Literature DB >> 29637030

An Alternative Myoelectric Pattern Recognition Approach for the Control of Hand Prostheses: A Case Study of Use in Daily Life by a Dysmelia Subject.

Enzo Mastinu1, Johan Ahlberg2, Eva Lendaro1, Liselotte Hermansson3,4, Bo Hakansson1, Max Ortiz-Catalan1,5.   

Abstract

The functionality of upper limb prostheses can be improved by intuitive control strategies that use bioelectric signals measured at the stump level. One such strategy is the decoding of motor volition via myoelectric pattern recognition (MPR), which has shown promising results in controlled environments and more recently in clinical practice. Moreover, not much has been reported about daily life implementation and real-time accuracy of these decoding algorithms. This paper introduces an alternative approach in which MPR allows intuitive control of four different grips and open/close in a multifunctional prosthetic hand. We conducted a clinical proof-of-concept in activities of daily life by constructing a self-contained, MPR-controlled, transradial prosthetic system provided with a novel user interface meant to log errors during real-time operation. The system was used for five days by a unilateral dysmelia subject whose hand had never developed, and who nevertheless learned to generate patterns of myoelectric activity, reported as intuitive, for multi-functional prosthetic control. The subject was instructed to manually log errors when they occurred via the user interface mounted on the prosthesis. This allowed the collection of information about prosthesis usage and real-time classification accuracy. The assessment of capacity for myoelectric control test was used to compare the proposed approach to the conventional prosthetic control approach, direct control. Regarding the MPR approach, the subject reported a more intuitive control when selecting the different grips, but also a higher uncertainty during proportional continuous movements. This paper represents an alternative to the conventional use of MPR, and this alternative may be particularly suitable for a certain type of amputee patients. Moreover, it represents a further validation of MPR with dysmelia cases.

Entities:  

Keywords:  Prosthetic control; assessment of capacity for myoelectric control (ACMC); dysmelia; electromyogram (emg); myoelectric pattern recognition (MPR)

Year:  2018        PMID: 29637030      PMCID: PMC5881457          DOI: 10.1109/JTEHM.2018.2811458

Source DB:  PubMed          Journal:  IEEE J Transl Eng Health Med        ISSN: 2168-2372            Impact factor:   3.316


  28 in total

1.  Establishing a standardized clinical assessment tool of pathologic and prosthetic hand function: normative data, reliability, and validity.

Authors:  Colin M Light; Paul H Chappell; Peter J Kyberd
Journal:  Arch Phys Med Rehabil       Date:  2002-06       Impact factor: 3.966

Review 2.  Differences in myoelectric and body-powered upper-limb prostheses: Systematic literature review.

Authors:  Stephanie L Carey; Derek J Lura; M Jason Highsmith
Journal:  J Rehabil Res Dev       Date:  2015

3.  Offline accuracy: A potentially misleading metric in myoelectric pattern recognition for prosthetic control.

Authors:  Max Ortiz-Catalan; Faezeh Rouhani; Rickard Branemark; Bo Hakansson
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2015-08

4.  Resolving the limb position effect in myoelectric pattern recognition.

Authors:  Anders Fougner; Erik Scheme; Adrian D C Chan; Kevin Englehart; Oyvind Stavdahl
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2011-08-15       Impact factor: 3.802

5.  Test-retest reliability and rater agreements of assessment of capacity for myoelectric control version 2.0.

Authors:  Helen Y N Lindner; Ann Langius-Eklöf; Liselotte M N Hermansson
Journal:  J Rehabil Res Dev       Date:  2014

6.  Pattern-recognition arm prosthesis: a historical perspective-a final report.

Authors:  R W Wirta; D R Taylor; F R Finley
Journal:  Bull Prosthet Res       Date:  1978

7.  Embedded System for Prosthetic Control Using Implanted Neuromuscular Interfaces Accessed Via an Osseointegrated Implant.

Authors:  Enzo Mastinu; Pascal Doguet; Yohan Botquin; Bo Hakansson; Max Ortiz-Catalan
Journal:  IEEE Trans Biomed Circuits Syst       Date:  2017-05-23       Impact factor: 3.833

8.  First-in-man demonstration of a fully implanted myoelectric sensors system to control an advanced electromechanical prosthetic hand.

