The Voight-Kampff Machine for Automatic Custom Gesture Rejection Threshold Selection

Taranta, Eugene M.; Maslych, Mykola; Ghamandi, Ryan; LaViola, Joseph J.

doi:10.1145/3491102.3502000

Cited by 2 publications

(1 citation statement)

References 37 publications

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“…They used algorithms to assign confidence that a gesture belonged in a given class, and reject classifications which fell below the assigned confidence and achieved a 93.3% overall accuracy in picking 10 dynamic gestures out of a stream of data. They reported that the thresholds of confidence had to be tailored to a given user and gesture [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

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

Dankovich

Vaughn-Cooke

Bergbreiter

2022

Sensors

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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.

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Section: Introductionmentioning

confidence: 99%

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

Dankovich

Vaughn-Cooke

Bergbreiter

2022

Sensors

View full text Add to dashboard Cite

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Transforming Hand Gesture Recognition Into Image Classification Using Data Level Fusion

Yusuf,

Habib,

Moustafa

2023

Global Perspectives on Robotics and Autonomous Systems

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Hand gesture recognition (HGR) is a form of perceptual computing with applications in human-machine interaction, virtual/augmented reality, and human behavior analysis. Within the HGR domain, several frameworks have been developed with different combinations of input modalities and neural network architectures to varying levels of efficacy. Such frameworks maximized performance at the expense of increased computational and hardware requirements. These drawbacks can be mitigated by a skeleton-based framework that transforms the hand gesture recognition task into an image classification task. This chapter explores several temporal information condensation (via data-level fusion) methods for encoding dynamic gesture information into static images. The efficacies of these methods are compared, and the best ones are aggregated into a generalized HGR framework which was extensively evaluated on the CNR, FPHA, LMDHG, SHREC2017, and DHG1428 benchmark datasets. The framework's performance shows competitiveness compared to other frameworks within the state-of-the-art for the datasets.

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The Voight-Kampff Machine for Automatic Custom Gesture Rejection Threshold Selection

Cited by 2 publications

References 37 publications

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

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

Transforming Hand Gesture Recognition Into Image Classification Using Data Level Fusion

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