Editing Affective computing (section)

==Potential applications==
=== Education ===
Affection influences learners' learning state. Using affective computing technology, computers can judge the learners' affection and learning state by recognizing their facial expressions. In education, the teacher can use the analysis result to understand the student's learning and accepting ability, and then formulate reasonable teaching plans. At the same time, they can pay attention to students' inner feelings, which is helpful to students' psychological health. Especially in distance education, due to the separation of time and space, there is no emotional incentive between teachers and students for two-way communication. Without the atmosphere brought by traditional classroom learning, students are easily bored, and affect the learning effect. Applying affective computing in distance education system can effectively improve this situation.
<ref>{{Cite journal|url=http://www.learntechlib.org/p/173785/|title = Review of affective computing in education/Learning: Trends and challenges|journal = British Journal of Educational Technology|date = November 2016|volume = 47|issue = 6|pages = 1304–1323|last1 = Wu|first1 = Chih-Hung|last2 = Huang|first2 = Yueh-Min|last3 = Hwang|first3 = Jan-Pan|doi = 10.1111/bjet.12324}}</ref>

=== Transportation ===
The applications of sensory computing may contribute to improving road safety. For example, a car can monitor the emotion of all occupants and engage in additional safety measures, such as alerting other vehicles if it detects the driver to be angry.<ref>{{cite web |date=30 August 2018 |title=In-Car Facial Recognition Detects Angry Drivers To Prevent Road Rage |url=https://gizmodo.com/in-car-facial-recognition-detects-angry-drivers-to-prev-1543709793 |website=Gizmodo}}</ref> In addition, affective computing systems for monitoring the driver's stress may allow various interventions such as driver assistance systems adjusted according to the stress level<ref>{{Cite journal |last1=Collet |first1=Christian |last2=Musicant |first2=Oren |date=2019-04-24 |title=Associating Vehicles Automation With Drivers Functional State Assessment Systems: A Challenge for Road Safety in the Future |journal=Frontiers in Human Neuroscience |volume=13 |page=131 |doi=10.3389/fnhum.2019.00131 |issn=1662-5161 |pmc=6503868 |pmid=31114489 |doi-access=free }}</ref> and minimal and direct interventions to change the emotional state of the driver.<ref>{{Cite book |last1=Balters |first1=Stephanie |last2=Bernstein |first2=Madeline |last3=Paredes |first3=Pablo E. |chapter=On-road Stress Analysis for In-car Interventions During the Commute |date=2019-05-02 |title=Extended Abstracts of the 2019 CHI Conference on Human Factors in Computing Systems |chapter-url=https://dl.acm.org/doi/10.1145/3290607.3312824 |language=en |publisher=ACM |pages=1–6 |doi=10.1145/3290607.3312824 |isbn=978-1-4503-5971-9|s2cid=144207824 }}</ref>

=== Healthcare ===
[[Social robot]]s, as well as a growing number of robots used in health care benefit from emotional awareness because they can better judge users' and patient's emotional states and alter their actions/programming appropriately. This is especially important in those countries with growing aging populations and/or a lack of younger workers to address their needs.<ref>{{Cite book|title=Heart of the Machine: Our Future in a World of Artificial Emotional Intelligence|last=Yonck|first=Richard|publisher=Arcade Publishing|year=2017|isbn=9781628727333|location=New York|pages=150–153|oclc=956349457}}</ref>

Affective computing is also being applied to the development of communicative technologies for use by people with autism.<ref>[http://affect.media.mit.edu/projects.php Projects in Affective Computing]</ref> The affective component of a text is also increasingly gaining attention, particularly its role in the so-called emotional or [[emotive Internet]].<ref>Shanahan, James; Qu, Yan; [[Janyce Wiebe|Wiebe, Janyce]] (2006). ''Computing Attitude and Affect in Text: Theory and Applications''. Dordrecht: Springer Science & Business Media. p. 94. {{ISBN|1402040261}}</ref>

