 12. Active Low-Pass Filter 12 • Remove high frequency noise interference. • Cutoff Frequency: 1 2π R1R2C1C2 = 1000 Hz • Order of filter: 2nd order • Gain: Unity • Output: Non inverted
  13. 13. 13 Analog Filters Simulation • LM358 dual Op-Amp IC was used in this Project. • Both filters ware designed and simulated in Porteous using LM358. • The PCB (printed circuit board) was also designed in Porteous Software LM358
  14. 14. 14 PCB Design of Analog FE 3D view of PCB with componentsPCB Layout
  15. 15. 15 EMG Recorder Circuit (Analog FE) AD620 LM358 RGain Pins to Connect Power Supply Electrode Connector Pins Output
  16. 16. 16 Complete EMG recorder Circuit Complete EMG Recorder FE
  17. 17. Recorded EMG Signals EMG signal Recorded shown on Oscilloscope 20/40 18
  18. 18. Signal Analysis in MATLAB 0 50 100 150 200 5000 10000 15000 Frequency (Hz) Magnitude Single frequency responce when there is no muscle contraction 0 50 100 150 200 2000 4000 6000 8000 10000 Frequency (Hz) Magnitude Single frequency responce when muscle contracts 21/40 19
  19. 19. 0 50 100 150 200 2000 4000 6000 8000 10000 Frequency (Hz) Magnitude Single frequency responce when there is no muscle contraction 0 50 100 150 200 2000 4000 6000 8000 10000 Frequency (Hz) Magnitude Single frequency responce when muscle contracts Signal Analysis in MATLAB (Cont…) • Filtering in Digital domain • Infinite impulse response (IIR) Filter • Notch Filter • To remove 50Hz power line noise • Order of filter: 2nd order • Stability: Stable • HPF • Cutoff frequency: 10Hz • Order of filter: 5th order • Stability: Stable • LPF • Cutoff frequency: 150Hz • Order of filter: 4th order • Stability: Stable Power line noise & other noise removal 20
  20. 20. Trial No. SNR with Background Noise (dB) SNR after Filtering (dB) Improvement in SNR after Filtering (dB) 1 38.08 65.89 27.81 2 36.52 65.11 28.58 3 39.12 67.74 28.61 4 38.28 66.64 28.35 5 42.05 70.74 28.68 Average 38.81 67.22 28.41 Signal Analysis in MATLAB (Cont…) Signal Improvement Analysis by calculating SNR EMG signal before and after filtering 𝐒𝐍𝐑 = 𝟐𝟎𝐥𝐨𝐠 𝟏𝟎 𝑺𝒊𝒈𝒏𝒂𝒍 𝑷𝒐𝒘𝒆𝒓 𝑵𝒐𝒊𝒔𝒆 𝑷𝒐𝒘𝒆𝒓 21
  21. 21. Prosthetic Arm 3D Design • Open Source Design • Designed and modified in TinkerCAD online free software 22
  22. 22. No. Name of the part No. of joints/parts Length /Weight 1 Thumb finger 2 5.5 cm 2 Index finger 3 6 cm 3 Middle finger 3 8.5 cm 4 Ring finger 3 7.5 cm 5 Pinky finger 3 5.5 cm 6 Palm 1 10 cm 7 Wrist 4 23 cm 8 Diameter at end of wrist ̶̶̶̶̶̶̶̶̶ 10 cm 9 Total length of the Arm ̶̶̶ 41.5 cm 10 In-fill ̶̶̶ 20% 11 Weight ̶̶̶ 300gm (approx.) 3D Printing (Technical Specs) • Printed in PRUSA mk3 • PLA filament • Speed 6.09gm/min 23
  23. 23. 3D Printed Arm • 5 Actuators (MG90s) for 5 Fingers • 5 DOF • Elastic foe flexibility • Strong thread as Tension wire • Extra supports for more stable finger movement 24
  24. 24. Control Circuitry Circuit includes o 2 Arduino pro-mini o EMG recorder Circuit o Voice recognition module o Prosthetic Arms actuators (MG90s servo motors) 1st Arduino • It gets signal from EMG recorder • Process the signal in digital domain using the coefficients calculated in MATLAB. • detects muscle construction by setting up a threshold and send a pulse to the 2nd Arduino 2nd Arduino • It detects the pulse from 1st Arduino. • For the 1st pulse the Arduino sends signal to the arm to perform a gesture and when it gets another pulse it sends a signal to relax the arm. • This Arduino parallelly checks the voice command. Whenever a different voice command is given the prosthetic arm performs a different gesture when it gets a pulse from 1st Arduino. 25
