Application of context-dependent interpretation of biosignals recognition to control a bionic multifunctional hand prosthesis

• Autorzy: Trajdos Pawel , Kurzynski Marek

Identyfikatory

DOI

10.1016/j.bbe.2024.01.001

• Języki publikacji: EN

Abstrakty

EN

The paper presents an original method for controlling a surface-electromyography-driven (sEMG) prosthesis. A context-dependent recognition system is proposed in which the same class of sEMG signals may have a different interpretation, depending on the context. This allowed the repertoire of performed movements to be increased. The proposed structure of the context-dependent recognition system includes unambiguously defined decision sequences covering the overall action of the prosthesis, i.e. the so-called boxes. Because the boxes are mutually isolated environments, each box has its own interpretation of the recognition result, as well as a separate local-recognition-task-focused classifier. Due to the freedom to assign contextual meanings to classes of biosignals, the construction procedure of the classifier can be optimised in terms of the local classification quality in a given box or the classification quality of the entire system. In the paper, two optimisation problems are formulated, differing in the adopted constraints on optimisation variables, with the methods of solving the problems based on an exhaustive search and an evolutionary algorithm, being developed. Experimental studies were conducted using signals from 1 able-bodied person with simulation of amputation and 10 volunteers with transradial amputations. The study compared the classical recognition system and the context-dependent system for various classifier models. An unusual testing strategy was adopted in the research, taking into account the specificity of the considered recognition task, with two original quality measures resulting from this scheme then being applied. The results obtained confirm the hypothesis that the application of the context-dependent classifier led to an improvement in classification quality.

Słowa kluczowe

EN

sEMG classification; context dependent classification; upper limb prosthesis

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2024
• Tom: Vol. 44, no. 1
• Strony: 161--182
• Opis fizyczny: Bibliogr. 57 poz., rys., tab.

Twórcy

autor

Trajdos Pawel

• Wroclaw University of Science and Technology, Wybrzeze Wyspianskiego 27, Wroclaw, 50-370, Poland

autor

Kurzynski Marek

• Wroclaw University of Science and Technology, Wybrzeze Wyspianskiego 27, Wroclaw, 50-370, Poland

