Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.3
2.5
Journals
Applied Sciences
Special Issues
Advances in HCI: Recognition Technologies and Their Applications
applsci-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Applied Sciences Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Advances in HCI: Recognition Technologies and Their Applications

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 April 2025 | Viewed by 5412

Special Issue Editor

Prof. Dr. Hongmei He

E-Mail Website
Guest Editor
School of Science, Engineering & Environment, University of Salford, Manchester M5 4WT, UK
Interests: artificial intelligence; machine learning; linguistic decision making; transparency and interpretability of AI; trustworthy robotics and autonomous systems with respect to safety; security and human–robot interactions; sensor fusion; IoT ecosystems; Internet of Robotic Things; optimisation; evolutionary computing

Special Issue Information

Dear Colleagues,

Trusted human–computer interactions (HCIs) play a crucial role in all artificial intelligence (AI) systems and bring numerous benefits to fields such as security and surveillance, healthcare, automotives and transport, smart homes, education, the IoT, etc. A trusted HCI is a challenge for human-centric AI ecosystems and requires the harmonious collaboration of many interdisciplinary fields. A critical challenge in implementing trusted HCIs lies in the recognition capabilities of machines and ensuring that machines perform the right physical actions, which should be both legible to and in coordination with humans. Recognition technologies that use AI and machine learning help solve various problems in computer vision, natural language processing, gesture recognition, etc., and open up new opportunities across various fields. HCI researchers strive to leverage advanced recognition technologies to create user-friendly systems that align with our lives and work environments.

Prof. Dr. Hongmei He
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • facial recognition
  • voice recognition
  • object recognition
  • text recognition
  • gesture recognition
  • emotion recognition
  • biometric recognition
  • pattern recognition
  • behaviour recognition
  • speech recognition
  • license plate recognition
  • brand recognition
  • character recognition
  • medical image recognition

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

17 pages, 1435 KiB  
Article
Causal Inference for Modality Debiasing in Multimodal Emotion Recognition
by Juyeon Kim, Juyoung Hong and Yukyung Choi
Appl. Sci. 2024, 14(23), 11397; https://doi.org/10.3390/app142311397 - 6 Dec 2024
Viewed by 918
Abstract
Multimodal emotion recognition (MER) aims to enhance the understanding of human emotions by integrating visual, auditory, and textual modalities. However, previous MER approaches often depend on a dominant modality rather than considering all modalities, leading to poor generalization. To address this, we propose [...] Read more.
Multimodal emotion recognition (MER) aims to enhance the understanding of human emotions by integrating visual, auditory, and textual modalities. However, previous MER approaches often depend on a dominant modality rather than considering all modalities, leading to poor generalization. To address this, we propose Causal Inference in Multimodal Emotion Recognition (CausalMER), which leverages counterfactual reasoning and causal graphs to capture relationships between modalities and reduce direct modality effects contributing to bias. This allows CausalMER to make unbiased predictions while being easily applied to existing MER methods in a model-agnostic manner, without requiring any architectural modifications. We evaluate CausalMER on the IEMOCAP and CMU-MOSEI datasets, widely used benchmarks in MER, and compare it with existing methods. On the IEMOCAP dataset with the MulT backbone, CausalMER achieves an average accuracy of 83.4%. On the CMU-MOSEI dataset, the average accuracies with MulT, PMR, and DMD backbones are 50.1%, 48.8%, and 48.8%, respectively. Experimental results demonstrate that CausalMER is robust in missing modality scenarios, as shown by its low standard deviation in performance drop gaps. Additionally, we evaluate modality contributions and show that CausalMER achieves balanced contributions from each modality, effectively mitigating direct biases from individual modalities. Full article
(This article belongs to the Special Issue Advances in HCI: Recognition Technologies and Their Applications)
►▼ Show Figures

