Electrotactile feedback for the discrimination of different surface textures using a microphone
(2021) In Sensors 21(10).- Abstract
Most commercial prosthetic hands lack closed-loop feedback, thus, a lot of research has been focusing on implementing sensory feedback systems to provide the user with sensory information during activities of daily living. This study evaluates the possibilities of using a microphone and electrotactile feedback to identify different textures. A condenser microphone was used as a sensor to detect the friction sound generated from the contact between different textures and the microphone. The generated signal was processed to provide a characteristic electrical stimulation presented to the participants. The main goal of the processing was to derive a continuous and intuitive transfer function between the microphone signal and stimulation... (More)
Most commercial prosthetic hands lack closed-loop feedback, thus, a lot of research has been focusing on implementing sensory feedback systems to provide the user with sensory information during activities of daily living. This study evaluates the possibilities of using a microphone and electrotactile feedback to identify different textures. A condenser microphone was used as a sensor to detect the friction sound generated from the contact between different textures and the microphone. The generated signal was processed to provide a characteristic electrical stimulation presented to the participants. The main goal of the processing was to derive a continuous and intuitive transfer function between the microphone signal and stimulation frequency. Twelve able-bodied volunteers participated in the study, in which they were asked to identify the stroked texture (among four used in this study: Felt, sponge, silicone rubber, and string mesh) using only electrotactile feedback. The experiments were done in three phases: 1) Training, 2) with-feedback, 3) without-feedback. Each texture was stroked 20 times each during all three phases. The results show that the participants were able to differentiate between different textures, with a median accuracy of 85%, by using only electrotactile feedback with the stimulation frequency being the only variable parameter.
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- author
- Svensson, Pamela LU ; Antfolk, Christian LU ; Björkman, Anders LU and Malešević, Nebojša LU
- organization
- publishing date
- 2021-05-02
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Electrotactile feedback, Feature extraction, Friction sound, Non-invasive stimulation, Texture sensor
- in
- Sensors
- volume
- 21
- issue
- 10
- article number
- 3384
- publisher
- MDPI AG
- external identifiers
-
- scopus:85105455790
- pmid:34066279
- ISSN
- 1424-8220
- DOI
- 10.3390/s21103384
- language
- English
- LU publication?
- yes
- id
- e1405c4c-d122-4501-8103-89701c9c78fd
- date added to LUP
- 2021-06-01 17:51:05
- date last changed
- 2024-06-15 11:57:16
@article{e1405c4c-d122-4501-8103-89701c9c78fd,
abstract = {{<p>Most commercial prosthetic hands lack closed-loop feedback, thus, a lot of research has been focusing on implementing sensory feedback systems to provide the user with sensory information during activities of daily living. This study evaluates the possibilities of using a microphone and electrotactile feedback to identify different textures. A condenser microphone was used as a sensor to detect the friction sound generated from the contact between different textures and the microphone. The generated signal was processed to provide a characteristic electrical stimulation presented to the participants. The main goal of the processing was to derive a continuous and intuitive transfer function between the microphone signal and stimulation frequency. Twelve able-bodied volunteers participated in the study, in which they were asked to identify the stroked texture (among four used in this study: Felt, sponge, silicone rubber, and string mesh) using only electrotactile feedback. The experiments were done in three phases: 1) Training, 2) with-feedback, 3) without-feedback. Each texture was stroked 20 times each during all three phases. The results show that the participants were able to differentiate between different textures, with a median accuracy of 85%, by using only electrotactile feedback with the stimulation frequency being the only variable parameter.</p>}},
author = {{Svensson, Pamela and Antfolk, Christian and Björkman, Anders and Malešević, Nebojša}},
issn = {{1424-8220}},
keywords = {{Electrotactile feedback; Feature extraction; Friction sound; Non-invasive stimulation; Texture sensor}},
language = {{eng}},
month = {{05}},
number = {{10}},
publisher = {{MDPI AG}},
series = {{Sensors}},
title = {{Electrotactile feedback for the discrimination of different surface textures using a microphone}},
url = {{http://dx.doi.org/10.3390/s21103384}},
doi = {{10.3390/s21103384}},
volume = {{21}},
year = {{2021}},
}