| Title: | Sensors and artificial intelligence methods and algorithms for human - computer intelligent interaction: a systematic mapping study |
|---|
| Authors: | ID Šumak, Boštjan (Author)
ID Brdnik, Saša (Author)
ID Pušnik, Maja (Author) |
|---|
| Files: |  sensors-22-00020.pdf (8,70 MB)
MD5: 703B50058F1E7BC8238D9153A460CEEA
 https://www.mdpi.com/1424-8220/22/1/20
|
|---|
| Language: | English |
|---|
| Work type: | Article |
|---|
| Typology: | 1.01 - Original Scientific Article |
|---|
| Organization: | FERI - Faculty of Electrical Engineering and Computer Science
|
|---|
| Abstract: | To equip computers with human communication skills and to enable natural interaction
between the computer and a human, intelligent solutions are required based on artificial intelligence
(AI) methods, algorithms, and sensor technology. This study aimed at identifying and analyzing
the state-of-the-art AI methods and algorithms and sensors technology in existing human–computer
intelligent interaction (HCII) research to explore trends in HCII research, categorize existing evidence,
and identify potential directions for future research. We conduct a systematic mapping study of the
HCII body of research. Four hundred fifty-four studies published in various journals and conferences
between 2010 and 2021 were identified and analyzed. Studies in the HCII and IUI fields have
primarily been focused on intelligent recognition of emotion, gestures, and facial expressions using
sensors technology, such as the camera, EEG, Kinect, wearable sensors, eye tracker, gyroscope, and
others. Researchers most often apply deep-learning and instance-based AI methods and algorithms.
The support sector machine (SVM) is the most widely used algorithm for various kinds of recognition,
primarily an emotion, facial expression, and gesture. The convolutional neural network (CNN)
is the often-used deep-learning algorithm for emotion recognition, facial recognition, and gesture
recognition solutions. |
|---|
| Keywords: | human–computer intelligent interaction, intelligent user interfaces, IUI, sensors, artificial intelligence |
|---|
| Publication status: | Published |
|---|
| Publication version: | Version of Record |
|---|
| Submitted for review: | 15.11.2021 |
|---|
| Article acceptance date: | 18.12.2021 |
|---|
| Publication date: | 21.12.2022 |
|---|
| Publisher: | MDPI AG |
|---|
| Year of publishing: | 2022 |
|---|
| Number of pages: | 40 str. |
|---|
| Numbering: | Vol. 22, iss. 1 |
|---|
| PID: | 20.500.12556/DKUM-92332  |
|---|
| UDC: | 004.8 |
|---|
| ISSN on article: | 1424-8220 |
|---|
| COBISS.SI-ID: | 91775747 |
|---|
| DOI: | 10.3390/s22010020 |
|---|
| Copyright: | © 2021 by the authors |
|---|
| Publication date in DKUM: | 31.03.2025 |
|---|
| Views: | 166 |
|---|
| Downloads: | 12 |
|---|
| Metadata: |    |
|---|
| Categories: | Misc.
|
|---|
|
:
|
Copy citation |
|---|
| | | | Average score: | (0 votes) |
|---|
| Your score: | Voting is allowed only for logged in users. |
|---|
| Share: |  |
|---|
Hover the mouse pointer over a document title to show the abstract or click
on the title to get all document metadata. |