Figure 1

30 pages, 6759 KiB  
Article
A Sensor-Fusion-Based Experimental Apparatus for Collecting Touchscreen Handwriting Biometric Features
by Alen Salkanovic, David Bačnar, Diego Sušanj and Sandi Ljubic
Appl. Sci. 2024, 14(23), 11234; https://doi.org/10.3390/app142311234 - 2 Dec 2024
Viewed by 759
Abstract
Using biometric data for user authentication is a frequently addressed subject within the context of computer security. Despite significant advancements in technology, handwriting analysis continues to be the most common method of identifying individuals. There are two distinct types of handwriting recognition: offline [...] Read more.
Using biometric data for user authentication is a frequently addressed subject within the context of computer security. Despite significant advancements in technology, handwriting analysis continues to be the most common method of identifying individuals. There are two distinct types of handwriting recognition: offline and online. The first type involves the identification and interpretation of handwritten content obtained from an image, such as digitized human handwriting. The latter pertains to the identification of handwriting derived from digital writing performed on a touchpad or touchscreen. This research paper provides a comprehensive overview of the proposed apparatus specifically developed for collecting handwritten data. The acquisition of biometric information is conducted using a touchscreen device equipped with a variety of integrated and external sensors. In addition to acquiring signatures, the sensor-fusion-based configuration accumulates handwritten phrases, words, and individual letters to facilitate online user authentication. The proposed system can collect an extensive array of data. Specifically, it is possible to capture data related to stylus pressure, magnetometer readings, images, videos, and audio signals associated with handwriting executed on a tablet device. The study incorporates instances of gathered records, providing a graphical representation of the variation in handwriting among distinct users. The data obtained were additionally analyzed with regard to inter-person variability, intra-person variability, and classification potential. Initial findings from a limited sample of users demonstrate favorable results, intending to gather data from a more extensive user base. Full article
(This article belongs to the Special Issue Advances in HCI: Recognition Technologies and Their Applications)
►▼ Show Figures

Figure 1

22 pages, 4406 KiB  
Article
Multi-Label Emotion Recognition of Korean Speech Data Using Deep Fusion Models
by Seoin Park, Byeonghoon Jeon, Seunghyun Lee and Janghyeok Yoon
Appl. Sci. 2024, 14(17), 7604; https://doi.org/10.3390/app14177604 - 28 Aug 2024
Viewed by 1251
Abstract
As speech is the most natural way for humans to express emotions, studies on Speech Emotion Recognition (SER) have been conducted in various ways However, there are some areas for improvement in previous SER studies: (1) while some studies have performed multi-label classification, [...] Read more.
As speech is the most natural way for humans to express emotions, studies on Speech Emotion Recognition (SER) have been conducted in various ways However, there are some areas for improvement in previous SER studies: (1) while some studies have performed multi-label classification, almost none have specifically utilized Korean speech data; (2) most studies have not utilized multiple features in combination for emotion recognition. Therefore, this study proposes deep fusion models for multi-label emotion classification using Korean speech data and follows four steps: (1) preprocessing speech data labeled with Sadness, Happiness, Neutral, Anger, and Disgust; (2) applying data augmentation to address the data imbalance and extracting speech features, including the Log-mel spectrogram, Mel-Frequency Cepstral Coefficients (MFCCs), and Voice Quality Features; (3) constructing models using deep fusion architectures; and (4) validating the performance of the constructed models. The experimental results demonstrated that the proposed model, which utilizes the Log-mel spectrogram and MFCCs with a fusion of Vision-Transformer and 1D Convolutional Neural Network–Long Short-Term Memory, achieved the highest average binary accuracy of 71.2% for multi-label classification, outperforming other baseline models. Consequently, this study anticipates that the proposed model will find application based on Korean speech, specifically mental healthcare and smart service systems. Full article
(This article belongs to the Special Issue Advances in HCI: Recognition Technologies and Their Applications)
►▼ Show Figures

Figure 1

16 pages, 4398 KiB  
Article
Multi-Person Action Recognition Based on Millimeter-Wave Radar Point Cloud
by Xiaochao Dang, Kai Fan, Fenfang Li, Yangyang Tang, Yifei Gao and Yue Wang
Appl. Sci. 2024, 14(16), 7253; https://doi.org/10.3390/app14167253 - 17 Aug 2024
Cited by 2 | Viewed by 1575
Abstract
Human action recognition has many application prospects in human-computer interactions, innovative furniture, healthcare, and other fields. The traditional human motion recognition methods have limitations in privacy protection, complex environments, and multi-person scenarios. Millimeter-wave radar has attracted attention due to its ultra-high resolution and [...] Read more.
Human action recognition has many application prospects in human-computer interactions, innovative furniture, healthcare, and other fields. The traditional human motion recognition methods have limitations in privacy protection, complex environments, and multi-person scenarios. Millimeter-wave radar has attracted attention due to its ultra-high resolution and all-weather operation. Many existing studies have discussed the application of millimeter-wave radar in single-person scenarios, but only some have addressed the problem of action recognition in multi-person scenarios. This paper uses a commercial millimeter-wave radar device for human action recognition in multi-person scenarios. In order to solve the problems of severe interference and complex target segmentation in multiplayer scenarios, we propose a filtering method based on millimeter-wave inter-frame differences to filter the collected human point cloud data. We then use the DBSCAN algorithm and the Hungarian algorithm to segment the target, and finally input the data into a neural network for classification. The classification accuracy of the system proposed in this paper reaches 92.2% in multi-person scenarios through experimental tests with the five actions we set. Full article
(This article belongs to the Special Issue Advances in HCI: Recognition Technologies and Their Applications)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop