ArticleSensors (Basel, Switzerland)2021
Sensors and Artificial Intelligence Methods and Algorithms for Human-Computer Intelligent Interaction: A Systematic Mapping Study.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
11 citing papers in PubMed.
- Different pattern: a new EEG-based method for mental performance detection.BMC medical informatics and decision making · 2026Article
- Article
- Mapping Eye-Tracking Research in Human-Computer Interaction: A Science-Mapping and Content-Analysis Study.Journal of eye movement research · 2026Review
- Performance Measurement of Gesture-Based Human-Machine Interfaces Within eXtended Reality Head-Mounted Displays.Sensors (Basel, Switzerland) · 2025Article
- RETRACTED: Optimizing the impact of time domain segmentation techniques on upper limb EMG decoding using multimodal features.PloS one · 2025Article
- Exploring emotional intelligence in artificial intelligence systems: a comprehensive analysis of emotion recognition and response mechanisms.Annals of medicine and surgery (2012) · 2024Review
- End-User Assessment of an Innovative Clothing-Based Sensor Developed for Pressure Injury Prevention: A Mixed-Method Study.International journal of environmental research and public health · 2023Article
- The Application of Deep Learning for the Evaluation of User Interfaces.Sensors (Basel, Switzerland) · 2022Article
- Intelligent User Interfaces and Their Evaluation: A Systematic Mapping Study.Sensors (Basel, Switzerland) · 2022Article
- Validation of Continuous Monitoring System for Epileptic Users in Outpatient Settings.Sensors (Basel, Switzerland) · 2022Article
- Towards Cognitive Authentication for Smart Healthcare Applications.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.