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A low-cost transradial prosthesis controlled by the intention of muscular contraction

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Abstract

Persons with upper-limb amputations face severe problems due to a reduction in their ability to perform the activities of daily living. The prosthesis controlled by electromyography (EMG) or other signals from sensors, switches, accelerometers, etc., can somewhat regain the lost capability of such individuals. However, there are several issues with these prostheses, such as expensive cost, limited functionality, unnatural control, slow operating speed, complexity, heavyweight, large size, etc. This paper proposes an affordable transradial prosthesis, controlled by the muscular contractions from user intention. A surface EMG sensor was explicitly fabricated for capturing the muscle contraction information from the residual forearm of subjects with amputation. An under actuated 3D printed hand was developed with a prosthetic socket assembly to attach the remaining upper-limb of such subjects. The hand integrates an intuitive closed-loop control system that receives reference input from the designed sensor and feedback input from a force sensor installed at the thumb tip. The performance of the EMG sensor was compared with that of a traditional sensor in detecting muscle contractions from the subjects. The designed sensor showed a good correlation (r > 0.93) and a better signal-to-noise ratio (SNR) feature to the conventional sensor. Further, a successful trial of the developed hand prosthesis was made on five different subjects with transradial amputation. The users wearing the hand prototype were able to perform faster and delicate grasping of various objects. The implemented control system allowed the prosthesis users to control the grasp force of hand fingers with their intention of muscular contractions.

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References

  1. Slade P, Akhtar A, Nguyen M, Bretl T (2015) Tact: design and performance of an open-source, affordable, myoelectric prosthetic hand. In: 2015 IEEE International Conference on Robotics and Automation (ICRA). IEEE, Seattle, WA, USA, pp 6451–6456

  2. World Report on Disability. 350

  3. Sahu A, Sagar R, Sarkar S, Sagar S (2016) Psychological effects of amputation: A review of studies from India. Ind Psychiatry J 25:4–10. https://doi.org/10.4103/0972-6748.196041

    Article  PubMed  PubMed Central  Google Scholar 

  4. Geethanjali P (2016) Myoelectric control of prosthetic hands: state-of-the-art review. Med Devices (Auckl) 9:247–255. https://doi.org/10.2147/MDER.S91102

    Article  Google Scholar 

  5. Uellendahl J (2017) Myoelectric versus body-powered upper-limb prostheses: a clinical perspective. JPO 29:25. https://doi.org/10.1097/JPO.0000000000000151

    Article  Google Scholar 

  6. Cordella F, Ciancio AL, Sacchetti R, Davalli A, Cutti AG, Guglielmelli E, Zollo L (2016) Literature review on needs of upper limb prosthesis users. Front Neurosci. https://doi.org/10.3389/fnins.2016.00209

    Article  PubMed  PubMed Central  Google Scholar 

  7. Prakash A, Sahi AK, Sharma N, Sharma S (2020) Force myography controlled multifunctional hand prosthesis for upper-limb amputees. Biomed Signal Process Control 62:102122. https://doi.org/10.1016/j.bspc.2020.102122

    Article  Google Scholar 

  8. Cheesborough JE, Smith LH, Kuiken TA, Dumanian GA (2015) Targeted muscle reinnervation and advanced prosthetic arms. Semin Plast Surg 29:62–72. https://doi.org/10.1055/s-0035-1544166

    Article  PubMed  PubMed Central  Google Scholar 

  9. Fifer MS, Acharya S, Benz HL, Mollazadeh M, Crone NE, Thakor NV (2012) towards electrocorticographic control of a dexterous upper limb prosthesis. IEEE Pulse 3:38–42. https://doi.org/10.1109/MPUL.2011.2175636

    Article  PubMed  PubMed Central  Google Scholar 

  10. AL-Quraishi MS, Elamvazuthi I, Daud SA, Parasuraman S, Borboni A (2018) EEG-based control for upper and lower limb exoskeletons and prostheses: a systematic review. Sensors (Basel). https://doi.org/10.3390/s18103342

