Expressure: Detect Expressions Related to Emotional and Cognitive Activities Using Forehead Textile Pressure Mechanomyography
We investigate how pressure-sensitive smart textiles, in the form of a headband, can detect changes in facial expressions that are indicative of emotions and cognitive activities. Specifically, we present the Expressure system that performs surface pressure mechanomyography on the forehead using an...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/3/730 |
| _version_ | 1798005038053851136 |
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| author | Bo Zhou Tandra Ghose Paul Lukowicz |
| author_facet | Bo Zhou Tandra Ghose Paul Lukowicz |
| author_sort | Bo Zhou |
| collection | DOAJ |
| description | We investigate how pressure-sensitive smart textiles, in the form of a headband, can detect changes in facial expressions that are indicative of emotions and cognitive activities. Specifically, we present the Expressure system that performs surface pressure mechanomyography on the forehead using an array of textile pressure sensors that is not dependent on specific placement or attachment to the skin. Our approach is evaluated in systematic psychological experiments. First, through a mimicking expression experiment with 20 participants, we demonstrate the system’s ability to detect well-defined facial expressions. We achieved accuracies of 0.824 to classify among three eyebrow movements (0.333 chance-level) and 0.381 among seven full-face expressions (0.143 chance-level). A second experiment was conducted with 20 participants to induce cognitive loads with N-back tasks. Statistical analysis has shown significant correlations between the Expressure features on a fine time granularity and the cognitive activity. The results have also shown significant correlations between the Expressure features and the N-back score. From the 10 most facially expressive participants, our approach can predict whether the N-back score is above or below the average with 0.767 accuracy. |
| first_indexed | 2024-04-11T12:32:54Z |
| format | Article |
| id | doaj.art-f5286b05be70479a8745c6a0b77e250c |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T12:32:54Z |
| publishDate | 2020-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-f5286b05be70479a8745c6a0b77e250c2022-12-22T04:23:42ZengMDPI AGSensors1424-82202020-01-0120373010.3390/s20030730s20030730Expressure: Detect Expressions Related to Emotional and Cognitive Activities Using Forehead Textile Pressure MechanomyographyBo Zhou0Tandra Ghose1Paul Lukowicz2German Research Center for Artificial Intelligence (DFKI), 67663 Kaiserslautern, GermanyDepartment of Psychology, University of Kaiserslautern, 67663 Kaiserslautern, GermanyGerman Research Center for Artificial Intelligence (DFKI), 67663 Kaiserslautern, GermanyWe investigate how pressure-sensitive smart textiles, in the form of a headband, can detect changes in facial expressions that are indicative of emotions and cognitive activities. Specifically, we present the Expressure system that performs surface pressure mechanomyography on the forehead using an array of textile pressure sensors that is not dependent on specific placement or attachment to the skin. Our approach is evaluated in systematic psychological experiments. First, through a mimicking expression experiment with 20 participants, we demonstrate the system’s ability to detect well-defined facial expressions. We achieved accuracies of 0.824 to classify among three eyebrow movements (0.333 chance-level) and 0.381 among seven full-face expressions (0.143 chance-level). A second experiment was conducted with 20 participants to induce cognitive loads with N-back tasks. Statistical analysis has shown significant correlations between the Expressure features on a fine time granularity and the cognitive activity. The results have also shown significant correlations between the Expressure features and the N-back score. From the 10 most facially expressive participants, our approach can predict whether the N-back score is above or below the average with 0.767 accuracy.https://www.mdpi.com/1424-8220/20/3/730affective computingsmart textilespressure mechanomyographyfacial expressioncognitive loademotion recognition |
| spellingShingle | Bo Zhou Tandra Ghose Paul Lukowicz Expressure: Detect Expressions Related to Emotional and Cognitive Activities Using Forehead Textile Pressure Mechanomyography Sensors affective computing smart textiles pressure mechanomyography facial expression cognitive load emotion recognition |
| title | Expressure: Detect Expressions Related to Emotional and Cognitive Activities Using Forehead Textile Pressure Mechanomyography |
| title_full | Expressure: Detect Expressions Related to Emotional and Cognitive Activities Using Forehead Textile Pressure Mechanomyography |
| title_fullStr | Expressure: Detect Expressions Related to Emotional and Cognitive Activities Using Forehead Textile Pressure Mechanomyography |
| title_full_unstemmed | Expressure: Detect Expressions Related to Emotional and Cognitive Activities Using Forehead Textile Pressure Mechanomyography |
| title_short | Expressure: Detect Expressions Related to Emotional and Cognitive Activities Using Forehead Textile Pressure Mechanomyography |
| title_sort | expressure detect expressions related to emotional and cognitive activities using forehead textile pressure mechanomyography |
| topic | affective computing smart textiles pressure mechanomyography facial expression cognitive load emotion recognition |
| url | https://www.mdpi.com/1424-8220/20/3/730 |
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