Abstract
This paper explores the theoretical aspect of providing context to contextually impaired individuals and offers some considerations on how a machine learning system can be adapted to learn contextual clues and then provide these to users. Context itself, is the clarifying component of a situation and helps people to understand what is happening and why. Some people struggle to understand the situations that they are in, due to several issues, particularly those with sensory or social impairments, such as autism. These impairments can be interpreted as form of contextual blindness, where a lack of awareness of certain contextual clues render communication or understanding of it difficult. These clues can be as simple as a person’s current location, to the more nuanced impact of a conversational partner’s body language. Since awareness of the current context of a situation can help to clarify its meaning, it is expected that provision of contextual information to contextually impaired individuals will be beneficial; by giving them a point of reference for the tone of the situation they are in and supporting them to react accordingly. The results presented demonstrate the impact of contextual information in a specific social situation (dating), giving an indication of the benefits of both context and contextual awareness, which provide the basis for further investigation.
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Kinch, M., Keates, S. (2021). Contextual Cues: The Role of Machine Learning in Supporting Contextually Impaired Users. In: Antona, M., Stephanidis, C. (eds) Universal Access in Human-Computer Interaction. Design Methods and User Experience. HCII 2021. Lecture Notes in Computer Science(), vol 12768. Springer, Cham. https://doi.org/10.1007/978-3-030-78092-0_39
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DOI: https://doi.org/10.1007/978-3-030-78092-0_39
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