    Article  Google Scholar 

  11. Asghari Oskoei M, Hu H (2007) Myoelectric control systems: a survey. Biomed Signal Process Control 2:275–294. https://doi.org/10.1016/j.bspc.2007.07.009

    Article  Google Scholar 

  12. Parker P, Englehart K, Hudgins B (2006) Myoelectric signal processing for control of powered limb prostheses. J Electromyogr Kinesiol 16:541–548. https://doi.org/10.1016/j.jelekin.2006.08.006

    Article  CAS  PubMed  Google Scholar 

  13. George JA, Davis TS, Brinton MR, Clark GA (2020) Intuitive neuromyoelectric control of a dexterous bionic arm using a modified Kalman filter. J Neurosci Methods 330:108462. https://doi.org/10.1016/j.jneumeth.2019.108462

    Article  PubMed  Google Scholar 

  14. Jiang N, Rehbaum H, Vujaklija I, Graimann B, Farina D (2014) Intuitive, online, simultaneous, and proportional myoelectric control over two degrees-of-freedom in upper limb amputees. IEEE Trans Neural Syst Rehabil Eng 22:501–510. https://doi.org/10.1109/TNSRE.2013.2278411

    Article  PubMed  Google Scholar 

  15. Milosevic B, Benatti S, Farella E (2017) Design challenges for wearable EMG applications. In: Design, Automation Test in Europe Conference Exhibition (DATE), pp 1432–1437

  16. Liu J, Zhou P (2013) A novel myoelectric pattern recognition strategy for hand function restoration after incomplete cervical spinal cord injury. IEEE Trans Neural Syst Rehabil Eng 21:96–103. https://doi.org/10.1109/TNSRE.2012.2218832

    Article  PubMed  Google Scholar 

  17. Tavakoli M, Benussi C, Lourenco JL (2017) Single channel surface EMG control of advanced prosthetic hands. Expert Syst Appl 79:322–332. https://doi.org/10.1016/j.eswa.2017.03.012

    Article  Google Scholar 

  18. Pancholi S, Joshi AM (2018) Portable EMG data acquisition module for upper limb prosthesis application. IEEE Sens J 18:3436–3443. https://doi.org/10.1109/JSEN.2018.2809458

    Article  Google Scholar 

  19. Muzumdar A (2004) Powered upper limb prostheses. Springer, Berlin

    Book  Google Scholar 

  20. Prakash A, Sharma S (2020) A low-cost system to control prehension force of a custom-made myoelectric hand prosthesis. Res Biomed Eng 36:237–247. https://doi.org/10.1007/s42600-020-00064-w

    Article  Google Scholar 

  21. Cranny A, Cotton DPJ, Chappell PH, Beeby SP, White NM (2005) Thick-film force, slip and temperature sensors for a prosthetic hand. Meas Sci Technol 16:931–941. https://doi.org/10.1088/0957-0233/16/4/005

    Article  CAS  Google Scholar 

  22. Engeberg ED, Meek SG (2013) adaptive sliding mode control for prosthetic hands to simultaneously prevent slip and minimize deformation of grasped objects. IEEE/ASME Trans Mechatron 18:376–385. https://doi.org/10.1109/TMECH.2011.2179061

    Article  Google Scholar 

  23. Borisov II, Borisova OV, Krivosheev SV, Oleynik RV, Reznikov SS Prototyping of EMG-controlled prosthetic hand with sensory system. 5

  24. Cipriani C, Controzzi M, Carrozza MC (2011) The SmartHand transradial prosthesis. J NeuroEng Rehabil 8:29. https://doi.org/10.1186/1743-0003-8-29

    Article  PubMed  PubMed Central  Google Scholar 

  25. Furui A, Eto S, Nakagaki K, Shimada K, Nakamura G, Masuda A, Chin T, Tsuji T (2019) A myoelectric prosthetic hand with muscle synergy–based motion determination and impedance model–based biomimetic control. Sci Robot. https://doi.org/10.1126/scirobotics.aaw6339