  25. 25. Gesture Mode Thumb Index Middle Ring Pinky Grab 130° 150° 160° 155° 130° Pick 130° 150° 0° 0° 0° Open 0° 0° 0° 0° 0° Control Circuitry (Cont…) • Prosthetic Control o Each finger is connected to a servo motor by a tension wire. Whenever the servo rotates the fingers of the arm move to perform a gesture o Servos are controlled by PWM signal with fixed frequency of 50 Hz. o All servos receive individual PWM signal from the 2nd Arduino. By varying the pulse width the angular rotation varies. o For different gestures the PWM signal changes differently for each finger as a result the each individual motor has individual angular rotation Initial Angle Maximum Angle 26
  26. 26. 2 Different gestures performed for different task Grabbing of Objects by the Arm27
  27. 27. Prosthetic Arm 1st Arduino EMG Recorder Circuit Servo motors Voice Recognition Module 2nd Arduino Complete System 28
  28. 28. 28 Performance Evaluation (Accuracy) 0 20 40 60 80 100 Subject 1 Subject 2 Subject 3 Subject 4 Subject 5 Subject 6 Total Accuracy 1st finger 2nd finger 3rd finger 𝐴𝑐𝑐 = 𝑇𝑃 + 𝑇𝑁 𝑇𝑃 + 𝑇𝑁 + 𝐹𝑃 + 𝐹𝑁 Average Accuracy per subject is around 84%! • 6 subjects • Each subject had 5 trials • And 6 commands in each trial • In total 180 trial samples for accuracy calculation
  29. 29. 29 Performance Evaluation (Response Time & Power Consumption) Supply Voltage Current Consumption (No load) Current Consumption (Full load) Total Power Consumption (Full load) Overall System Response Time 5V 0.5A 2.6A 13W 1.5 sec (approx.) • 7.4V 6000mAh Li-ion battery • Regulated using 7805 voltage regulator
  30. 30. (Component) Cost Analysis 30 SI Component Unit Cost (BDT) 1 Electrodes 3 30 2 Connectors 1 20 3 PCB 1 50 4 AD620 1 220 5 LM350 Op-Amp 1 10 6 Electrical components (resistor capacitor, connector, wires, LED, Vero-board, regulators etc.) ̶ 200 7 Lipo Batteries 2 400 8 Li-ion Batteries 6 300 9 Boost Converters 2 100 10 Arduino Pro-Mini 2 300 11 Prosthetic Arm (printing cost) 1 1000 12 Servo motor MG90s 5 750 13 Voice recognition module V3 1 2000 14 Others (glue, tension wire, elastic etc.) ̶ 100 Total cost 5480 Commercial EMG recorder available in Bangladesh (Price: 5150 BDT)
  31. 31. 31 Comparison with Other Works Ref No. Biomedical recorder Robotic/Prosthetic hand Extra feature 1 Commercial EMG recorder In house 3D printed Prosthetic Hand No Extra Feature 2 In house-built EMG recorder Not enough info available 3 Commercial EMG recorder No prosthesis / simulated controlling 4 Commercial biomedical signal recorder In house built Robotic arm 5 In house-built EMG recorder Commercial robotic arm 6 Not enough info No prosthesis / simulated controlling 7 Commercial EMG recorder Commercial 3D printed Prosthetic Hand 8 Commercial EMG recorder No prosthesis / simulated controlling 9 Not enough info In house built Prosthetic Hand 10 Commercial EMG recorder No prosthesis / simulated controlling 11 Commercial EMG recorder In house-built hand rehabilitation robotic system 12 Commercial EMG recorder In house 3D printed Prosthetic Hand 13 Not used Simulation test Voice Recognition14 In house 3D printed Prosthetic Hand 15 Commercial robotic arm This One In House built EMG recorder In house 3D printed prosthetic arm Voice Recognition
  32. 32. Conclusion  The EMG signal and Voice command controlled Prosthetic system developed in this project is less expensive and can be affordable for people in developing country like Bangladesh.  The system was successfully designed and tested on an amputee individual.  Since the whole system is designed and developed here, so if any modification required, can be possible. 32
  33. 33. Future Works  Making the prosthetic arm more user friendly  Using Dry electrodes  Making an in-house built voice recognition module with local recources  Improving the design of the prosthetic arm  Increasing the degree of freedom of the prosthetic arm  Making the project available for the people so that they can have the benefits of this project by improving their quality of life.  EMG signal based diagnosis of neuromuscular diseases. 33
  34. 34. 34 Sample Video of Experiment
