Human-computer Interaction Applications Research Articles

Volumetric 3D printing of ionic conductive elastomers for multifunctional flexible electronics

Flexible electronics based on ionic conductive elastomers (ICE) hold significant potential for applications in smart wearables, self-powered sensing, and human-computer interaction. However, current fabrication techniques constrain ICE-based ionic electronic components to simplified volumetric geometries, limiting their functionality. This work reports a volumetric 3D printing (V3DP) for fabricating flexible electronic components with excessive transparency, high conductivity, excellent thermal stability, and superior adhesion. By controlling the light dose, this printing technique enables precise modulation of the printed structures' mechanical properties. Furthermore, V3DP greatly improves the processing efficiency of high-viscosity ionic conductive liquids and makes it easier to prepare composite structures, combining different conductive mechanisms through unique overprinting. This study provides a promising strategy for preparing multifunctional, liquid-free, ionic flexible electronics, such as strain sensors and ionic-electronic triboelectric nanogenerators (iTENG).

Polyacrylamide/starch hydrogels doped with layered double hydroxides towards strain sensing applications

Electrically conductive hydrogels have attracted enormous attention due to the rapid development of flexible electronics. In this paper, the application of layered double hydroxides (LDHs) in the field of conductive hydrogel for strain sensing is firstly explored. LDHs are introduced to the polyacrylamide (PAM)/starch (St) semi-interpenetrating network (SIPN) to fabricate conductive PAM/St/LDHs (PSL) hydrogels for strain sensing applications. The results show that LDHs incorporated into PAM/St SIPN as inorganic nano fillers not only improve the mechanical strength, but also innovatively endow the hydrogel with electrical conductivity properties. The PSL hydrogels exhibit excellent mechanical properties (with an elongation strain of 1750 % and a fracture strength of 0.22 MPa) and decent sensing properties (with a gauge factor of 2.73). As a proof of concept, an 8*8 sensing array based on PSL hydrogels is designed to realize the visualization of pressure sensing, demonstrating the broad prospect of PSL hydrogels in applications of human-computer interaction, flexible wearable and soft robotics.

Hybrid deep models for parallel feature extraction and enhanced emotion state classification

Emotions play a vital role in recognizing a person’s thoughts and vary significantly with stress levels. Emotion and stress classification have gained considerable attention in robotics and artificial intelligence applications. While numerous methods based on machine learning techniques provide average classification performance, recent deep learning approaches offer enhanced results. This research presents a hybrid deep learning model that extracts features using AlexNet and DenseNet models, followed by feature fusion and dimensionality reduction via Principal Component Analysis (PCA). The reduced features are then classified using a multi-class Support Vector Machine (SVM) to categorize different types of emotions. The proposed model was evaluated using the DEAP and EEG Brainwave datasets, both well-suited for emotion analysis due to their comprehensive EEG signal recordings and diverse emotional stimuli. The DEAP dataset includes EEG signals from 32 participants who watched 40 one-minute music videos, while the EEG Brainwave dataset categorizes emotions into positive, negative, and neutral based on EEG recordings from participants exposed to six different film clips. The proposed model achieved an accuracy of 95.54% and 97.26% for valence and arousal categories in the DEAP dataset, respectively, and 98.42% for the EEG Brainwave dataset. These results significantly outperform existing methods, demonstrating the model’s superior performance in terms of precision, recall, F1-score, specificity, and Mathew correlation coefficient. The integration of AlexNet and DenseNet, combined with PCA and multi-class SVM, makes this approach particularly effective for capturing the intricate patterns in EEG data, highlighting its potential for applications in human-computer interaction and mental health monitoring, marking a significant advancement over traditional methods.