    Article  PubMed  Google Scholar 

  26. Wang N, Lao K, Zhang X (2017) Design and myoelectric control of an anthropomorphic prosthetic hand. J Bionic Eng 14:47–59. https://doi.org/10.1016/S1672-6529(16)60377-3

    Article  Google Scholar 

  27. Williams MR, Walter W (2015) Development of a prototype over-actuated biomimetic prosthetic hand. PLoS ONE 10:e0118817. https://doi.org/10.1371/journal.pone.0118817

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. bebionic hand. https://www.ottobockus.com/prosthetics/upper-limb-prosthetics/solution-overview/bebionic-hand/. Accessed 21 Oct 2019

  29. i-limb quantum | Touch Bionics. http://touchbionics.com/products/active-prostheses/i-limb-quantum. Accessed 12 Apr 2019

  30. Michelangelo prosthetic hand. https://www.ottobockus.com/prosthetics/upper-limb-prosthetics/solution-overview/michelangelo-prosthetic-hand/. Accessed 12 Apr 2019

  31. VINCENTevolution 3. https://vincentsystems.de/en/prosthetics/vincent-evolution-3/. Accessed 12 Apr 2019

  32. Atzori M, Müller H (2015) Control capabilities of myoelectric robotic prostheses by hand amputees: a scientific research and market overview. Front Syst Neurosci 9:162. https://doi.org/10.3389/fnsys.2015.00162

    Article  PubMed  PubMed Central  Google Scholar 

  33. Li G, Schultz AE, Kuiken TA (2010) Quantifying pattern recognition—based myoelectric control of multifunctional transradial prostheses. IEEE Trans Neural Syst Rehabil Eng 18:185–192. https://doi.org/10.1109/TNSRE.2009.2039619

    Article  PubMed  PubMed Central  Google Scholar 

  34. Daley H, Englehart K, Hargrove L, Kuruganti U (2012) High density electromyography data of normally limbed and transradial amputee subjects for multifunction prosthetic control. J Electromyogr Kinesiol 22:478–484. https://doi.org/10.1016/j.jelekin.2011.12.012

    Article  PubMed  Google Scholar 

  35. Hamner SR, Narayan VG, Donaldson KM (2013) Designing for scale: development of the remotion knee for global emerging markets. Ann Biomed Eng 41:1851–1859. https://doi.org/10.1007/s10439-013-0792-8

    Article  PubMed  Google Scholar 

  36. Prakash A, Sharma S (2020) Development of an affordable myoelectric hand for transradial amputees: international journal of biomedical and clinical. Engineering 9:1–15. https://doi.org/10.4018/IJBCE.2020010101

    Article  CAS  Google Scholar 

  37. Al-Timemy AH, Bugmann G, Escudero J, Outram N (2013) Classification of finger movements for the dexterous hand prosthesis control with surface electromyography. IEEE J Biomed Health Inform 17:608–618. https://doi.org/10.1109/JBHI.2013.2249590

    Article  PubMed  Google Scholar 

  38. Khushaba RN, Al-Timemy AH, Al-Ani A, Al-Jumaily A (2017) A framework of temporal-spatial descriptors-based feature extraction for improved myoelectric pattern recognition. IEEE Trans Neural Syst Rehabil Eng 25:1821–1831. https://doi.org/10.1109/TNSRE.2017.2687520

    Article  PubMed  Google Scholar 

  39. Chandrapal M (2012) Intelligent Assistive Knee Orthotic Device Utilizing Pneumatic Artificial Muscles

  40. Chatfield LT, Fortune BC, McKenzie LR, Pretty CG (2018) Implementation of a particle filter to estimate torque from electromyography. IFAC-PapersOnLine 51:327–332. https://doi.org/10.1016/j.ifacol.2018.11.620

    Article  Google Scholar 

  41. Chatfield LT, Fortune BC, McKenzie LR, Pretty CG (2019) Development of an assist-as-need controller for an upper-limb exoskeleton with voluntary torque estimate. american society of mechanical engineers digital collection

