Jamie Hutton and Emanuele Lindo Secco*

Robotics Lab, School of Mathematics, Computer Science and Engineering, Liverpool Hope University, UK

*Corresponding Author: Emanuele Lindo Secco, Robotics Lab, School of Mathematics, Computer Science and Engineering, Liverpool Hope University, UK.

Received: January 20, 2023; Published: February 06, 2023

Abstract

The learning curve of many prosthetic hands can be too difficult for users to grasp, this difficulty often leads users to stop using the prosthetic, especially if they have another hand which can pick up the slack. This paper created an interface controlled by a MYO Armband, which uses surface electrodes to detect ElectroMyoGraphic (EMG) signals in fore-arm muscles. The goal was to have participants take part in an experiment using the interface and track how well they can learn the process of moving a cursor across a 2D screen, the interface responds to four poses which control the cursors movement in left, right, up, and down motions. Two main variables were tracked, the time taken to complete a task and the accuracy on the cursor during the task, the poses being used were also tracked. All three participants had difficulties remembering the poses and controlling the cursor in the beginning, however after several attempts the participants saw improvements in time and accuracy. The improvements in time slow and even reversed in some instances, this is possibly because of fatigue in the arm being used, alternatively the accuracy continued to increase throughout the experiment for all three participants. There were two types of poses used, one type was using fingers “fist pose” and “spread fingers” pose, and the second type was using the wrist, “wave out” and “wave in” poses, two of the participants seemed to favor the wrist movements more than the finger movements. Conversely, the final participant favored the finger movements over the wrist movements, the reason for these differences could be comfort or possibly good/bad experiences during use in the early stages of the experiment.

Keywords: Human Robot Interface; Human Prosthetic Interface; ElectroMyoGraphy (EMG); User Interface; Upper Limb Prosthetics; Robotics

References

1. SPECIALISED COMMISSIONING TEAM NE. “Hand and upper limb reconstruction using vascularised composite allotransplantation (HAUL-VCA)” (2015).
2. Handi Hand (2022).
3. CÔTÉ-ALLARD U., et al. “Deep Learning for Electromyographic Hand Gesture Signal Classification Using Transfer Learning”. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27 (2019): 760-771.
4. KUIKEN TA., et al. “Prosthetic Command Signals Following Targeted Hyper-Reinnervation Nerve Transfer Surgery”. 2005 IEEE Engineering in Medicine and Biology 27^th Annual Conference, 17-18 Jan. 2006 (2005): 7652-7655.
5. LONDONCENTER TL P. Bebionic hand (2022).
6. Matrone G., et al. “Bio-Inspired Controller for a Dexterous Prosthetic Hand Based on Principal Component Analysis”. 31^th Annual Int Conf of the IEEE Eng in Medicine and Biology Society – EMBC (2009): 5022-5025.
7. Mcmullen D P., et al. “Demonstration of a Semi-Autonomous Hybrid BMI Using Human Intracranial EEG, Eye Tracking, and Computer Vision to Control a Robotic Upper Limb Prosthetic”. IEEE Trans on Neural Systems and Rehab Eng, 22 (2014): 784-796.
8. MENG J., et al. “Noninvasive Electroencephalogram Based Control of a Robotic Arm for Reach and Grasp Tasks”. Scientific Reports (2016): 6.
9. NICOLELIS MAL. “Brain–machine interfaces to restore motor function and probe neural circuits”. Nature Reviews Neuroscience 4 (2003): 417-422.
10. MAGENES G., et al. “ A new approach of multi-d.o.f. prosthetic control”. 2008 30^th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 20-25 Aug. 2008 (2008): 3443-3446.
11. SHANA R and SATTAR M. “Prosthetic hand control using wearable gesture armband based on surface electromyography”. Journal of Engineering and Applied Sciences 13 (2018): 9662.
12. STERN, B. 2022. Inside Myo (2022).
13. TSOLI A and JENKINS OC. “2d subspaces for user-driven robot grasping”. Robotics, Science and Systems Conference: Workshop on Robot Manipulation (2007): 7-2.
14. MATRONE G C., et al. “Principal components analysis based control of a multi-DoF underactuated prosthetic hand”. Journal of NeuroEngineering and Rehabilitation 7 (2010): 16.
15. Rosenstein N. “Python bindings for the Myo SDK” (2017).
16. EL Secco., et al. “Development of a sustainable and ergonomic interface for the EMG control of prosthetic hands”. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 192 (2017): 321-327.
17. EL Secco., et al. “Development of an Algorithm for the EMG Control of Prosthetic Hand, Soft Computing for Problem Solving”. Advances in Intelligent Systems and Computing, 1139, chapter 15, Springer.
18. M Ormazabal and EL Secco. “A low cost EMG Graphical User Interface controller for robotic hand”. Future Technologies Conference (FTC 2021), Lecture Notes in Networks and Systems 2 (2021): 459-475.
19. Mosier KM., et al. “Remapping hand movements in a novel geometrical environment”. Journal of Neurophysiology6 (2005): 4362-4372.
20. TS Chu., et al. “Performance Analysis of a Neuro Fuzzy Algorithm in Human Centered and Non-Invasive BCI”. Sixth International Congress on Information and Communication Technology (ICICT), 2021 - Lecture Notes in Networks and Systems 2 (2021): 241-252.
21. D Elstob and EL Secco. “A low cost EEG based BCI Prosthetic using motor imagery”. International Journal of Information Technology Convergence and Services1 (2016): 23-36.
22. ^th edition, Park Publishing. Co., Minneapolis, MN (2006): 30-37.

Citation

Citation: Jamie Hutton and Emanuele Lindo Secco. “Development of an Interface for Real-Time Control of a Dexterous Robotic Hand, Using MYO Muscle Sensor" Acta Scientific Computer Sciences 5.3 (2023): 25-36.

Copyright

Copyright: © 2023 Jamie Hutton and Emanuele Lindo Secco. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.