On the Gesture Recognition of a Faint Phantom Motion for the Control of a Transradial Prosthesis amidst varying Contraction Forces


Nsugbe, E. (2023). On the Gesture Recognition of a Faint Phantom Motion for the Control of a Transradial Prosthesis amidst varying Contraction Forces. Digital Technologies Research and Applications, 2(2), 13–20.


  • Ejay Nsugbe
    Nsugbe Research Labs

The variation of the contraction force associated with the phantom motion used for the actuation of a bionic upper-limb prosthesis represents a scenario encountered regularly by amputees, while prior research appears to not have been able to succinctly address this problem. In this study, an extended prosthesis control system is proposed which is able to recognise gesture intent motions alongside the prediction of an associated contraction force as part of an advanced pattern recognition system. As part of this research topic, this paper introduces the proposed control architecture and is based on the solving of the gesture recognition problem amidst varying contraction forces for a transradial amputee with a seemingly faint phantom motion.

The work involves the application of a novel decomposition algorithm and the use of a set of computationally effective features, alongside the contrast of the recognition capabilities of the proposed approach using various classification models. The results show an enhanced recognition of gesture motion intent with the use of the decomposition method, despite the faint phantom motion signal from the amputee.



Prosthesis Pattern recognition Signal processing Bionics EMG

Author Biography


  1. Cordella F, Ciancio AL, Sacchetti R, Davalli A, Cutti AG, Guglielmelli E, et al. Literature Review on Needs of Upper Limb Prosthesis Users. Frontiers in Neuroscience. 2016;10:209.
  2. Nsugbe E. An Insight into Phantom Sensation and the Application of Ultrasound Imaging to the Study of Gesture Motions for Transhumeral Prosthesis. International Journal of Biomedical Engineering and Technology. 2021 Apr 28.
  3. LIMBLESS STATISTICS [Internet]. Limbless Sta-tistics. [cited 2021 Dec 27]. Available from:
  4. Nsugbe E, Phillips C, Fraser M, McIntosh J. Gesture recognition for transhumeral prosthesis control using EMG and NIR. IET Cyber-Systems and Ro-botics. 2020;2(3):122–31.
  5. Nsugbe E. Brain-machineand mus-cle-machine bio-sensing methods for gesture intent acquisition in upper-limb prosthesis control: a re-view. J Med Eng Technol. 2021 Feb;45(2):115–28.
  6. Armiger RS, Tenore FV, Bishop WE, Beaty JD, Bridges MM, Burck JM, et al. A Real-Time Virtual Integration Environment. JOHNS HOPKINS APL TECHNICAL DIGEST. 2011;30(3):9.
  7. Cipriani C, Controzzi M, Carrozza MC. The SmartHand transradial prosthesis. Journal of Neu-roEngineering and Rehabilitation. 2011 May 22;8(1):29.
  8. Light CM, Chappell PH. Development of a light-weight and adaptable multiple-axis hand prosthesis. Med Eng Phys. 2000 Dec;22(10):679–84.
  9. Losier Y, Wilson A, Scheme E, Englehart K, Kyberd P, Hudgins B. An overview of the UNB hand system. In Fredericton, Canada: University of New Bruns-wick; 2011.
  10. bebionic® The world’s most lifelike bionic hand [Internet]. Available from: https://
  11. Fascinated. With Michelangelo® Perfect use of precision technology [Internet]. Available from: https://accessprosthetics. com/wp-content/uploads/2017/06/michelangelo-technology.pdf
  12. Össur: Life Without Limitations [Internet]. Availa-ble from:
  13. Belter JT, Segil JL, Dollar AM, Weir RF. Mechan-ical design and performance specifications of an-thropomorphic prosthetic hands: a review. J Rehabil Res Dev. 2013;50(5):599–618.
  14. Scheme E, Englehart K. Electromyogram pattern recognition for control of powered upper-limb prostheses: state of the art and challenges for clin-ical use. J Rehabil Res Dev. 2011;48(6):643–59.
  15. Guo W, Sheng X, Liu H, Zhu X. Mechanomyogra-phy Assisted Myoeletric Sensing for Up-per-Extremity Prostheses: A Hybrid Approach. IEEE Sensors Journal. 2017;
  16. Barry D, Leonard J, Gitter A, Ball RD. Acoustic myography as a control signal for an externally powered prosthesis. Archives of physical medicine and rehabilitation. 1986;
  17. Guo W, Yao P, Sheng X, Zhang D, Zhu X. An en-hanced human-computer interface based on simul-taneous sEMG and NIRS for prostheses control. In: 2014 IEEE International Conference on Information and Automation (ICIA) [Internet]. Hailar, Hulun Buir, China: IEEE; 2014 [cited 2021 Dec 27]. p. 204–7. Available from: org/document/6932653/