  42. Sankai Y (2011) HAL: hybrid assistive limb based on cybernics. In: Kaneko M, Nakamura Y (eds) Robotics research. Springer, Berlin, Heidelberg, pp 25–34

    Google Scholar 

  43. Fortune BC, Pretty CG, Chatfield LT, McKenzie LR, Hayes MP (2019) Low-cost active electromyography. HardwareX 6:e00085. https://doi.org/10.1016/j.ohx.2019.e00085

    Article  Google Scholar 

  44. Prakash A, Kumari B, Sharma S (2019) A low-cost, wearable sEMG sensor for upper limb prosthetic application. J Med Eng Technol 43:235–247. https://doi.org/10.1080/03091902.2019.1653391

    Article  PubMed  Google Scholar 

  45. De Luca CJ (1997) The use of surface electromyography in biomechanics. J Appl Biomech 13:135–163. https://doi.org/10.1123/jab.13.2.135

    Article  Google Scholar 

  46. Gerdle B, Karlsson S, Day S, Djupsjöbacka M (1999) Acquisition, processing and analysis of the surface electromyogram. In: Windhorst U, Johansson H (eds) Modern techniques in neuroscience research. Springer, Berlin, pp 705–755

    Chapter  Google Scholar 

  47. Sharma N, Prakash A, Sahi AK, Sharma N, Sharma S (2020) Multimodal sensor to measure the concurrent electrical and mechanical activity of muscles for controlling a hand prosthesis. Instrum Sci Technol. https://doi.org/10.1080/10739149.2020.1804932

    Article  Google Scholar 

  48. Wang J, Tang L, Bronlund E (2013) Surface EMG signal amplification and filtering. Int J Comput Appl 82:15–22. https://doi.org/10.5120/14079-2073

    Article  Google Scholar 

  49. Prakash A, Sharma S, Sharma N (2019) A compact-sized surface EMG sensor for myoelectric hand prosthesis. Biomed Eng Lett 9:467–479. https://doi.org/10.1007/s13534-019-00130-y

    Article  PubMed  PubMed Central  Google Scholar 

  50. Farina D (2006) Interpretation of the surface electromyogram in dynamic contractions. Exerc Sport Sci Rev 34:121–127

    Article  Google Scholar 

  51. Heywood S, Pua YH, McClelland J, Geigle P, Rahmann A, Bower K, Clark R (2018) Low-cost electromyography – Validation against a commercial system using both manual and automated activation timing thresholds. J Electromyogr Kinesiol 42:74–80. https://doi.org/10.1016/j.jelekin.2018.05.010

    Article  PubMed  Google Scholar 

  52. Tatarian K, Couceiro MS, Ribeiro EP, Faria DR (2018) Stepping-stones to Transhumanism: an EMG-controlled low-cost prosthetic hand for academia. In: 2018 International Conference on Intelligent Systems (IS). IEEE, Funchal-Madeira, Portugal, pp 807–812

  53. Lobo-Prat J, Kooren PN, Stienen AH, Herder JL, Koopman BF, Veltink PH (2014) Non-invasive control interfaces forintention detection in active movement-assistive devices. J NeuroEng Rehabil 11:168. https://doi.org/10.1186/1743-0003-11-168

    Article  PubMed  PubMed Central  Google Scholar 

  54. Agostini V, Knaflitz M (2012) An algorithm for the estimation of the signal-to-noise ratio in surface myoelectric signals generated during cyclic movements. IEEE Trans Biomed Eng 59:219–225. https://doi.org/10.1109/TBME.2011.2170687

    Article  PubMed  Google Scholar 

  55. Enabling The Future. In: Enabling The Future. https://enablingthefuture.org/. Accessed 20 Oct 2020

  56. Melocchi A, Parietti F, Loreti G, Maroni A, Gazzaniga A, Zema L (2015) 3D printing by fused deposition modeling (FDM) of a swellable/erodible capsular device for oral pulsatile release of drugs. J Drug Deliv Sci Technol 30:360–367. https://doi.org/10.1016/j.jddst.2015.07.016