Authors:  Paul F Pasquina; Melissa Evangelista; A J Carvalho; Joseph Lockhart; Sarah Griffin; George Nanos; Patricia McKay; Morten Hansen; Derek Ipsen; James Vandersea; Josef Butkus; Matthew Miller; Ian Murphy; David Hankin
Journal:  J Neurosci Methods       Date:  2014-08-04       Impact factor: 2.390

9.  Development and evaluation of the activities measure for upper limb amputees.

Authors:  Linda Resnik; Laurel Adams; Matthew Borgia; Jemy Delikat; Roxanne Disla; Christopher Ebner; Lisa Smurr Walters
Journal:  Arch Phys Med Rehabil       Date:  2012-10-17       Impact factor: 3.966

10.  A Prosthetic Hand Body Area Controller Based on Efficient Pattern Recognition Control Strategies.

Authors:  Simone Benatti; Bojan Milosevic; Elisabetta Farella; Emanuele Gruppioni; Luca Benini
Journal:  Sensors (Basel)       Date:  2017-04-15       Impact factor: 3.576

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  8 in total

1.  A Real-Time EMG-Based Fixed-Bandwidth Frequency-Domain Embedded System for Robotic Hand.

Authors:  Biao Chen; Chaoyang Chen; Jie Hu; Thomas Nguyen; Jin Qi; Banghua Yang; Dawei Chen; Yousef Alshahrani; Yang Zhou; Andrew Tsai; Todd Frush; Henry Goitz
Journal:  Front Neurorobot       Date:  2022-06-30       Impact factor: 3.493

2.  Competitive motivation increased home use and improved prosthesis self-perception after Cybathlon 2020 for neuromusculoskeletal prosthesis user.

Authors:  Eric J Earley; Jan Zbinden; Maria Munoz-Novoa; Enzo Mastinu; Andrew Smiles; Max Ortiz-Catalan
Journal:  J Neuroeng Rehabil       Date:  2022-05-16       Impact factor: 5.208

3.  User training for machine learning controlled upper limb prostheses: a serious game approach.

Authors:  Morten B Kristoffersen; Andreas W Franzke; Raoul M Bongers; Michael Wand; Alessio Murgia; Corry K van der Sluis
Journal:  J Neuroeng Rehabil       Date:  2021-02-12       Impact factor: 4.262

4.  Portable Take-Home System Enables Proportional Control and High-Resolution Data Logging With a Multi-Degree-of-Freedom Bionic Arm.

Authors:  Mark R Brinton; Elliott Barcikowski; Tyler Davis; Michael Paskett; Jacob A George; Gregory A Clark
Journal:  Front Robot AI       Date:  2020-09-25

5.  Effect of multi-grip myoelectric prosthetic hands on daily activities, pain-related disability and prosthesis use compared with single-grip myoelectric prostheses: A single-case study.

Authors:  Cathrine Widehammar; Ayako Hiyoshi; Kajsa Lidström Holmqvist; Helen Lindner; Liselotte Hermansson
Journal:  J Rehabil Med       Date:  2022-01-03       Impact factor: 2.912

6.  Virtual Sensor of Surface Electromyography in a New Extensive Fault-Tolerant Classification System.

Authors:  Karina de O A de Moura; Alexandre Balbinot
Journal:  Sensors (Basel)       Date:  2018-05-01       Impact factor: 3.576

Review 7.  Real-Time EMG Based Pattern Recognition Control for Hand Prostheses: A Review on Existing Methods, Challenges and Future Implementation.

Authors:  Nawadita Parajuli; Neethu Sreenivasan; Paolo Bifulco; Mario Cesarelli; Sergio Savino; Vincenzo Niola; Daniele Esposito; Tara J Hamilton; Ganesh R Naik; Upul Gunawardana; Gaetano D Gargiulo
Journal:  Sensors (Basel)       Date:  2019-10-22       Impact factor: 3.576

Review 8.  Interfaces with the peripheral nervous system for the control of a neuroprosthetic limb: a review.

Authors:  Kadir A Yildiz; Alexander Y Shin; Kenton R Kaufman
Journal:  J Neuroeng Rehabil       Date:  2020-03-10       Impact factor: 4.262

  8 in total

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