Highly conductive and sensitive alginate hydrogel strain sensors fabricated using near-field electrohydrodynamic direct-writing process

Hydrogel flexible sensors have attracted considerable attention because of their wearability, biocompatibility, and precision signal transmission capability. However, the hydrogel strain sensors fabricated by conventional printing or hand-injection methods have difficulty balancing their mechanical strength and sensing characteristics, limiting the application of hydrogel strain sensors. Herein, polyvinyl alcohol and polyacrylamide were loosely crosslinked with sodium alginate through chemical cross-linking. Subsequently, MXene nanosheets were introduced for doping, the crosslinked hydrogel conductive network was constructed, and the hydrogel strain sensors were fabricated using the electrohydrodynamic (EHD) printing method. The ions in the EHD-printed hydrogel undergo directional movement under an externally enhanced electric field, causing the formation of more uniform and dense porous conductive networks inside the hydrogel, and high electrical conductivity (0.49 S m−1) is obtained. These hydrogel strain sensors have excellent mechanical properties (tensile strength: 0.17 MPa at 787 % strain), high sensitivity (gauge factor: 1.54 at 0–100 % strain), and low detection limits (1 % strain). Furthermore, demonstrations of real-time Morse code tapping information transmission, handwriting recognition during writing, and human physiological behavior monitoring demonstrations using the fabricated sensors indicate that the EHD-printed hydrogel strain sensor method has significant potential for wearable devices and human-computer interaction applications.

Design of a Capacitive Tactile Sensor Array System for Human-Computer Interaction.

This paper introduces a novel capacitive sensor array designed for tactile perception applications. Utilizing an all-in-one inkjet deposition printing process, the sensor array exhibited exceptional flexibility and accuracy. With a resolution of up to 32.7 dpi, the sensor array was capable of capturing the fine details of touch inputs, making it suitable for applications requiring high spatial resolution. The design incorporates two multiplexers to achieve a scanning rate of 100 Hz, ensuring the rapid and responsive data acquisition that is essential for real-time feedback in interactive applications, such as gesture recognition and haptic interfaces. To evaluate the performance of the capacitive sensor array, an experiment that involved handwritten number recognition was conducted. The results demonstrated that the sensor accurately captured fingertip inputs with a high precision. When combined with an Auxiliary Classifier Generative Adversarial Network (ACGAN) algorithm, the sensor system achieved a recognition accuracy of 98% for various handwritten numbers from "0" to "9". These results show the potential of the capacitive sensor array for advanced human-computer interaction applications.

Dual Leap Motion Controller 2: A Robust Dataset for Multi-view Hand Pose Recognition

This paper presents Multi-view Leap2 Hand Pose Dataset (ML2HP Dataset), a new dataset for hand pose recognition, captured using a multi-view recording setup with two Leap Motion Controller 2 devices. This dataset encompasses a diverse range of hand poses, recorded from different angles to ensure comprehensive coverage. The dataset includes real images with the associated precise and automatic hand properties, such as landmark coordinates, velocities, orientations, and finger widths. This dataset has been meticulously designed and curated to maintain a balance in terms of subjects, hand poses, and the usage of right or left hand, ensuring fairness and parity. The content includes 714,000 instances from 21 subjects of 17 different hand poses (including real images and 247 associated hand properties). The multi-view setup is necessary to mitigate hand occlusion phenomena, ensuring continuous tracking and pose estimation required in real human-computer interaction applications. This dataset contributes to advancing the field of multimodal hand pose recognition by providing a valuable resource for developing advanced artificial intelligence human computer interfaces.

