Search Results (1,361)

This study addresses the ongoing challenge of optimizing chatbot interactions to significantly enhance customer experience and satisfaction through personalized, empathetic responses. Using advanced NLP tools and strong statistical methodologies, we developed and evaluated a multi-layered analytical framework to accurately identify user intents, assess customer feedback, and generate emotionally intelligent interactions. With over 270,000 customer chatbot interaction records in our dataset, we employed spaCy-based NER and clustering algorithms (HDBSCAN and K-Means) to categorize customer queries precisely. Text classification was performed using random forest, logistic regression, and SVM, achieving near-perfect accuracy. Sentiment analysis was conducted using VADER, Naive Bayes, and TextBlob, complemented by semantic analysis via LDA. Statistical tests, including Chi-square, Kruskal–Wallis, Dunn’s test, ANOVA, and logistic regression, confirmed the significant impact of tailored, empathetic response strategies on customer satisfaction. Correlation analysis indicated that traditional measures like sentiment polarity and text length insufficiently capture customer satisfaction nuances. The results underscore the critical role of context-specific adjustments and emotional responsiveness, paving the way for future research into chatbot personalization and customer-centric system optimization. Full article

With the growing use of digital technologies and interactive games, there is rising interest in how people respond to challenges, stress, and decision-making in virtual environments. Studying human behavior in such settings helps to improve design, training, and user experience. Instead of relying on complex devices, Virtual Reality (VR) creates new ways to observe and understand these responses in a simple and engaging format. This study introduces a lightweight method for monitoring stress levels that uses VR as the primary sensing platform. Detection relies on behavioral signals from VR. A minimal sensor such as Galvanic Skin Response (GSR), which measures skin conductance as a sign of physiological body response, supports the Sensor-Assisted Unity Architecture. The proposed Sensor-Assisted Unity Architecture focuses on analyzing the user’s behavior inside the virtual environment along with physical sensory measurements. Most existing systems rely on physiological wearables, which add both cost and complexity. The Sensor-Assisted Unity Architecture shifts the focus to behavioral analysis in VR supplemented by minimal physiological input. Behavioral cues captured within the VR environment are analyzed in real time by an embedded processor, which then triggers simple physical feedback. Results show that combining VR behavioral data with a minimal sensor can improve detection in cases where behavioral or physiological signals alone may be insufficient. While this study does not quantitatively compare the Sensor-Assisted Unity Architecture to multi-sensor setups, it highlights VR as the main platform, with sensor input offering targeted enhancements without significantly increasing system complexity. Full article

Global navigation satellite systems (GNSSs) are an integral part of modern society and are used in various industries, providing users with positioning, navigation, and timing (PNT). However, their effectiveness is vulnerable to signal interference, since GNSSs are based on received satellite signals from space, and that can severely impact applications that rely on continuous and accurate data. Interference can pose significant risks to sectors dependent on GNSSs, including transportation, telecommunications, finance, geodesy, and others. For this reason, in parallel with the development of GNSSs, various interference protection techniques are being developed to enable users to receive GNSS signals without the risk of interference, which can cause various effects, such as reducing the accuracy of positioning, as well as completely blocking signal reception and making it impossible to obtain positioning. There are various sources and methods of interfering with GNSS signals, and the greatest consequences are caused by intentional interference, which includes jamming, spoofing, and meaconing. This study investigates the effects of jamming devices on static GNSS observations using high-accuracy devices through multiple controlled experiments using both single-frequency (SF) and multi-frequency (MF) jammers. The aim was to identify the distances within which signal interference devices disrupt GNSS signal reception and position accuracy. The research conducted herein was divided into several phases where zones within which the jammer completely blocked the reception of the GNSS signal were determined (blackout zones), as were zones within which it was possible to obtain the position (but the influence of the jammer was present) and the influence of the jammer from different directions/azimuths in relation to the GNSS receiver. Various statistical indicators of the jammer’s influence, such as DOP (dilution of precision), SNR (signal-to-noise-ratio), RMS (root mean square), and others, were obtained through research. The results of this study indicate that commercially available, low-cost jamming devices, when operated within manufacturer-specified distances, completely disrupt the reception of GNSS signals. Their impact is also evident at greater distances, where they significantly reduce SNR values, increase DOP, and decrease the number of visible satellites, leading to reduced measurement reliability and integrity. These results underline the necessity of developing effective protection mechanisms against GNSS interference and strategies to ensure reliable signal reception in GNSS-dependent applications, particularly as the use of jamming devices becomes more prevalent. Full article