=== Video games ===

Affective video games can access their players' emotional states through [[biofeedback]] devices.<ref>{{cite conference |title=Affective Videogames and Modes of Affective Gaming: Assist Me, Challenge Me, Emote Me |first1=Kiel Mark |last1=Gilleade |first2=Alan |last2=Dix |first3=Jen |last3=Allanson |year=2005 |conference=Proc. [[Digital Games Research Association|DiGRA]] Conf. |url=http://comp.eprints.lancs.ac.uk/1057/1/Gilleade_Affective_Gaming_DIGRA_2005.pdf |access-date=2016-12-10 |archive-url=https://web.archive.org/web/20150406200454/http://comp.eprints.lancs.ac.uk/1057/1/Gilleade_Affective_Gaming_DIGRA_2005.pdf |archive-date=2015-04-06 |url-status=dead }}</ref> A particularly simple form of biofeedback is available through [[gamepad]]s that measure the pressure with which a button is pressed: this has been shown to correlate strongly with the players' level of [[arousal]];<ref>{{Cite conference| doi = 10.1145/765891.765957| title = Affective gaming: Measuring emotion through the gamepad| conference = CHI '03 Extended Abstracts on Human Factors in Computing Systems| year = 2003| last1 = Sykes | first1 = Jonathan| last2 = Brown | first2 = Simon| isbn = 1581136374| citeseerx = 10.1.1.92.2123}}</ref> at the other end of the scale are [[brain–computer interface]]s.<ref>{{Cite journal | doi = 10.1016/j.entcom.2009.09.007| title = Turning shortcomings into challenges: Brain–computer interfaces for games| journal = Entertainment Computing| volume = 1| issue = 2| pages = 85–94| year = 2009| last1 = Nijholt | first1 = Anton| last2 = Plass-Oude Bos | first2 = Danny| last3 = Reuderink | first3 = Boris| bibcode = 2009itie.conf..153N| url = http://wwwhome.cs.utwente.nl/~anijholt/artikelen/intetain_bci_2009.pdf}}</ref><ref>{{Cite conference| doi = 10.1007/978-3-642-02315-6_23| title = Affective Pacman: A Frustrating Game for Brain–Computer Interface Experiments| conference = Intelligent Technologies for Interactive Entertainment (INTETAIN)| pages = 221–227| year = 2009| last1 = Reuderink | first1 = Boris| last2 = Nijholt | first2 = Anton| last3 = Poel | first3 = Mannes| isbn = 978-3-642-02314-9}}</ref> Affective games have been used in medical research to support the emotional development of [[autism|autistic]] children.<ref>{{Cite journal
 | pmid = 19592726
| year = 2009
| last1 = Khandaker
| first1 = M
| title = Designing affective video games to support the social-emotional development of teenagers with autism spectrum disorders
| journal = Studies in Health Technology and Informatics
| volume = 144
| pages = 37–9
}}</ref>

=== Psychomotor training ===
Training methods of [[Psychomotor learning|psychomotor]] operations such as steering and maneuvering are used in various fields such as aviation, transportation and medicine. Integrating affective computing capabilities in this type of training systems, in accordance with the adaptive automation approach, has been found to be effective in improving the quality of training and shortening the required training duration.<ref>{{Cite journal |last1=Sahar |first1=Yotam |last2=Wagner |first2=Michael |last3=Barel |first3=Ariel |last4=Shoval |first4=Shraga |date=2022-11-01 |title=Stress-Adaptive Training: An Adaptive Psychomotor Training According to Stress Measured by Grip Force |journal=Sensors |language=en |volume=22 |issue=21 |pages=8368 |doi=10.3390/s22218368 |issn=1424-8220 |pmc=9654132 |pmid=36366066 |bibcode=2022Senso..22.8368S |doi-access=free }}</ref>

=== Other applications ===

Affective computing has potential applications in [[human computer interaction|human–computer interaction]], such as affective mirrors allowing the user to see how he or she performs; emotion monitoring agents sending a warning before one sends an angry email; or even music players selecting tracks based on mood.<ref>{{cite journal|last1=Janssen|first1=Joris H.|last2=van den Broek|first2=Egon L.|date=July 2012|title=Tune in to Your Emotions: A Robust Personalized Affective Music Player|journal=User Modeling and User-Adapted Interaction|volume=22|issue=3|pages=255–279|doi=10.1007/s11257-011-9107-7|doi-access=free|hdl=2066/103051|hdl-access=free}}</ref>

One idea put forth by the Romanian researcher Dr. Nicu Sebe in an interview is the analysis of a person's face while they are using a certain product (he mentioned ice cream as an example).<ref>{{cite web|url=https://www.sciencedaily.com/videos/2006/0811-mona_lisa_smiling.htm|title=Mona Lisa: Smiling? Computer Scientists Develop Software That Evaluates Facial Expressions|date=1 August 2006|website=ScienceDaily|archive-url=https://web.archive.org/web/20071019235625/http://sciencedaily.com/videos/2006/0811-mona_lisa_smiling.htm|archive-date=19 October 2007|url-status=dead}}</ref> Companies would then be able to use such analysis to infer whether their product will or will not be well received by the respective market.

One could also use affective state recognition in order to judge the impact of a TV advertisement through a real-time video recording of that person and through the subsequent study of his or her facial expression. Averaging the results obtained on a large group of subjects, one can tell whether that commercial (or movie) has the desired effect and what the elements which interest the watcher most are.