Sarcasm Detection Based on Sentiment Analysis of Audio Corpus Using Deep Learning

With the rapid progress in digitalization, hundreds of services and applications have begun to depend on human-machine communication, primarily voice-based. As technology advances, it has become increasingly imperative to develop systems that can discern sophisticated emotions from humans, like sarcasm. Despite research that has been conducted on the detection and analysis of speech signals to try and understand emotion, the detection of sarcasm, which is often not overtly expressed, is quite far from something that has received the attention it deserves in recent years. It plays a very important role in any utterance, searching to identify the psychology, mood, or even health behind it. Hence, it does give interactions with humans a more human-like flavor while raising the feeling of understanding human emotions. This article tries to identify sarcasm in speech using audio data derived from real-world, spontaneous, monolingual corpora. In this classification process, a deep learning classifier known as CNN-LSTM model is used. Results of the study showed that a robust combination between feature extraction through convolutional layers and sequential learning through LSTM layers can identify intricate speech patterns in sarcasm. Experimented and validated in terms of performance, this model shows the model to be very good at discriminating between sarcastic and no sarcastic speech, thus promoting it for more extensive use in various applications related to sentiment analysis and in human-computer interaction applications. The results show that this system provides far much closer approximations to human interaction styles, but its impact would extend into a wide area from customer services, monitoring mental health, and even AI-driven communication systems. It is a monolingual and spontaneous corpus that will open the gates for further work and development down the line and possibly extend to multilingual and more diversified speech contexts, thus fine-tuning accuracy in practical scenarios of sarcasm detection.

Enhancing human computer interaction with coot optimization and deep learning for multi language identification

Human-Computer Interaction (HCI) is a multidisciplinary field focused on designing and utilizing computer technology, underlining the interaction interface between computers and humans. HCI aims to generate systems that allow consumers to relate to computers effectively, efficiently, and pleasantly. Multiple Spoken Language Identification (SLI) for HCI (MSLI for HCI) denotes the ability of a computer system to recognize and distinguish various spoken languages to enable more complete and handy interactions among consumers and technology. SLI utilizing deep learning (DL) involves using artificial neural networks (ANNs), a subset of DL models, to automatically detect and recognize the language spoken in an audio signal. DL techniques, particularly neural networks (NNs), have succeeded in various pattern detection tasks, including speech and language processing. This paper develops a novel Coot Optimizer Algorithm with a DL-Driven Multiple SLI and Detection (COADL-MSLID) technique for HCI applications. The COADL-MSLID approach aims to detect multiple spoken languages from the input audio regardless of gender, speaking style, and age. In the COADL-MSLID technique, the audio files are transformed into spectrogram images as a primary step. Besides, the COADL-MSLID technique employs the SqueezeNet model to produce feature vectors, and the COA is applied to the hyperparameter range of the SqueezeNet method. The COADL-MSLID technique exploits the SLID process’s convolutional autoencoder (CAE) model. To underline the importance of the COADL-MSLID technique, a series of experiments were conducted on the benchmark dataset. The experimentation validation of the COADL-MSLID technique exhibits a greater accuracy result of 98.33% over other techniques.

A Text-based Intelligently driven Emotion Recognition Framework

Introduction: Emotion recognition from text has gained considerable interest due to its applications in human-computer interaction, emotion analysis, and psychiatric research. Traditional methods have struggled with emotional ambiguity, cultural nuances, and the dynamic nature of language, which affect the reliability of emotion recognition. This paper presents a novel emotion recognition framework named Artificial Gorilla Troops driven Modified Gaussian Support Vector Machine (AGT-MGSVM).Methods: We gathered a publicly available ISEAR dataset containing various textual emotional expressions and applied natural language processing (NLP) techniques for text pre-processing. The suggested AGT-MGSVM approach combines the resilience of the Gaussian assist vector gadget (GSVM) with the ability of AGT, a bio-stimulated optimization method. AGT improves the MGSVM with the aid of dynamically regulating its parameters based on the evolutionary conduct of gorilla troops, optimizing the version to enhance emotion popularity. Results: We examine the performance of the proposed technique against traditional emotion reputation methods using standard metrics inclusive of recall (89.2%), precision (89.5%), F1-score (89.4%), and accuracy (89.9%). Conclusion: The counseled AGT-MGSVM method is a promising improvement in intelligence-driven emotion reputation from the text.

