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Journal of NeuroEngineering and Rehabilitation
Published in: Journal of NeuroEngineering and Rehabilitation 1/2014

Open Access 01-12-2014 | Research

System training and assessment in simultaneous proportional myoelectric prosthesis control

Authors: Anders L Fougner, Øyvind Stavdahl, Peter J Kyberd

Published in: Journal of NeuroEngineering and Rehabilitation | Issue 1/2014

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Abstract

Background

Pattern recognition control of prosthetic hands take inputs from one or more myoelectric sensors and controls one or more degrees of freedom. However, most systems created allow only sequential control of one motion class at a time. Additionally, only recently have researchers demonstrated proportional myoelectric control in such systems, an option that is believed to make fine control easier for the user. Recent developments suggest improved reliability if the user follows a so-called prosthesis guided training (PGT) scheme.

Methods

In this study, a system for simultaneous proportional myoelectric control has been developed for a hand prosthesis with two motor functions (hand open/close, and wrist pro-/supination). The prosthesis has been used with a prosthesis socket equivalent designed for normally-limbed subjects. An extended version of PGT was developed for use with proportional control. The control system’s performance was tested for two subjects in the Clothespin Relocation Task and the Southampton Hand Assessment Procedure (SHAP). Simultaneous proportional control was compared with three other control strategies implemented on the same prosthesis: mutex proportional control (the same system but with simultaneous control disabled), mutex on-off control, and a more traditional, sequential proportional control system with co-contractions for state switching.

Results

The practical tests indicate that the simultaneous proportional control strategy and the two mutex-based pattern recognition strategies performed equally well, and superiorly to the more traditional sequential strategy according to the chosen outcome measures.