Privacy risk mining, a crucial domain in data privacy protection, endeavors to uncover potential information among datasets that could be linked to individuals’ sensitive data. Existing anonymization and privacy assessment techniques either lack quantitative granularity or fail to adapt to dynamic, heterogeneous data environments. In this work, we propose a unified two-phase linkability quantification framework that systematically measures privacy risks at both the inter-dataset and intra-dataset levels. Our approach integrates unsupervised clustering on attribute distributions with record-level matching to compute interpretable, fine-grained risk scores. By aligning risk measurement with regulatory standards such as the GDPR, our framework provides a practical, scalable solution for safeguarding user privacy in evolving data-sharing ecosystems. Extensive experiments on real-world and synthetic datasets show that our method achieves up to 96.7% precision in identifying true linkage risks, outperforming the compared baseline by 13 percentage points under identical experimental settings. Ablation studies further demonstrate that the hierarchical risk fusion strategy improves sensitivity to latent vulnerabilities, providing more actionable insights than previous privacy gain-based metrics. Full article

In the era of digital interconnectivity, user-generated reviews on platforms such as TripAdvisor have become a valuable source of social feedback, reflecting collective experiences and perceptions of airline services. However, aggregating such feedback presents several challenges: evaluations are typically expressed using linguistic ordinal scales, are subjective, often incomplete, and influenced by opinion dynamics within social networks. To effectively deal with these complexities and extract meaningful insights, this study proposes an information-driven decision-making framework that integrates Fuzzy Belief Structures with the TOPSIS method. To handle the uncertainty and imprecision of linguistic ratings, user opinions are modeled as fuzzy belief distributions over satisfaction levels. Rankings are then derived using TOPSIS by comparing each airline’s aggregated profile to ideal satisfaction benchmarks via a belief-based distance measure. This framework presents a novel solution for measuring synthetic satisfaction in complex social feedback systems, thereby contributing to the understanding of information flow, belief aggregation, and emergent order in digital opinion networks. The methodology is demonstrated using a real-world dataset of TripAdvisor airline reviews, providing a robust and interpretable benchmark for service quality. Moreover, this study applies Shannon entropy to classify and interpret the consistency of customer satisfaction ratings among Star Alliance airlines. The results confirm the stability of the Airline Satisfaction Index (ASI), with extremely high correlations among the five rankings generated using different fuzzy utility function models. The methodology reveals that airlines such as Singapore Airlines, ANA, EVA Air, and Air New Zealand consistently achieve high satisfaction scores across all fuzzy model configurations, highlighting their strong and stable performance regardless of model variation. These airlines also show both low entropy and high average scores, confirming their consistent excellence. Full article

This paper addresses the problem of recognising and classifying hand movements to control an upper-limb exoskeleton. To solve this problem, a multisensory system based on the fusion of data from electromyography (EMG) sensors, inertial measurement units (IMUs), and virtual reality (VR) trackers is proposed, which provides highly accurate detection of users’ movements. Signal preprocessing (noise filtering, segmentation, normalisation) and feature extraction were performed to generate input data for regression and classification models. Various machine learning algorithms are used to recognise motor activity, ranging from classical algorithms (logistic regression, k-nearest neighbors, decision trees) and ensemble methods (random forest, AdaBoost, eXtreme Gradient Boosting, stacking, voting) to deep neural networks, including convolutional neural networks (CNNs), gated recurrent units (GRUs), and transformers. The algorithm for integrating machine learning models into the exoskeleton control system is considered. In experiments aimed at abandoning proprietary tracking systems (VR trackers), absolute position regression was performed using data from IMU sensors with 14 regression algorithms: The random forest ensemble provided the best accuracy (mean absolute error = 0.0022 metres). The task of classifying activity categories out of nine types is considered below. Ablation analysis showed that IMU and VR trackers produce a sufficient informative minimum, while adding EMG also introduces noise, which degrades the performance of simpler models but is successfully compensated for by deep networks. In the classification task using all signals, the maximum result (99.2%) was obtained on Transformer; the fully connected neural network generated slightly worse results (98.4%). When using only IMU data, fully connected neural network, Transformer, and CNN–GRU networks provide 100% accuracy. Experimental results confirm the effectiveness of the proposed architectures for motor activity classification, as well as the use of a multi-sensor approach that allows one to compensate for the limitations of individual types of sensors. The obtained results make it possible to continue research in this direction towards the creation of control systems for upper exoskeletons, including those used in rehabilitation and virtual simulation systems. Full article