Sitting Posture Recognition Systems: Comprehensive Literature Review and Analysis

Sitting posture recognition systems have gained significant attention due to their potential applications in various domains, including healthcare, ergonomics, and human-computer interaction. This paper presents a comprehensive literature review and analysis of existing sitting posture recognition systems. Through an extensive examination of relevant research articles and conference papers, we identify and analyze the underlying technologies, methodologies, datasets, performance metrics, and applications associated with these systems. The review encompasses both traditional methods, such as vision-based approaches and sensor-based techniques, as well as emerging technologies such as machine learning and deep learning algorithms. Additionally, we examine the challenges, constraints, and future trends in the field of sitting posture recognition systems. Researchers, practitioners, and policymakers who want to comprehend the most recent developments and latest trends in sitting posture recognition technology will find great value in this study.

Application of Human-Computer Interaction in Religious Music Resources System Based on Artificial Intelligent Powered CAD

Application of Human-Computer Interaction in Religious Music Resources System Based on Artificial Intelligent Powered CAD

Application of E-commerce Human-Computer Interaction Based on AI-Powered CAD and Big Data Analysis in Sustainable Agriculture

Application of E-commerce Human-Computer Interaction Based on AI-Powered CAD and Big Data Analysis in Sustainable Agriculture

Highly conductive and stable double network carrageenan organohydrogels for advanced strain sensing and signal recognition arrays

Conductive hydrogels with excellent mechanical properties, a broad detection range, and stability in complex environments have remained a significant challenge for the development of flexible sensors. In this study, a straightforward freeze-thaw cycles strategy was developed to fabricate a polyvinyl alcohol (PVA)/carrageenan (CA)/calcium chloride (CaCl2)/MXene-based double network organohydrogel (PCCME) for highly flexible and responsive strain detection across a broad temperature spectrum. The PCCME organohydrogel features multiple interactive forces including hydrogen bonding, ionic interactions, and microphase crystallization, which contribute to the organohydrogel's exceptional mechanical and electrical performance. The PCCME organohydrogel exhibited excellent performance in a load-unload test repeated 100 times after being maintained at room temperature for 7 days, with a minimal mechanical decay of only 2.6%. Furthermore, the repaired PCCME organohydrogel retained its robust stability after storage at low temperatures followed by placement at room temperature. The organohydrogel sensor not only detects various movement amplitudes of the human body but also recognizes arrays of pressure signals and converts these into digital images, highlighting its significant potential for applications in rehabilitation monitoring, pressure sensing, and human-computer interaction.

Public Behavior and Emotion Correlation Mining Driven by Aspect From News Corpus.

Emotion motivates behavior. Investigating the correlation between behavior and emotion, an often overlooked perspective, plays a significant role in uncovering the underlying motives behind behaviors and the intrinsic cause-effects of social events. This article proposes a methodology for mining the correlation between public behavior and emotion using daily news data. Initially, aspect-emotion-reaction (A-E-R) triplets are extracted and generalized, encompassing both explicit and implicit patterns. Then, a knowledge representation model based on hypothetical context (KRHC) with a self-reflection mechanism is proposed to uncover implicit relationships between emotion and behavior through attention mechanisms. By combining rule-based methods for explicit relationships and deep learning for implicit ones, an understanding of emotion-behavior patterns is achieved. In this study, the behaviors are divided into three categories of prosocial, antisocial, and normal behaviors with ten secondary types. Seven categories of emotions are adopted. The proposed deep learning model KRHC is validated on A-E-R datasets and public KINSHIP datasets. The experiment results are concluded; for example, when "fear", "sad", and "surprise" emotions appear, it drives behavior "panic" with most probability. These findings could provide insights for both human-computer interaction and public safety management applications.

