Impact of Physiological Signals Acquisition in the Emotional Support Provided in Learning Scenarios
Physiological sensors can be used to detect changes in the emotional state of users with affective computing. This has lately been applied in the educational domain, aimed to better support learners during the learning process. For this purpose, we have developed the AICARP (Ambient Intelligence Con...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-10-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/19/20/4520 |
| _version_ | 1811263174353092608 |
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| author | R. Uria-Rivas M. C. Rodriguez-Sanchez O. C. Santos J. Vaquero J. G. Boticario |
| author_facet | R. Uria-Rivas M. C. Rodriguez-Sanchez O. C. Santos J. Vaquero J. G. Boticario |
| author_sort | R. Uria-Rivas |
| collection | DOAJ |
| description | Physiological sensors can be used to detect changes in the emotional state of users with affective computing. This has lately been applied in the educational domain, aimed to better support learners during the learning process. For this purpose, we have developed the AICARP (Ambient Intelligence Context-aware Affective Recommender Platform) infrastructure, which detects changes in the emotional state of the user and provides personalized multisensorial support to help manage the emotional state by taking advantage of ambient intelligence features. We have developed a third version of this infrastructure, AICARP.V3, which addresses several problems detected in the data acquisition stage of the second version, (i.e., intrusion of the pulse sensor, poor resolution and low signal to noise ratio in the galvanic skin response sensor and slow response time of the temperature sensor) and extends the capabilities to integrate new actuators. This improved incorporates a new acquisition platform (shield) called PhyAS (Physiological Acquisition Shield), which reduces the number of control units to only one, and supports both gathering physiological signals with better precision and delivering multisensory feedback with more flexibility, by means of new actuators that can be added/discarded on top of just that single shield. The improvements in the quality of the acquired signals allow better recognition of the emotional states. Thereof, AICARP.V3 gives a more accurate personalized emotional support to the user, based on a rule-based approach that triggers multisensorial feedback, if necessary. This represents progress in solving an open problem: develop systems that perform as effectively as a human expert in a complex task such as the recognition of emotional states. |
| first_indexed | 2024-04-12T19:39:56Z |
| format | Article |
| id | doaj.art-b0ea6e2a316146ae9252872cf0b3654d |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-12T19:39:56Z |
| publishDate | 2019-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-b0ea6e2a316146ae9252872cf0b3654d2022-12-22T03:19:07ZengMDPI AGSensors1424-82202019-10-011920452010.3390/s19204520s19204520Impact of Physiological Signals Acquisition in the Emotional Support Provided in Learning ScenariosR. Uria-Rivas0M. C. Rodriguez-Sanchez1O. C. Santos2J. Vaquero3J. G. Boticario4aDeNu Research Group, Artificial Intelligence Department, Computer Science School, UNED, Calle Juan del Rosal, 16., 28040 Madrid, SpainElectronic Technology Department, Rey Juan Carlos University, c/Tulipan s/n, 28933 Mostoles, SpainaDeNu Research Group, Artificial Intelligence Department, Computer Science School, UNED, Calle Juan del Rosal, 16., 28040 Madrid, SpainElectronic Technology Department, Rey Juan Carlos University, c/Tulipan s/n, 28933 Mostoles, SpainaDeNu Research Group, Artificial Intelligence Department, Computer Science School, UNED, Calle Juan del Rosal, 16., 28040 Madrid, SpainPhysiological sensors can be used to detect changes in the emotional state of users with affective computing. This has lately been applied in the educational domain, aimed to better support learners during the learning process. For this purpose, we have developed the AICARP (Ambient Intelligence Context-aware Affective Recommender Platform) infrastructure, which detects changes in the emotional state of the user and provides personalized multisensorial support to help manage the emotional state by taking advantage of ambient intelligence features. We have developed a third version of this infrastructure, AICARP.V3, which addresses several problems detected in the data acquisition stage of the second version, (i.e., intrusion of the pulse sensor, poor resolution and low signal to noise ratio in the galvanic skin response sensor and slow response time of the temperature sensor) and extends the capabilities to integrate new actuators. This improved incorporates a new acquisition platform (shield) called PhyAS (Physiological Acquisition Shield), which reduces the number of control units to only one, and supports both gathering physiological signals with better precision and delivering multisensory feedback with more flexibility, by means of new actuators that can be added/discarded on top of just that single shield. The improvements in the quality of the acquired signals allow better recognition of the emotional states. Thereof, AICARP.V3 gives a more accurate personalized emotional support to the user, based on a rule-based approach that triggers multisensorial feedback, if necessary. This represents progress in solving an open problem: develop systems that perform as effectively as a human expert in a complex task such as the recognition of emotional states.https://www.mdpi.com/1424-8220/19/20/4520physiological sensorsaffective computingheart rategalvanic skin responseskin temperatureemotionsapplications and case studieslearning environmentsfeedbackopen hardware |
| spellingShingle | R. Uria-Rivas M. C. Rodriguez-Sanchez O. C. Santos J. Vaquero J. G. Boticario Impact of Physiological Signals Acquisition in the Emotional Support Provided in Learning Scenarios Sensors physiological sensors affective computing heart rate galvanic skin response skin temperature emotions applications and case studies learning environments feedback open hardware |
| title | Impact of Physiological Signals Acquisition in the Emotional Support Provided in Learning Scenarios |
| title_full | Impact of Physiological Signals Acquisition in the Emotional Support Provided in Learning Scenarios |
| title_fullStr | Impact of Physiological Signals Acquisition in the Emotional Support Provided in Learning Scenarios |
| title_full_unstemmed | Impact of Physiological Signals Acquisition in the Emotional Support Provided in Learning Scenarios |
| title_short | Impact of Physiological Signals Acquisition in the Emotional Support Provided in Learning Scenarios |
| title_sort | impact of physiological signals acquisition in the emotional support provided in learning scenarios |
| topic | physiological sensors affective computing heart rate galvanic skin response skin temperature emotions applications and case studies learning environments feedback open hardware |
| url | https://www.mdpi.com/1424-8220/19/20/4520 |
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