Sensors and Artificial Intelligence Methods and Algorithms for Human–Computer Intelligent Interaction: A Systematic Mapping Study

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.

A Typological Examination of Modern Chinese OV Word Order Based on Deep Learning and the Design of Stance Expression Methods

“…The main difference between the two is that position tendency is generated for a certain descriptive subject, while sentiment analysis focuses on positive or negative emotional features in the text, such as whether it contains words with strong emotional messages and does not involve the descriptive subject as information. Currently, researchers have proposed many proven research methods in the field of stance representation, the vast majority of which use a combination of feature engineering and traditional machine learning [18,19].…”

Intelligent User Interfaces and Their Evaluation: A Systematic Mapping Study

“…Classification variable domain was used to describe the article’s primary application domain. Values for applicable domains were primarily gathered from related work [ 5 , 6 , 9 ] and expanded in the pilot study. If the study used any IUI definitions, they were extracted to EC12 verbatim, together with the source used in the definition, if the source was mentioned.…”

“…Additionally, ref. [ 9 ] included intelligent interaction, whereas [ 29 ] expanded the review to transparency, explainability, and context interpretation. One of the secondary studies [ 8 ] focused only on adaptive and adaptable user interfaces.…”

“…Most of the evaluations were quantitative, and the most commonly evaluated aspects of usability and UX were user satisfaction, effectiveness, efficiency, and performance. In a recent and related systematic mapping study [ 9 ], the focus was widely placed on 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. The primary focus in HCII studies has been intelligent recognition of emotion, gestures, and facial expressions using sensors technology, such as the camera, EEG, Kinect, wearable sensors, eye tracker, and gyroscope.…”

“…Additional insight is provided into current trends in the evaluation of intelligent interfaces. In existing IUI-related research, systematic literature reviews and systematic mapping studies have investigated, for example, smart, context-sensitive, and multimodal user interfaces [ 5 ], adaptive user interfaces [ 5 , 6 , 7 , 8 ], intelligent human–computer interaction [ 9 ], and adaptive and adaptable user interfaces [ 8 ]. Because the focus of our work is on IUI solutions, our study can be considered close to the work performed by Volkel et al [ 10 ].…”