Background: Virtual reality (VR) technology has emerged as a promising tool for promoting physical activity through immersive gaming experiences. This study aimed to compare the physiological responses and perceived exertion during VR gaming using two different locomotion interfaces: omnidirectional treadmill and traditional controllers. Methods: Twenty-one university students (7 women, 14 men; age 23.5 ± 1.4 years) participated in a crossover study comparing physical activity intensity during VR gaming using traditional controllers versus an omnidirectional treadmill (Virtuix Omni). Participants played VRZ Torment for 15 min in each condition, separated by 30 min washout periods. Physiological responses were measured using indirect calorimetry (Cortex METAMAX® 3B), heart rate monitoring (Polar V800), and subjective ratings of perceived exertion (RPE). Exercise intensity was classified according to established guidelines, and user satisfaction was assessed using a 10-point scale. Results: Omnidirectional treadmill locomotion resulted in significantly higher physiological responses and perceived exertion across all measured variables compared to controller-based movement: heart rate (76.7 ± 11.7% vs. 51.7 ± 9.5% HRmax, p < 0.001), metabolic equivalents (7.3 ± 1.7 vs. 2.1 ± 0.3 METs, p < 0.001), and RPE (14.4 ± 2.9 vs. 9.3 ± 1.5, p < 0.001). Treadmill gaming achieved vigorous-intensity exercise, while controller gaming remained at light intensity. User satisfaction was significantly higher with treadmill locomotion (8.5 ± 1.3 vs. 5.0 ± 2.3, p < 0.001). Strong correlations were observed between physiological measures only during high-intensity treadmill exercise. Conclusions: Omnidirectional treadmill VR gaming achieves vigorous-intensity physical activity sufficient to meet health recommendations, while traditional controller gaming provides only light-intensity exercise. These findings support the potential of locomotion-enhanced VR systems for health promotion. Full article

Effective water quality monitoring is very important for detecting pollution and protecting public health. However, traditional methods are slow, relying on costly equipment, central laboratories, and expert staffing, which delays real-time measurements. At the same time, significant advancements have been made in the field of plasmonic sensing technologies, making them ideal for environmental monitoring. However, their reliance on large, expensive spectrometers limits accessibility. This work aims to bridge the gap between advanced plasmonic sensing and practical water monitoring needs, by integrating plasmonic sensors with mobile technology. We present BioColor, a mobile platform that consists of a plasmonic sensor setup, mobile application, and cloud services. The platform processes captured colorimetric sensor images in real-time using optimized image processing algorithms, including region-of-interest segmentation, color extraction (mean and dominant), and comparison via the CIEDE2000 metric. The results are visualized within the mobile app, providing instant and automated access to the sensing outcome. In our validation experiments, the system consistently measured color differences in various sensor images captured under media with different refractive indices. A user experience test with 12 participants demonstrated excellent usability, resulting in a System Usability Scale (SUS) score of 93. The BioColor platform brings advanced sensing capabilities from hardware into software, making environmental monitoring more accessible, efficient, and continuous. Full article

Accurate full-body motion reconstruction from sparse sensors is crucial for VR/AR applications but remains challenging due to the under-constrained nature of limited observations and the computational constraints of mobile platforms. This paper presents LGSIK-Poser, a unified and lightweight framework that supports real-time motion reconstruction from heterogeneous sensor configurations, including head-mounted displays, handheld controllers, and up to three optional inertial measurement units, without requiring reconfiguration across scenarios. The model integrates temporally grouped LSTM modeling, anatomically structured graph-based reasoning, and region-specific inverse kinematics refinement to enhance end-effector accuracy and structural consistency. Personalized body shape is estimated using user-specific anthropometric priors within the SMPL model, a widely adopted parametric representation of human shape and pose. Experiments on the AMASS benchmark demonstrate that LGSIK-Poser achieves state-of-the-art accuracy with up to 48% improvement in hand localization, while reducing model size by 60% and latency by 22% compared to HMD-Poser. The system runs at 63.65 FPS with only 3.74 M parameters, highlighting its suitability for real-time immersive applications. Full article