Bibliografia

• [1] Kay SPJ, Leonard DA. Hand transplantation: Can we balance the risks and benefits? J Hand Surg (European Volume) 2023;48(3):208-13.
• [2] Milek D, Reed LT, Echternacht SR, Shanmugarajah K, Cetrulo CL, Lellouch AG, Langstein HN, et al. A systematic review of the reported complications related to facial and upper extremity vascularized composite allotransplantation. J Surg Res 2023;281:164-75.
• [3] Ghadage D, Bagde R, Jha S, Dhadi M, Barhate C. A review on current technological advancements in prosthetic arms. In: 2023 3rd International conference on advances in computing, communication, embedded and secure systems. IEEE; 2023, p. 328-33. http://dx.doi.org/10.1109/access57397.2023.10200952.
• [4] Piazza C, Grioli G, Catalano M, Bicchi A. A century of robotic hands. Annu Rev Control, Robot, Auton Syst 2019;2(1):1-32.
• [5] Chen Z, Min H, Wang D, Xia Z, Sun F, Fang B. A review of myoelectric control for prosthetic hand manipulation. Biomimetics 2023;8(3):328.
• [6] Yadav D, Veer K. Recent trends and challenges of surface electromyography in prosthetic applications. Biomed Eng Lett 2023;13(3):353-73.
• [7] Parajuli N, Sreenivasan N, Bifulco P, Cesarelli M, Savino S, Niola V, et al. Real-time EMG based pattern recognition control for hand prostheses: A review on existing methods, challenges and future implementation. Sensors 2019;19(20):4596.
• [8] Hahne JM, Wilke MA, Koppe M, Farina D, Schilling AF. Longitudinal case study of regression-based hand prosthesis control in daily life. Front Neurosci 2020;14.
• [9] Campbell E, Phinyomark A, Scheme E. Current trends and confounding factors in myoelectric control: Limb position and contraction intensity. Sensors 2020;20(6):1613.
• [10] Mendez V, Iberite F, Shokur S, Micera S. Current solutions and future trends for robotic prosthetic hands. Annu Rev Control, Robot, Auton Syst 2021;4(1):595-627.
• [11] Freitas MLB, Mendes JJA, Dias TS, Siqueira HV, Stevan SL. Surgical instrument signaling gesture recognition using surface electromyography signals. Sensors 2023;23(13):6233.
• [12] Kurzynski M, Trajdos P, Wolczowski A. Multiclassifier system using class and interclass competence of base classifiers applied to the recognition of grasping movements in the control of bioprosthetic hand. In: Progress in artificial intelligence. Springer International Publishing; 2017, p. 174-85. http://dx.doi. Org/10.1007/978-3-319-65340-2_15.
• [13] Akbulut A, Gungor F, Tarakci E, Aydin MA, Zaim AH, Catal C. Identification of phantom movements with an ensemble learning approach. Comput Biol Med 2022;150:106132.
• [14] Subasi A, Qaisar SM. Surface EMG signal classification using TQWT, bagging and boosting for hand movement recognition. J Ambient Intell Humaniz Comput 2020;13(7):3539-54.
• [15] Simon AM, Turner KL, Miller LA, Dumanian GA, Potter BK, Beachler MD, et al. Myoelectric prosthesis hand grasp control following targeted muscle reinnervation in individuals with transradial amputation. PLoS One 2023;18(1):e0280210.
• [16] Nguyen AT, Xu J, Jiang M, Luu DK, Wu T, kin Tam W, et al. A bioelectric neural interface towards intuitive prosthetic control for amputees. J Neural Eng 2020;17(6):066001.
• [17] Moradi A, Rafiei H, Daliri M, Akbarzadeh-T. M-R, Akbarzadeh A, Naddaf-Sh. A-M, et al. Clinical implementation of a bionic hand controlled with kineticomyographic signals. Sci Rep 2022;12(1).
• [18] Rask DM, Adams MH, Liverneaux P, Plucknette BF, Wilson DJ, Alderete JF, et al. Targeted muscle reinnervation in upper extremity amputation in military hand surgery: A systematic review. Hand Surg Rehabil 2023;42(5):392=9.
• [19] Schone HR, Udeozor M, Moninghoff M, Rispoli B, Vandersea J, Lock B, et al. Should bionic limb control mimic the human body? Impact of control strategy on bionic hand skill learning. 2023, bioRxiv.
• [20] Fajardo J, Maldonado G, Cardona D, Ferman V, Rohmer E. Evaluation of userprosthesis-interfaces for sEMG-based multifunctional prosthetic hands. Sensors 2021;21(21):7088.
• [21] Fajardo J, Ferman V, Munoz A, Andrade D, Neto AR, Rohmer E. User-prosthesis interface for upper limb prosthesis based on object classification. In: 2018 Latin American robotic symposium, 2018 Brazilian symposium on robotics (SBR) and 2018 workshop on robotics in education. IEEE; 2018, p. 390-5. http://dx.doi.org/10.1109/lars/sbr/wre.2018.00076.
• [22] Shi C, Yang D, Qiu S, Zhao J. I-GSI: A novel grasp switching interface based on eye-tracking and augmented reality for multi-grasp prosthetic hands. IEEE Robot Automat Lett 2023;8(3):1619-26.
• [23] Patel GK, Hahne JM, Castellini C, Farina D, Dosen S. Context-dependent adaptation improves robustness of myoelectric control for upper-limb prostheses. J Neural Eng 2017;14(5):056016.
• [24] Batzianoulis I, Simon AM, Hargrove L, Billard A. Reach-to-grasp motions: Towards a dynamic classification approach for upper-limp prosthesis. In: 2019 9th International IEEE/EMBS conference on neural engineering. NER, IEEE; 2019, p. 287-90. http://dx.doi.org/10.1109/ner.2019.8717110.
• [25] Nacpil E, Wang Z, Zheng R, Kaizuka T, Nakano K. Design and evaluation of a surface electromyography-controlled steering assistance interface. Sensors 2019;19(6):1308.
• [26] Geng Y, Zhou P, Li G. Toward attenuating the impact of arm positions on electromyography pattern-recognition based motion classification in transradial amputees. J NeuroEng Rehabil 2012;9(1).
• [27] Cardona D, Maldonado G, Ferman V, Lemus A, Fajardo J. Impact of diverse aspects in user-prosthesis interfaces for myoelectric upper-limb prostheses. In: 2020 8th IEEE RAS/EMBS international conference for biomedical robotics and biomechatronics. BioRob, IEEE; 2020, p. 954-60. http://dx.doi.org/10.1109/biorob49111.2020.9224288.