  35. 35. References 35 1. Yazicioglu, Refet Firat, Chris Van Hoof, and Robert Puers. Biopotential readout circuits for portable acquisition systems. Springer Science & Business Media, 2008. 2. https://www.ottobockus.com/prosthetics/upper-limb-prosthetics/solution-overview/bebionic-hand/ (assessed on 02/09/2019) 3. https://www.touchbionics.com/(assessed on 02/09/2019) 4. http://www.openhandproject.org/(assessed on 02/09/2019) 5. https://openbionics.com/(assessed on 02/09/2019) 6. Mounika, M. P., B. S. S. Phanisankar, and M. Manoj. "Design & analysis of prosthetic hand with EMG technology in 3-D printing machine." Int. J. Curr. Eng. Technol 7, no. 1 (2017): 115-119. 7. Trzmiel, Grzegorz, Dariusz Kurz, and Wiktor Smoczyński. "The use of the EMG signal for the arm model control." In ITM Web of Conferences, vol. 28, p. 01024. EDP Sciences, 2019. 8. Ganiev, Asilbek, Ho-Sun Shin, and Kang-Hee Lee. "Study on virtual control of a robotic arm via a myo armband for the selfmanipulation of a hand amputee." Int. J. Appl. Eng. Res 11, no. 2 (2016): 775-782. 9. Minati, Ludovico, Natsue Yoshimura, and Yasuharu Koike. "Hybrid control of a vision-guided robot arm by EOG, EMG, EEG biosignals and head movement acquired via a consumer-grade wearable device." Ieee Access 4 (2016): 9528-9541. 10. Bitzer, Sebastian, and Patrick Van Der Smagt. "Learning EMG control of a robotic hand: towards active prostheses." In Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006., pp. 2819-2823. IEEE, 2006. 11. Blana, Dimitra, Theocharis Kyriacou, Joris M. Lambrecht, and Edward K. Chadwick. "Feasibility of using combined EMG and kinematic signals for prosthesis control: A simulation study using a virtual reality environment." Journal of Electromyography and Kinesiology 29 (2016): 21-27. 12. Said, S., M. Sheikh, F. Al-Rashidi, Y. Lakys, T. Beyrouthy, and A. Nait-ali. "A Customizable Wearable Robust 3D Printed Bionic Arm: Muscle Controlled." In 2019 3rd International Conference on Bio-engineering for Smart Technologies (BioSMART), pp. 1-6. IEEE, 2019. 13. Wong, Tat Hang, Davide Asnaghi, and Suk Wai Winnie Leung. "Mechatronics Enabling Kit for 3D Printed Hand Prosthesis." In Proceedings of the Design Society: International Conference on Engineering Design, vol. 1, no. 1, pp. 769-778. Cambridge University Press, 2019. 14. Borisov, Ivan I., Olga V. Borisova, Sergei V. Krivosheev, Roman V. Oleynik, and Stanislav S. Reznikov. "Prototyping of emg-controlled prosthetic hand with sensory system." IFAC- PapersOnLine 50, no. 1 (2017): 16027-16031. 15. Visconti, P., F. Gaetani, G. A. Zappatore, and P. Primiceri. "Technical features and functionalities of Myo armband: an overview on related literature and advanced applications of myoelectric armbands mainly focused on arm prostheses." Int. J. Smart Sens. Intell. Syst 11, no. 1 (2018): 1-25. 16. Abdallah, Ismail Ben, Yassine Bouteraa, and Chokri Rekik. "DESIGN AND DEVELOPMENT OF 3D PRINTED MYOELECTRIC ROBOTIC EXOSKELETON FOR HAND REHABILITATION." International Journal on Smart Sensing & Intelligent Systems 10, no. 2 (2017). 17. Hetherington, Austin T. "Integration of a Sensory Driven Model for Hand Grasp Function in 3D Printed Prostheses." (2018). 18. Gruppioni, E., B. G. Saldutto, A. G. Cutti, Elena Mainardi, and A. Davalli. "A voice-controlled prosthesis: test of a vocabulary and development of the prototype." In Proceeding of MEC (Myoelectric Control Conference). 2008. 19. Asyali, Musa Hakan, Mustafa Yilmaz, Mahmut Tokmakci, Kanber Sedef, Bekir Hakan Aksebzeci, and Rohin Mittal. "Design and implementation of a voice-controlled prosthetic hand." Turkish Journal of Electrical Engineering & Computer Sciences 19, no. 1 (2011): 33-46. 20. House, Brandi, Jonathan Malkin, and Jeff Bilmes. "The VoiceBot: a voice-controlled robot arm." In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 183-192. ACM, 2009.
  36. 36. Thank You! 36

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