A Review of Deep Learning Based Video Action Recognition Techniques

Video action recognition is an important research direction in the field of computer vision and pattern recognition, with extensive applications in intelligent video surveillance, human-computer interaction, and sports analysis. The development of data storage and computing hardware over the past decade has driven a shift from traditional feature extraction and machine learning algorithms to deep learning-based approaches. This paper reviews the current state of development, problems, and future research directions of video action recognition techniques. Traditional methods are gradually being replaced by deep learning methods such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long-short-term memory networks (LSTMs). These methods automatically extract features and handle time-dependency, significantly improving the accuracy and robustness of action recognition. In particular, models based on the attention mechanism further enhance action recognition performance by dynamically adjusting the focus of attention, a current hot spot in research. Despite many advances, video action recognition still faces several challenges, including high computational resource requirements, complex model training, dataset bias issues, and variations in real-world application scenarios such as viewpoint changes, lighting changes, and occlusion. Future research can explore multi-modal fusion, lightweight models, self-supervised learning, and cross-domain transfer learning to improve the accuracy, robustness, and generalization of action recognition. The review provided aims to offer researchers a comprehensive perspective on the current state of development and future research directions of video action recognition technology.

Flexible and washable MXene@PEDOT:PSS@CS@PU pressure sensors

Flexible electronics have attracted great interest because of their potential applications in healthcare monitoring and human-computer interaction. The performance of flexible electronics depends on the design of structure, materials, and mechanical responses. In this work, we explore the use of polyurethane (PU) sponge as the scaffold to form flexible piezoresistive pressure sensors with conductive networks made from conductive polymer PEDOT:PSS and MXene nanosheets. The sensitivity of the prepared pressure sensors reaches 0.2171 kPa−1 under mechanical pressure in a range of 1.8–6.15 kPa. The prepared pressure sensors are re-usable after washing. The applications of the prepared pressure sensors in the sensing of physiological behavior are demonstrated under the action of finger tapping, finger bending, vocal cord vibration, swallowing, and pulsing of radial artery. The prepared pressure sensors have great prospects for applications in human-machine interaction and physiological diagnosis.

Wearable high-density EMG sleeve for complex hand gesture classification and continuous joint angle estimation

High-density electromyography (HD-EMG) can provide a natural interface to enhance human–computer interaction (HCI). This study aims to demonstrate the capability of a novel HD-EMG forearm sleeve equipped with up to 150 electrodes to capture high-resolution muscle activity, decode complex hand gestures, and estimate continuous hand position via joint angle predictions. Ten able-bodied participants performed 37 hand movements and grasps while EMG was recorded using the HD-EMG sleeve. Simultaneously, an 18-sensor motion capture glove calculated 23 joint angles from the hand and fingers across all movements for training regression models. For classifying across the 37 gestures, our decoding algorithm was able to differentiate between sequential movements with 97.3±0.3%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$97.3 \\pm 0.3\\%$$\\end{document} accuracy calculated on a 100 ms bin-by-bin basis. In a separate mixed dataset consisting of 19 movements randomly interspersed, decoding performance achieved an average bin-wise accuracy of 92.8±0.8%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$92.8 \\pm 0.8\\%$$\\end{document}. When evaluating decoders for use in real-time scenarios, we found that decoders can reliably decode both movements and movement transitions, achieving an average accuracy of 93.3±0.9%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$93.3 \\pm 0.9\\%$$\\end{document} on the sequential set and 88.5±0.9%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$88.5 \\pm 0.9\\%$$\\end{document} on the mixed set. Furthermore, we estimated continuous joint angles from the EMG sleeve data, achieving a R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$R^2$$\\end{document} of 0.884±0.003\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$0.884 \\pm 0.003$$\\end{document} in the sequential set and 0.750±0.008\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$0.750 \\pm 0.008$$\\end{document} in the mixed set. Median absolute error (MAE) was kept below 10° across all joints, with a grand average MAE of 1.8±0.04∘\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$1.8 \\pm 0.04^\\circ$$\\end{document} and 3.4±0.07∘\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$3.4 \\pm 0.07^\\circ$$\\end{document} for the sequential and mixed datasets, respectively. We also assessed two algorithm modifications to address specific challenges for EMG-driven HCI applications. To minimize decoder latency, we used a method that accounts for reaction time by dynamically shifting cue labels in time. To reduce training requirements, we show that pretraining models with historical data provided an increase in decoding performance compared with models that were not pretrained when reducing the in-session training data to only one attempt of each movement. The HD-EMG sleeve, combined with sophisticated machine learning algorithms, can be a powerful tool for hand gesture recognition and joint angle estimation. This technology holds significant promise for applications in HCI, such as prosthetics, assistive technology, rehabilitation, and human–robot collaboration.

