Cargando…

Can You Do That Again? Time Series Consolidation as a Robust Method of Tailoring Gesture Recognition to Individual Users

Robust inter-session modeling of gestures is still an open learning challenge. A sleeve equipped with capacitive strap sensors was used to capture two gesture data sets from a convenience sample of eight subjects. Two pipelines were explored. In FILT a novel two-stage algorithm was introduced which...

Descripción completa

Detalles Bibliográficos
Autores principales: Dankovich, Louis J., Vaughn-Cooke, Monifa, Bergbreiter, Sarah
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9572399/
https://www.ncbi.nlm.nih.gov/pubmed/36236611
http://dx.doi.org/10.3390/s22197512
_version_ 1784810604265996288
author Dankovich, Louis J.
Vaughn-Cooke, Monifa
Bergbreiter, Sarah
author_facet Dankovich, Louis J.
Vaughn-Cooke, Monifa
Bergbreiter, Sarah
author_sort Dankovich, Louis J.
collection PubMed
description Robust inter-session modeling of gestures is still an open learning challenge. A sleeve equipped with capacitive strap sensors was used to capture two gesture data sets from a convenience sample of eight subjects. Two pipelines were explored. In FILT a novel two-stage algorithm was introduced which uses an unsupervised learning algorithm to find samples representing gesture transitions and discards them prior to training and validating conventional models. In TSC a confusion matrix was used to automatically consolidate commonly confused class labels, resulting in a set of gestures tailored to an individual subject’s abilities. The inter-session testing accuracy using the Time Series Consolidation (TSC) method increased from a baseline inter-session average of 42.47 ± 3.83% to 93.02% ± 4.97% while retaining an average of 5.29 ± 0.46 out of the 11 possible gesture categories. These pipelines used classic machine learning algorithms which require relatively small amounts of data and computational power compared to deep learning solutions. These methods may also offer more flexibility in interface design for users suffering from handicaps limiting their manual dexterity or ability to reliably make gestures, and be possible to implement on edge devices with low computational power.
format Online
Article
Text
id pubmed-9572399
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-95723992022-10-17 Can You Do That Again? Time Series Consolidation as a Robust Method of Tailoring Gesture Recognition to Individual Users Dankovich, Louis J. Vaughn-Cooke, Monifa Bergbreiter, Sarah Sensors (Basel) Article Robust inter-session modeling of gestures is still an open learning challenge. A sleeve equipped with capacitive strap sensors was used to capture two gesture data sets from a convenience sample of eight subjects. Two pipelines were explored. In FILT a novel two-stage algorithm was introduced which uses an unsupervised learning algorithm to find samples representing gesture transitions and discards them prior to training and validating conventional models. In TSC a confusion matrix was used to automatically consolidate commonly confused class labels, resulting in a set of gestures tailored to an individual subject’s abilities. The inter-session testing accuracy using the Time Series Consolidation (TSC) method increased from a baseline inter-session average of 42.47 ± 3.83% to 93.02% ± 4.97% while retaining an average of 5.29 ± 0.46 out of the 11 possible gesture categories. These pipelines used classic machine learning algorithms which require relatively small amounts of data and computational power compared to deep learning solutions. These methods may also offer more flexibility in interface design for users suffering from handicaps limiting their manual dexterity or ability to reliably make gestures, and be possible to implement on edge devices with low computational power. MDPI 2022-10-03 /pmc/articles/PMC9572399/ /pubmed/36236611 http://dx.doi.org/10.3390/s22197512 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Dankovich, Louis J.
Vaughn-Cooke, Monifa
Bergbreiter, Sarah
Can You Do That Again? Time Series Consolidation as a Robust Method of Tailoring Gesture Recognition to Individual Users
title Can You Do That Again? Time Series Consolidation as a Robust Method of Tailoring Gesture Recognition to Individual Users
title_full Can You Do That Again? Time Series Consolidation as a Robust Method of Tailoring Gesture Recognition to Individual Users
title_fullStr Can You Do That Again? Time Series Consolidation as a Robust Method of Tailoring Gesture Recognition to Individual Users
title_full_unstemmed Can You Do That Again? Time Series Consolidation as a Robust Method of Tailoring Gesture Recognition to Individual Users
title_short Can You Do That Again? Time Series Consolidation as a Robust Method of Tailoring Gesture Recognition to Individual Users
title_sort can you do that again? time series consolidation as a robust method of tailoring gesture recognition to individual users
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9572399/
https://www.ncbi.nlm.nih.gov/pubmed/36236611
http://dx.doi.org/10.3390/s22197512
work_keys_str_mv AT dankovichlouisj canyoudothatagaintimeseriesconsolidationasarobustmethodoftailoringgesturerecognitiontoindividualusers
AT vaughncookemonifa canyoudothatagaintimeseriesconsolidationasarobustmethodoftailoringgesturerecognitiontoindividualusers
AT bergbreitersarah canyoudothatagaintimeseriesconsolidationasarobustmethodoftailoringgesturerecognitiontoindividualusers