Literature DB >> 25420268

Sensory feedback in prosthetics: a standardized test bench for closed-loop control.

Strahinja Dosen, Marko Markovic, Cornelia Hartmann, Dario Farina.   

Abstract

Closing the control loop by providing sensory feedback to the user of a prosthesis is an important challenge, with major impact on the future of prosthetics. Developing and comparing closed-loop systems is a difficult task, since there are many different methods and technologies that can be used to implement each component of the system. Here, we present a test bench developed in Matlab Simulink for configuring and testing the closed-loop human control system in standardized settings. The framework comprises a set of connected generic blocks with normalized inputs and outputs, which can be customized by selecting specific implementations from a library of predefined components. The framework is modular and extensible and it can be used to configure, compare and test different closed-loop system prototypes, thereby guiding the development towards an optimal system configuration. The use of the test bench was demonstrated by investigating two important aspects of closed-loop control: performance of different electrotactile feedback interfaces (spatial versus intensity coding) during a pendulum stabilization task and feedforward methods (joystick versus myocontrol) for force control. The first experiment demonstrated that in the case of trained subjects the intensity coding might be superior to spatial coding. In the second experiment, the control of force was rather poor even with a stable and precise control interface (joystick), demonstrating that inherent characteristics of the prosthesis can be an important limiting factor when considering the overall effectiveness of the closed-loop control. The presented test bench is an important instrument for investigating different aspects of human manual control with sensory feedback.

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Year:  2014        PMID: 25420268     DOI: 10.1109/TNSRE.2014.2371238

Source DB:  PubMed          Journal:  IEEE Trans Neural Syst Rehabil Eng        ISSN: 1534-4320            Impact factor:   3.802


  11 in total

1.  Building an internal model of a myoelectric prosthesis via closed-loop control for consistent and routine grasping.

Authors:  Strahinja Dosen; Marko Markovic; Nicola Wille; Markus Henkel; Mario Koppe; Andrei Ninu; Cornelius Frömmel; Dario Farina
Journal:  Exp Brain Res       Date:  2015-03-25       Impact factor: 1.972

2.  EMG Biofeedback for online predictive control of grasping force in a myoelectric prosthesis.

Authors:  Strahinja Dosen; Marko Markovic; Kelef Somer; Bernhard Graimann; Dario Farina
Journal:  J Neuroeng Rehabil       Date:  2015-06-19       Impact factor: 4.262

3.  Electrotactile Feedback Improves Performance and Facilitates Learning in the Routine Grasping Task.

Authors:  Milica Isaković; Minja Belić; Matija Štrbac; Igor Popović; Strahinja Došen; Dario Farina; Thierry Keller
Journal:  Eur J Transl Myol       Date:  2016-06-13

4.  Tactile feedback is an effective instrument for the training of grasping with a prosthesis at low- and medium-force levels.

Authors:  Alessandro Marco De Nunzio; Strahinja Dosen; Sabrina Lemling; Marko Markovic; Meike Annika Schweisfurth; Nan Ge; Bernhard Graimann; Deborah Falla; Dario Farina
Journal:  Exp Brain Res       Date:  2017-05-26       Impact factor: 1.972

Review 5.  Selectivity and Longevity of Peripheral-Nerve and Machine Interfaces: A Review.

Authors:  Usman Ghafoor; Sohee Kim; Keum-Shik Hong
Journal:  Front Neurorobot       Date:  2017-10-31       Impact factor: 2.650

6.  Myocontrol is closed-loop control: incidental feedback is sufficient for scaling the prosthesis force in routine grasping.

Authors:  Marko Markovic; Meike A Schweisfurth; Leonard F Engels; Dario Farina; Strahinja Dosen
Journal:  J Neuroeng Rehabil       Date:  2018-09-03       Impact factor: 4.262

7.  Online Closed-Loop Control Using Tactile Feedback Delivered Through Surface and Subdermal Electrotactile Stimulation.

Authors:  Jian Dong; Winnie Jensen; Bo Geng; Ernest Nlandu Kamavuako; Strahinja Dosen
Journal:  Front Neurosci       Date:  2021-02-18       Impact factor: 4.677

8.  Combined spatial and frequency encoding for electrotactile feedback of myoelectric signals.

Authors:  Sara Nataletti; Fabrizio Leo; Jakob Dideriksen; Luca Brayda; Strahinja Dosen
Journal:  Exp Brain Res       Date:  2022-07-25       Impact factor: 2.064

9.  The clinical relevance of advanced artificial feedback in the control of a multi-functional myoelectric prosthesis.

Authors:  Marko Markovic; Meike A Schweisfurth; Leonard F Engels; Tashina Bentz; Daniela Wüstefeld; Dario Farina; Strahinja Dosen
Journal:  J Neuroeng Rehabil       Date:  2018-03-27       Impact factor: 4.262

10.  Psychometric characterization of incidental feedback sources during grasping with a hand prosthesis.

Authors:  Meike Annika Wilke; Christian Niethammer; Britta Meyer; Dario Farina; Strahinja Dosen
Journal:  J Neuroeng Rehabil       Date:  2019-12-10       Impact factor: 4.262

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