Conclusions

This is the first simultaneous proportional myoelectric control system demonstrated on a prosthesis affixed to the forearm of a subject. The study illustrates that PGT is a promising system training method for proportional control. Due to the limited number of subjects in this study, no definite conclusions can be drawn.
Literature
1.
2.
Lovely DF: Signals and signal processing for myoelectric control. In Powered upper limb prostheses: Control, implementation and clinical application. Edited by: Muzumdar A. Berlin Heidelberg, Germany: Springer-Verlag; 2004:35-53.CrossRef
3.
4.
Jiang N, Muceli S, Graimann B, Farina D: Effect of arm position on the prediction of kinematics from EMG in amputees. Med Biol Eng Comput 2013, 51: 143-151. [doi:10.1007/s11517-012-0979-4] 10.1007/s11517-012-0979-4CrossRefPubMedPubMedCentral
5.
Muceli S, Farina D: Simultaneous and proportional estimation of hand kinematics from EMG during mirrored movements at multiple degrees-of-freedom. IEEE Trans Neural Syst Rehabil Eng 2012,20(3):371-378. [doi:10.1109/TNSRE.2011.2178039]CrossRefPubMed
6.
7.
Ameri A, Englehart KB, Parker PA: A comparison between force and position control strategies in myoelectric prostheses. In Proc. of the IEEE Eng. Med. Biol. Soc. Conf. (EMBC). IEEE EMBS: IEEE; 2012:1342-1345.
8.
Pulliam CL, Lambrecht JM, Kirsch RF: User-in-the-loop continuous and proportional control of a virtual prosthesis in a posture matching task. In Proc. of the IEEE Eng. Med. Biol. Soc. Conf(EMBC), Volume 34. IEEE: IEEE EMBS; 2012:3557-3559.
9.
10.
Hargrove L, Losier YG, Lock B, Englehart KB, Hudgins B: A real-time pattern recognition based myoelectric control usability study implemented in a virtual environment. In Proc. of the IEEE Eng. Med. Biol. Soc. Conf. (EMBC). IEEE EMBS: IEEE; 2007:4842-4845.
11.
12.
Scheme E, Fougner A, Chan ADC, Englehart KB, Stavdahl Ø: Examining the adverse effects of limb position on pattern recognition based myoelectric control. In Proc. of the IEEE Eng. MedBiol. Soc. Conf. (EMBC), Volume 32. IEEE EMBS: IEEE; 2010:6337-6340.
13.
14.
Scheme E, Biron K, Englehart KB: Improving myoelectric pattern recognition positional robustness using advanced training protocols. In Proc. of the IEEE Eng. Med. Biol. Soc. Conf(EMBC), Volume 33. IEEE EMBS: IEEE; 2011:4828-4831.
15.
Hill W, Hermansson LN, Kyberd PJ, Swanson S, Hubbard S, Stavdahl Ø: Upper limb prosthetic outcome measures (ULPOM): a working group and their findings. J Prosthet Orthot 2009, 9: 69-82.CrossRef
16.
17.
18.
19.
20.
21.
22.
Bouwsema H, van der Sluis C, Bongers R: Learning to control opening and closing a myoelectric hand. Arch Phys Med Rehab 2010,91(9):1442-1446. 10.1016/j.apmr.2010.06.025CrossRef
23.
Simon A, Hargrove L, Lock B, Kuiken T: A decision-based velocity ramp for minimizing the effect of misclassifications during real-time pattern recognition control. IEEE Trans Biomed Eng 2011,58(8):2360-2368.CrossRef
24.
Farrell T, Weir R: The optimal controller delay for myoelectric prostheses. IEEE Trans Neural Syst Rehabil Eng 2007, 15: 111-118.CrossRefPubMed
25.
Hudgins B, Parker PA, Scott RN: A new strategy for multifunction myoelectric control. IEEE Trans Biomed Eng 1993, 40: 82-94. 10.1109/10.204774CrossRefPubMed
26.
Isidori A, Nicolò F: Uno Strumento Per la Rivelazione e la Misura di Alcuni Parametri dei Potenziali Mioelettrici [in Italian]. Rapporto interno no. II, Electrotechnical Institute, Sapienza Università, di Roma 1966.
27.
Isidori A, Monteleone M, Nicolò F: Hand prosthesis with continuous myoelectric control [in English]. Automazione e Strumentazione 1967,15(3):98-105.
28.
Loomis A: Figure Drawing for All It’s Worth. Irvine, CA, US: Walter Foster; 1943.
29.
Miller L, Lipschutz R, Stubblefield K, Lock B, Huang H, Williams T, Weir R, Kuiken T: Control of a six degree of freedom prosthetic arm after targeted muscle reinnervation surgery. Arch Phys Med Rehabil 2008,89(11):2057-2065. 10.1016/j.apmr.2008.05.016CrossRefPubMedPubMedCentral
30.
O’Shaughnessy K, Dumanian G, Lipschutz R, Miller L, Stubblefield K, Kuiken T: Targeted reinnervation to improve prosthesis control in transhumeral amputees: A report of three cases. J Bone Joint Surg 2008,90(2):393-400.CrossRefPubMed
31.
Simon A, Lock B, Stubblefield K: Patient training for functional use of pattern recognition–controlled prostheses. J Prosthet Orthot 2012,24(2):56-64. 10.1097/JPO.0b013e3182515437CrossRefPubMedPubMedCentral
32.
Light C, Chappell P, Kyberd P: Establishing a standardized clinical assessment tool of pathologic and prosthetic hand function: normative data, reliability, and validity. Arch Phys Med Rehabil 2002, 83: 776-783. 10.1053/apmr.2002.32737CrossRefPubMed
33.
Kyberd P, Murgia A, Gasson M, Tjerks T, Metcalf C, Chappell P, Warwick K, Lawson S, Barnhill T: Case studies to demonstrate the range of applications of the Southampton Hand Assessment Procedure. 2009,72(5):212-218.
34.
35.
Atkins DJ, Heard DC, Donovan WH: Epidemiologic overview of individuals with upper-limb loss and their reported research priorities. J Prosthet Orthot 1996, 8: 2-11. 10.1097/00008526-199600810-00003CrossRef
36.
Hill W, Hermansson LN, Kyberd PJ, Swanson S, Hubbard S, Stavdahl Ø: Functional Outcomes in the WHO-ICF Model: establishment of the upper limb prosthetic outcome measures group. J Prosthet Orthot 2009,21(2):115-119. 10.1097/JPO.0b013e3181a1d2dcCrossRef
37.
Bongers RM, Bouwsema H, van der Sluis CK: Changes in prehension, force control, and gaze when learning to use a myoelectric simulator with a MyoHand VariPlus Speed. In World Congress of the Int. Soc. of Prosthetics and Orthotics (ISPO). Leipzig, Germany; 2010.
38.
Bouwsema H, van der Sluis CK, Bongers RM: Trent International Prosthetics Symposium (TIPS). Loughborough, Trent, UK; 2012.
Metadata
Title
System training and assessment in simultaneous proportional myoelectric prosthesis control
Authors
Anders L Fougner
Øyvind Stavdahl
Peter J Kyberd
Publication date
01-12-2014
Publisher
BioMed Central
Published in
Journal of NeuroEngineering and Rehabilitation / Issue 1/2014
Electronic ISSN: 1743-0003
DOI
https://doi.org/10.1186/1743-0003-11-75

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