- Title
- A Novel Method of Exploring the Uncanny Valley in Avatar Gender(Sex) and Realism Using Electromyography
- Creator
- Bailey, Jacqueline D.; Blackmore, Karen L.
- Relation
- Big Data and Cognitive Computing Vol. 6, Issue 2, no. 61
- Publisher Link
- http://dx.doi.org/10.3390/bdcc6020061
- Publisher
- MDPI AG
- Resource Type
- journal article
- Date
- 2022
- Description
- Despite the variety of applications that use avatars (virtual humans), how end-users perceive avatars are not fully understood, and accurately measuring these perceptions remains a challenge. To measure end-user responses more accurately to avatars, this pilot study uses a novel methodology which aims to examine and categorize end-user facial electromyography (f-EMG) responses. These responses (n = 92) can be categorized as pleasant, unpleasant, and neutral using control images sourced from the International Affective Picture System (IAPS). This methodology can also account for variability between participant responses to avatars. The novel methodology taken here can assist in the comparisons of avatars, such as gender(sex)-based differences. To examine these gender(sex) differences, participant responses to an avatar can be categorized as either pleasant, unpleasant, neutral or a combination. Although other factors such as age may unconsciously affect the participant responses, age was not directly considered in this work. This method may allow avatar developers to better understand how end-users objectively perceive an avatar. The recommendation of this methodology is to aim for an avatar that returns a pleasant, neutral, or pleasant-neutral response, unless an unpleasant response is the intended. This methodology demonstrates a novel and useful way forward to address some of the known variability issues found in f-EMG responses, and responses to avatar realism and uncanniness that can be used to examine gender(sex) perceptions.
- Subject
- avatar; electromyography; realism; international affective picture system (IAPS); gender(sex)
- Identifier
- http://hdl.handle.net/1959.13/1479964
- Identifier
- uon:50421
- Identifier
- ISSN:2504-2289
- Rights
- © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
- Language
- eng
- Full Text
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