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...

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Vydané v:	Sensors (Basel, Switzerland) Ročník 22; číslo 19; s. 7512
Hlavní autori:	Dankovich, Louis J., Vaughn-Cooke, Monifa, Bergbreiter, Sarah
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Switzerland MDPI AG 03.10.2022 MDPI
Predmet:	Accuracy Algorithms classification Data mining Deep learning Electromyography gesture recognition Gestures Hand Hands Humans inter-set testing Machine learning Methods Pattern Recognition, Automated - methods Sensors Time Factors Time series User behavior wearable sensors wearable sensors gesture recognition classification machine learning inter-set testing
ISSN:	1424-8220, 1424-8220
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Shrnutí:	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.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	1424-8220 1424-8220
DOI:	10.3390/s22197512