Publicação
An approach to behavioural distraction patterns detection and classification in a human-robot interaction
| Resumo: | The capacity of remaining focused on a task can be crucial in some circumstances. In general, this ability is intrinsic in a human social interaction and it is naturally used in any social context. Nevertheless, some individuals have difficulties in remaining concentrated in an activity, resulting in a short attention span. In order to recognize human distraction behaviours and capture the user attention, several patterns of distraction, as well as systems to automatically detect them, have been developed. One of the most used distraction patterns detection methods is based on the measurement of the head pose and eye gaze. The present work proposes a system based on a RGB camera, capable of detecting the distraction patterns, head pose, eye gaze, blinks frequency, and the distance of the user to the camera, during an activity, and then classify the user's state using a machine learning algorithm. The goal is to interface this system with a humanoid robot to consequently adapt its behaviour taking into account the individual affective state during an emotion imitation activity. |
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| Autores principais: | Amaro, Bruno Filipe Viana |
| Outros Autores: | Silva, Vinicius Corrêa Alves; Soares, Filomena; Esteves, João Sena |
| Assunto: | Human-Robot Interaction ZECA Robot Distraction Patterns Emotional States Machine Learning |
| Ano: | 2018 |
| País: | Portugal |
| Tipo de documento: | comunicação em conferência |
| Tipo de acesso: | acesso restrito |
| Instituição associada: | Universidade do Minho |
| Idioma: | inglês |
| Origem: | RepositóriUM - Universidade do Minho |
| Resumo: | The capacity of remaining focused on a task can be crucial in some circumstances. In general, this ability is intrinsic in a human social interaction and it is naturally used in any social context. Nevertheless, some individuals have difficulties in remaining concentrated in an activity, resulting in a short attention span. In order to recognize human distraction behaviours and capture the user attention, several patterns of distraction, as well as systems to automatically detect them, have been developed. One of the most used distraction patterns detection methods is based on the measurement of the head pose and eye gaze. The present work proposes a system based on a RGB camera, capable of detecting the distraction patterns, head pose, eye gaze, blinks frequency, and the distance of the user to the camera, during an activity, and then classify the user's state using a machine learning algorithm. The goal is to interface this system with a humanoid robot to consequently adapt its behaviour taking into account the individual affective state during an emotion imitation activity. |
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