• [28] D’Accolti D, Clemente F, Mannini A, Mastinu E, Ortiz-Catalan M, Cipriani C. Online classification of transient EMG patterns for the control of the wrist and hand in a transradial prosthesis. IEEE Robot Automat Lett 2023;8(2):1045-52.
• [29] Piazza C, Simon AM, Turner KL, Miller LA, Catalano MG, Bicchi A, et al. Exploring augmented grasping capabilities in a multi-synergistic soft bionic hand. J NeuroEng Rehabil 2020;17(1).
• [30] Farina D, Jiang N, Rehbaum H, Holobar A, Graimann B, Dietl H, et al. The extraction of neural information from the surface EMG for the control of upperlimb prostheses: Emerging avenues and challenges. IEEE Trans Neural Syst Rehabil Eng 2014;22(4):797-809.
• [31] Kristoffersen MB, Franzke AW, Bongers RM, Wand M, Murgia A, van der Sluis CK. User training for machine learning controlled upper limb prostheses: A serious game approach. J NeuroEng Rehabil 2021;18(1).
• [32] Garbarini F, Bisio A, Biggio M, Pia L, Bove M. Motor sequence learning and intermanual transfer with a phantom limb. Cortex 2018;101:181-91.
• [33] Kurzynski M, Jaskolska A, Marusiak J, Wolczowski A, Bierut P, Szumowski L, et al. Computer-aided training sensorimotor cortex functions in humans before the upper limb transplantation using virtual reality and sensory feedback. Comput Biol Med 2017;87:311-21.
• [34] Dyson M, Dupan S, Jones H, Nazarpour K. Learning, generalization, and scalability of abstract myoelectric control. IEEE Trans Neural Syst Rehabil Eng 2020;28(7):1539-47.
• [35] Sarvamangala DR, Kulkarni RV. Convolutional neural networks in medical image understanding: A survey. Evol Intell 2021;15(1):1-22.
• [36] Grzegorzek M. Sensor data understanding. Logos Verlag Berlin; 2017.
• [37] Cini F, Ortenzi V, Corke P, Controzzi M. On the choice of grasp type and location when handing over an object. Science Robotics 2019;4(27).
• [38] Barandas M, Folgado D, Fernandes L, Santos S, Abreu M, Bota P, et al. TSFEL: Time series feature extraction library. SoftwareX 2020;11:100456.
• [39] Junior JJAM, Freitas ML, Siqueira HV, Lazzaretti AE, Pichorim SF, Stevan SL. Feature selection and dimensionality reduction: An extensive comparison in hand gesture classification by sEMG in eight channels armband approach. Biomed Signal Process Control 2020;59:101920.
• [40] Shariatzadeh M, Hafshejani EH, J.Mitchell C, Chiao M, Grecov D. Predicting muscle fatigue during dynamic contractions using wavelet analysis of surface electromyography signal. Biocybern Biomed Eng 2023;43(2):428-41.
• [41] Khan MA. Special issue ‘‘algorithms for feature selection’’. Algorithms 2023;16(8):368.
• [42] Velliangiri S, Alagumuthukrishnan S, joseph SIT. A review of dimensionality reduction techniques for efficient computation. Procedia Comput Sci 2019;165:104-11.
• [43] Calado A, Soares F, Matos D. A review on commercially available anthropomorphic myoelectric prosthetic hands, pattern-recognition-based microcontrollers and sEMG sensors used for prosthetic control. In: 2019 IEEE international conference on autonomous robot systems and competitions. IEEE; 2019, p. 1-6. http://dx.doi.org/10.1109/icarsc.2019.8733629.
• [44] Kaur S, Kumar Y, Koul A, Kamboj SK. A systematic review on metaheuristic optimization techniques for feature selections in disease diagnosis: Open issues and challenges. Arch Comput Methods Eng 2022;30(3):1863-95.
• [45] Rabin N, Kahlon M, Malayev S, Ratnovsky A. Classification of human hand movements based on EMG signals using nonlinear dimensionality reduction and data fusion techniques. Expert Syst Appl 2020;149:113281.
• [46] Lorena AC, Garcia LPF, Lehmann J, Souto MCP, Ho TK. How complex is your classification problem? ACM Comput Surv 2019;52(5):1-34.
• [47] Gong M. A novel performance measure for machine learning classification. Int J Manag Inf Technol 2021;13(1):11-9.
• [48] Cicirello VA. Classification of permutation distance metrics for fitness landscape analysis. In: Bio-inspired information and communication technologies. Springer International Publishing; 2019, p. 81-97. http://dx.doi.org/10.1007/978-3-030- 24202-2_7.
• [49] Zebari R, Abdulazeez A, Zeebaree D, Zebari D, Saeed J. A comprehensive review of dimensionality reduction techniques for feature selection and feature extraction. J Appl Sci Technol Trends 2020;1(2):56-70.
• [50] Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. Scikit-learn: Machine learning in Python. J Mach Learn Res 2011;12:2825-30.
• [51] Al-Timemy AH, Khushaba RN, Bugmann G, Escudero J. Improving the performance against force variation of EMG controlled multifunctional upper-limb prostheses for transradial amputees. IEEE Trans Neural Syst Rehabil Eng 2016;24(6):650-61.
• [52] Garcia S, Herrera F. An extension on‘‘statistical comparisons of classifiers over multiple data sets’’for all pairwise comparisons. J Mach Learn Res 2008;9:2677-94.
• [53] Suplino LO, Sommer LF, Forner-Cordero A. EMG-based control in a test platform for exoskeleton with one degree of freedom. In: 2019 41st annual international conference of the IEEE engineering in medicine and biology society. IEEE; 2019, p. 5366-9. http://dx.doi.org/10.1109/embc.2019.8856836.
• [54] Luaces O, Díez J, Barranquero J, del Coz JJ, Bahamonde A. Binary relevance efficacy for multilabel classification. Prog Artif Intell 2012;1(4):303-13.
• [55] Virtanen P, Gommers R, Oliphant TE, Haberland M, Reddy T, Cournapeau D, et al. SciPy 1.0: Fundamental algorithms for scientific computing in Python. Nature Methods 2020;17(3):261-72.
• [56] Rudiman R, Mirbagheri A, Candrawinata VS. Assessment of robotic telesurgery system among surgeons: A single-center study. J Robot Surg 2023.
• [57] Wolczowski A, Bledowski M, Witkowski J. The system for EMG and MMG singals recording for the bioprosthetic hand control. J Automat, Mob Robot Intell Syst 2017;11(3):22-9.

Uwagi

PL

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).