  18. Nsugbe E. A pilot exploration on the use of NIR monitored haemodynamics in gesture recognition for transradial prosthesis control. Intelligent Sys-tems with Applications. 2021 Apr 1;9:200045.
  19. Guo W, Sheng X, Liu H, Zhu X. Development of a Multi-Channel Compact-Size Wireless Hybrid sEMG/NIRS Sensor System for Prosthetic Manip-ulation. IEEE Sensors Journal. 2015 Jan 1;16:1–1.
  20. Attenberger A, Wojciechowski S. A Real-Time Classification System for Upper Limb Prosthesis Control in MATLAB. In: Moreno-Díaz R, Pichler F, Quesada-Arencibia A, editors. Computer Aided Systems Theory – EUROCAST 2017: 16th Interna-tional Conference, Las Palmas de Gran Canaria, Spain, February 19-24, 2017, Revised Selected Pa-pers, Part II [Internet]. Springer International Pub-lishing; [cited 2021 Dec 27]. (Lecture Notes in Computer Science; vol. 10672). Available from:
  21. Guo W, Sheng X, Liu H, Zhu X. Toward an enhanced human-machine interface for upper-limb prosthesis control with combined EMG and NIRS signals. IEEE Transactions on Human-Machine Systems. 2017 Aug;47(4):564–75.
  22. Nazarpour K, Al-Timemy AH, Bugmann G, Jackson A. A note on the probability distribution function of the surface electromyogram signal. Brain Research Bulletin. 2013 Jan 1;90:88–91.
  23. Doheny EP, Lowery MM, Fitzpatrick DP, O’Malley MJ. Effect of elbow joint angle on force-EMG re-lationships in human elbow flexor and extensor muscles. J Electromyogr Kinesiol. 2008 Oct;18 (5):760–70.
  24. Al-Timemy A, Khushaba R, Bugmann G, Escudero J. Improving the Performance Against Force Varia-tion of EMG Controlled Multifunctional Up-per-Limb Prostheses for Transradial Amputees. IEEE transactions on neural systems and rehabili-tation engineering: a publication of the IEEE En-gineering in Medicine and Biology Society. 2015 Jun 23.
  25. Di Pino G, Guglielmelli E, Rossini PM. Neuroplas-ticity in amputees: main implications on bidirec-tional interfacing of cybernetic hand prostheses. Prog Neurobiol. 2009 Jun;88(2):114–26.
  26. Nsugbe E. Particle size distribution estimation of a powder agglomeration process using acoustic emissions [Internet] [Thesis]. 2017 [cited 2021 Dec 27]. Available from:
  27. Nsugbe E, Starr A, Foote P, Ruiz-Carcel C, Jennions I. Size Differentiation Of A Continuous Stream Of Particles Using Acoustic Emissions. IOP Conf Ser: Mater Sci Eng. 2016 Nov;161:012090.
  28. Nsugbe E, Ruiz-Carcel C, Starr A, Jennions I. Es-timation of Fine and Oversize Particle Ratio in a Heterogeneous Compound with Acoustic Emissions. Sensors. 2018 Mar;18(3):851.
  29. Nsugbe E, Starr A, Jennions IK, Ruiz-Cárcel C. Particle Size Distribution Estimation of A Mixture of Regular and Irregular Sized Particles Using Acoustic Emissions. Procedia Manufacturing. 2017 Dec 31;11:2252–9.
  30. Nsugbe E, William Samuel O, Asogbon MG, Li G. Contrast of multi-resolution analysis approach to transhumeral phantom motion decoding. CAAI Transactions on Intelligence Technology. 2021; 6(3):360–75.
  31. Nsugbe E, Sanusi I. Towards an affordable magne-tomyography instrumentation and low model com-plexity approach for labour imminency prediction using a novel multiresolution analysis. Applied AI Letters [Internet]. 2021 Sep [cited 2021 Dec 27];2(3). Available from:
  32. Daubechies I. Ten lectures on wavelets. Phil-adelphia, Pa: Society for Industrial and Applied Mathematics; 1992. 357 p. (CBMS-NSF regional conference series in applied mathematics).
  33. Nsugbe E. On the Application of Metaheuristics and Deep Wavelet Scattering Decompositions for the Prediction of Adolescent Psychosis using Brain Wave Signals. In peer review.
  34. Nsugbe E, Al-Timemy AH, Samuel OW. Intelli-gence Combiner: A Combination of Deep Learning and Handcrafted Features for an Adolescent Psy-chosis Prediction using EEG Signals. In peer re-view.
  35. Too J, Abdullah AR, Saad NM. Classification of Hand Movements based on Discrete Wavelet Transform and Enhanced Feature Extraction. In-ternational Journal of Advanced Computer Science and Applications (IJACSA) [Internet]. 2019 29 [cited 2021 Dec 27];10(6). Available from: https://thesai. org/Publications/ViewPaper?Volume=10&Issue=6&Code=IJACSA&SerialNo=12.
  36. Sharma H, Kumar S. A Survey on Decision Tree Algorithms of Classification in Data Mining. In-ternational Journal of Science and Research (IJSR). 2016 Apr 1;5.