| DOI | Resolve DOI: https://doi.org/10.1145/3434074.3447193 |
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| Author | Search for: Bartlett, Madeleine E.; Search for: Stewart, Terrence C.1; Search for: Thill, Serge |
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| Affiliation | - National Research Council of Canada. Digital Technologies
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| Format | Text, Article |
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| Conference | HRI '21: ACM/IEEE International Conference on Human-Robot Interaction, March 8-11, 2021, Boulder CO USA |
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| Subject | task engagement; intensity; legendre memory units; MLP; activity recognition |
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| Abstract | In this study, we examine whether the data requirements associated with training a system to recognize multiple 'levels' of an internal state can be reduced by training systems on the 'extremes' in a way that allows them to estimate "intermediate" classes as falling in-between the trained extremes. Specifically, this study explores whether a novel recurrent neural network, the Legendre Delay Network, added as a pre-processing step to a Multi-Layer Perception, produces an output which can be used to separate an untrained intermediate class of task engagement from the trained extreme classes. The results showed that identifying untrained classes after training on the extremes is feasible, particularly when using the Legendre Delay Network. |
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| Publication date | 2021-03-08 |
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| Publisher | ACM |
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| In | |
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| Language | English |
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| Peer reviewed | Yes |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | 46482e71-f504-4bdf-bb24-d29f65c2c1ad |
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| Record created | 2021-07-28 |
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| Record modified | 2021-07-28 |
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