EEG-VTTCNet: A loss joint training model based on the vision transformer and the temporal convolution network for EEG-based motor imagery classification

Brain-computer interface (BCI) is a technology that directly connects signals between the human brain and a computer or other external device. Motor imagery electroencephalographic (MI-EEG) signals are considered a promising paradigm for BCI systems, with a wide range of potential applications in medical rehabilitation, human–computer interaction, and virtual reality. Accurate decoding of MI-EEG signals poses a significant challenge due to issues related to the quality of the collected EEG data and subject variability. Therefore, developing an efficient MI-EEG decoding network is crucial and warrants research. This paper proposes a loss joint training model based on the vision transformer (VIT) and the temporal convolutional network (EEG-VTTCNet) to classify MI-EEG signals. To take advantage of multiple modules together, the EEG-VTTCNet adopts a shared convolution strategy and a dual-branching strategy. The dual-branching modules perform complementary learning and jointly train shared convolutional modules with better performance. We conducted experiments on the BCI Competition IV-2a and IV-2b datasets, and the proposed network outperformed the current state-of-the-art techniques with an accuracy of 84.58% and 90.94%, respectively, for the subject-dependent mode. In addition, we used t-SNE to visualize the features extracted by the proposed network, further demonstrating the effectiveness of the feature extraction framework. We also conducted extensive ablation and hyperparameter tuning experiments to construct a robust network architecture that can be well generalized.

EDA-Graph: Graph Signal Processing of Electrodermal Activity for Emotional States Detection.

The continuous detection of emotional states has many applications in mental health, marketing, human-computer interaction, and assistive robotics. Electrodermal activity (EDA), a signal modulated by sympathetic nervous system activity, provides continuous insight into emotional states. However, EDA possesses intricate nonstationary and nonlinear characteristics, making the extraction of emotion-relevant information challenging. We propose a novel graph signal processing (GSP) approach to model EDA signals as graphical networks, termed EDA-graph. The GSP leverages graph theory concepts to capture complex relationships in time-series data. To test the usefulness of EDA-graphs to detect emotions, we processed EDA recordings from the CASE emotion dataset using GSP by quantizing and linking values based on the Euclidean distance between the nearest neighbors. From these EDA-graphs, we computed the features of graph analysis, including total load centrality (TLC), total harmonic centrality (THC), number of cliques (GNC), diameter, and graph radius, and compared those features with features obtained using traditional EDA processing techniques. EDA-graph features encompassing TLC, THC, GNC, diameter, and radius demonstrated significant differences (p < 0.05) between five emotional states (Neutral, Amused, Bored, Relaxed, and Scared). Using machine learning models for classifying emotional states evaluated using leave-one-subject-out cross-validation, we achieved a five-class F1 score of up to 0.68.

Implementation of MATLAB image processing and AI for real-time mood prediction

This study proposes an advanced real-time mood prediction system that utilizes Convolutional Neural Networks (CNNs) and webcam-based Region of Interest (ROI) extraction. By employing MATLAB and its Computer Vision Toolbox, the system detects and preprocesses facial regions from live webcam feeds to accurately predict an individual’s mood. The performance of this system is validated using a custom dataset, showcasing its potential for applications in human-computer interaction, psychological assessments, and real-time monitoring. This approach highlights a significant advancement in combining AI and image processing for real-time emotional analysis.