    Article  CAS  Google Scholar 

  57. Pylatiuk C, Schulz S, Kargov A, Bretthauer G (2004) Two multiarticulated hydraulic hand prostheses. Artif Organs 28:980–986. https://doi.org/10.1111/j.1525-1594.2004.00014.x

    Article  PubMed  Google Scholar 

  58. Prakash A, Sharma N, Sharma S (2020) Novel force myography sensor to measure muscle contractions for controlling hand prostheses. Instrum Sci Technol 48:43–62. https://doi.org/10.1080/10739149.2019.1655441

    Article  CAS  Google Scholar 

  59. Schoepp KR, Dawson MR, Schofield JS, Carey JP, Hebert JS (2018) Design and integration of an inexpensive wearable mechanotactile feedback system for myoelectric prostheses. IEEE J Transl Eng Health Med 6:1–11. https://doi.org/10.1109/JTEHM.2018.2866105

    Article  Google Scholar 

  60. Fougner A, Stavdahl O, Kyberd PJ, Losier YG, Parker PA (2012) Control of upper limb prostheses: terminology and proportional myoelectric control-a review. IEEE Trans Neural Syst Rehabil Eng 20:663–677. https://doi.org/10.1109/TNSRE.2012.2196711

    Article  PubMed  Google Scholar 

  61. Lenzi T, De Rossi SMM, Vitiello N, Carrozza MC (2011) Proportional EMG control for upper-limb powered exoskeletons. In: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, Boston, MA, pp 628–631

  62. Sinderby C, Lindström L, Grassino AE (1995) Automatic assessment of electromyogram quality. J Appl Physiol 79:1803–1815. https://doi.org/10.1152/jappl.1995.79.5.1803

    Article  CAS  PubMed  Google Scholar 

  63. Jamal MZ, Kim K-S (2018) A finely machined toothed silver electrode surface for improved acquisition of EMG signals. In: 2018 IEEE Sensors Applications Symposium (SAS). IEEE, Seoul, pp 1–5

  64. Sebastian F, Fu Q, Santello M, Polygerinos P (2017) Soft robotic haptic interface with variable stiffness for rehabilitation of neurologically impaired hand function. Front Robot AI. https://doi.org/10.3389/frobt.2017.00069

    Article  Google Scholar 

  65. Zhu G, Duan X, Deng H (2013) Hybrid Force-Position Fuzzy Control for a Prosthetic Hand. In: Lee J, Lee MC, Liu H, Ryu J-H (eds) Intelligent Robotics and Applications. Springer, Berlin , pp 415–426

    Chapter  Google Scholar 

  66. Geng W, Du Y, Jin W, Wei W, Hu Y, Li J (2016) Gesture recognition by instantaneous surface EMG images. Sci Rep 6:36571. https://doi.org/10.1038/srep36571

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Myoelectric Speed hands. https://www.ottobockus.com/prosthetics/upper-limb-prosthetics/solution-overview/myoelectric-devices-speedhands/. Accessed 12 Apr 2019

  68. Chadwell A, Kenney L, Thies S, Galpin A, Head J (2016) The reality of myoelectric prostheses: understanding what makes these devices difficult for some users to control. Front Neurorobot. https://doi.org/10.3389/fnbot.2016.00007

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

The authors would like to thank the Science and Engineering Research Board (SERB), Department of Science and Technology, Government of India, for funding this project.

Funding

Science and Engineering Research Board (SERB) [Grant No. CRG/2018/001612], Department of Science and Technology, Government of India.

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Correspondence to Alok Prakash.

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This article does not contain any studies with animals performed by any of the authors.

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This article involves surface EMG data acquisition from various human subjects. Ethical approval was taken from the Ethical committee, Institute of medical sciences, BHU, Varanasi before performing this experiment. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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Prakash, A., Sharma, S. A low-cost transradial prosthesis controlled by the intention of muscular contraction. Phys Eng Sci Med 44, 229–241 (2021). https://doi.org/10.1007/s13246-021-00972-w

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