A compact solution for vibrotactile proprioceptive feedback of wrist rotation and hand aperture.


Journal

Journal of neuroengineering and rehabilitation
ISSN: 1743-0003
Titre abrégé: J Neuroeng Rehabil
Pays: England
ID NLM: 101232233

Informations de publication

Date de publication:
13 Aug 2024
Historique:
received: 08 02 2024
accepted: 10 07 2024
medline: 13 8 2024
pubmed: 13 8 2024
entrez: 12 8 2024
Statut: epublish

Résumé

Closing the control loop between users and their prostheses by providing artificial sensory feedback is a fundamental step toward the full restoration of lost sensory-motor functions. We propose a novel approach to provide artificial proprioceptive feedback about two degrees of freedom using a single array of 8 vibration motors (compact solution). The performance afforded by the novel method during an online closed-loop control task was compared to that achieved using the conventional approach, in which the same information was conveyed using two arrays of 8 and 4 vibromotors (one array per degree of freedom), respectively. The new method employed Gaussian interpolation to modulate the intensity profile across a single array of vibration motors (compact feedback) to convey wrist rotation and hand aperture by adjusting the mean and standard deviation of the Gaussian, respectively. Ten able-bodied participants and four transradial amputees performed a target achievement control test by utilizing pattern recognition with compact and conventional vibrotactile feedback to control the Hannes prosthetic hand (test conditions). A second group of ten able-bodied participants performed the same experiment in control conditions with visual and auditory feedback as well as no-feedback. Conventional and compact approaches resulted in similar positioning accuracy, time and path efficiency, and total trial time. The comparison with control condition revealed that vibrational feedback was intuitive and useful, but also underlined the power of incidental feedback sources. Notably, amputee participants achieved similar performance to that of able-bodied participants. The study therefore shows that the novel feedback strategy conveys useful information about prosthesis movements while reducing the number of motors without compromising performance. This is an important step toward the full integration of such an interface into a prosthesis socket for clinical use.

Sections du résumé

BACKGROUND BACKGROUND
Closing the control loop between users and their prostheses by providing artificial sensory feedback is a fundamental step toward the full restoration of lost sensory-motor functions.
METHODS METHODS
We propose a novel approach to provide artificial proprioceptive feedback about two degrees of freedom using a single array of 8 vibration motors (compact solution). The performance afforded by the novel method during an online closed-loop control task was compared to that achieved using the conventional approach, in which the same information was conveyed using two arrays of 8 and 4 vibromotors (one array per degree of freedom), respectively. The new method employed Gaussian interpolation to modulate the intensity profile across a single array of vibration motors (compact feedback) to convey wrist rotation and hand aperture by adjusting the mean and standard deviation of the Gaussian, respectively. Ten able-bodied participants and four transradial amputees performed a target achievement control test by utilizing pattern recognition with compact and conventional vibrotactile feedback to control the Hannes prosthetic hand (test conditions). A second group of ten able-bodied participants performed the same experiment in control conditions with visual and auditory feedback as well as no-feedback.
RESULTS RESULTS
Conventional and compact approaches resulted in similar positioning accuracy, time and path efficiency, and total trial time. The comparison with control condition revealed that vibrational feedback was intuitive and useful, but also underlined the power of incidental feedback sources. Notably, amputee participants achieved similar performance to that of able-bodied participants.
CONCLUSIONS CONCLUSIONS
The study therefore shows that the novel feedback strategy conveys useful information about prosthesis movements while reducing the number of motors without compromising performance. This is an important step toward the full integration of such an interface into a prosthesis socket for clinical use.

Identifiants

pubmed: 39135110
doi: 10.1186/s12984-024-01420-y
pii: 10.1186/s12984-024-01420-y
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

142

Subventions

Organisme : Istituto Nazionale Assicurazione Infortuni sul Lavoro
ID : iHannes (PR19-PAS-P1)
Organisme : Istituto Nazionale Assicurazione Infortuni sul Lavoro
ID : iHannes (PR19-PAS-P1)
Organisme : Istituto Nazionale Assicurazione Infortuni sul Lavoro
ID : Dexter Hand (PR23-PAS-P1)

Informations de copyright

© 2024. The Author(s).

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Auteurs

Andrea Marinelli (A)

RehabTechnology Lab, Italian Institute of Technology, Via Morego, 30, Genova, GE, 16163, Italy. andrea.marinelli@iit.it.

Nicolò Boccardo (N)

RehabTechnology Lab, Italian Institute of Technology, Via Morego, 30, Genova, GE, 16163, Italy.
The Open University Affiliated Research Centre at Istituto Italiano di Tecnologia (ARC@IIT), via Morego 30, Genova, 16163, Italy.

Michele Canepa (M)

RehabTechnology Lab, Italian Institute of Technology, Via Morego, 30, Genova, GE, 16163, Italy.
The Open University Affiliated Research Centre at Istituto Italiano di Tecnologia (ARC@IIT), via Morego 30, Genova, 16163, Italy.

Dario Di Domenico (D)

RehabTechnology Lab, Italian Institute of Technology, Via Morego, 30, Genova, GE, 16163, Italy.
Department of Electronics and Telecommunications, Politecnico di Torino, Turin, 10124, Italy.

Emanuele Gruppioni (E)

Centro Protesi INAIL, Istituto Nazionale per l'Assicurazione contro gli Infortuni sul Lavoro, Vigorso di Budrio, Italy.

Matteo Laffranchi (M)

RehabTechnology Lab, Italian Institute of Technology, Via Morego, 30, Genova, GE, 16163, Italy.

Lorenzo De Michieli (L)

RehabTechnology Lab, Italian Institute of Technology, Via Morego, 30, Genova, GE, 16163, Italy.

Michela Chiappalone (M)

RehabTechnology Lab, Italian Institute of Technology, Via Morego, 30, Genova, GE, 16163, Italy.
Bioengineering Lab, University of Genova, DIBRIS, Genova, Italy.

Marianna Semprini (M)

RehabTechnology Lab, Italian Institute of Technology, Via Morego, 30, Genova, GE, 16163, Italy.

Strahinja Dosen (S)

Neurorehabilitation Systems, Department of Health Science and Technology, Aalborg University, Aalborg, Denmark. sdosen@hst.aau.dk.

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