Joint attention, the capacity of two or more individuals to focus on a common event simultaneously, is fundamental to human–human interaction, enabling effective communication. When considering the field of social robotics, emulating this capability might be necessary for promoting natural interactions and thus improving user engagement. Responding to joint attention (RJA), defined as the ability to react to external attentional cues by aligning focus with another individual, plays a critical role in promoting mutual understanding. This study examines how RJA impacts user engagement during human–robot interaction. The participants play a turn-taking game against a social robot under two conditions: with our RJA system active and with the system inactive. Auditory and visual stimuli are introduced to simulate real-world dynamics, testing the robot’s ability to detect and follow the user’s focus of attention. We use a twofold approach to evaluate the system’s impact on the user’s experience during the interaction. On the one hand, we use head pose telemetry to quantify attentional aspects of engagement, including measures of distraction and focus during the interaction. On the other hand, we use a post-experimental questionnaire incorporating the User Engagement Scale Short Form to assess engagement. The results regarding telemetry data reveal reduced distraction and improved attentional consistency, highlighting the system’s ability to maintain attention on the current task effectively. Furthermore, the questionnaire responses show that RJA significantly enhances self-reported engagement when the system is active. We believe these findings confirm the value of attentional mechanisms in promoting engaging human–robot interactions. Full article

The accurate perception of visual stimuli in human–machine systems is crucial for improving system safety, usability, and task performance. The widespread adoption of digital technology has significantly increased the importance of visual interfaces and information. Therefore, it is essential to design visual interfaces and information with user characteristics in mind to ensure accurate perception of visual information. This study employed the Cognitive Perceptual Assessment for Driving (CPAD) to evaluate and compare gender differences in the ability to perceive visual signals within complex visual stimuli. The experimental setup included a computer with CPAD installed, along with a touch monitor, mouse, joystick, and keyboard. The participants included 11 male and 20 female students, with an average age of 22 for males and 21 for females. Prior to the experiment, participants were instructed to determine whether a signal stimulus was present: if a square, presented as the signal, was included in the visual stimulus, they moved the joystick to the left; otherwise, they moved it to the right. Each participant performed a total of 40 trials. The entire experiment was recorded on video to measure overall response times. The experiment measured the number of correct detections of signal presence, response times, the number of misses (failing to detect the signal when present), and false alarms (detecting the signal when absent). The analysis of experimental data revealed no significant differences in perceptual ability or response times for visual stimuli between genders. However, males demonstrated slightly superior perceptual ability and marginally shorter response times compared to females. Analyses of sensitivity and response bias, based on signal detection theory, also indicated a slightly higher perceptual ability in males. In conclusion, although these differences were not statistically significant, males demonstrated a slightly better perception ability for visual stimuli. The findings of this study can inform the design of information, user interfaces, and visual displays in human–machine systems, particularly in light of the recent trend of increased female participation in the industrial sector. Future research will focus on diverse types of visual information to further validate these findings. Full article

Background/Objectives: This study investigated the association between cochlear implant (CI) users’ assessed perception of musical sound quality and their subjective music perception and music-related quality of life (QoL). The aim was to provide a comprehensive evaluation by integrating a relatively objective Turkish Multiple Stimulus with Hidden Reference and Anchor (TR-MUSHRA) test and a subjective music questionnaire. Methods: Thirty CI users and thirty normal-hearing (NH) adults were assessed. Perception of sound quality was measured using the TR-MUSHRA test. Subjective assessments were conducted with the Music-Related Quality of Life Questionnaire (MuRQoL). Results: TR-MUSHRA results showed that while NH participants rated all filtered stimuli as perceptually different from the original, CI users provided similar ratings for stimuli with adjacent high-pass filter settings, indicating less differentiation in perceived sound quality. On the MuRQoL, groups differed on the Frequency subscale but not the Importance subscale. Critically, no significant correlation was found between the TR-MUSHRA scores and the MuRQoL subscale scores in either group. Conclusions: The findings demonstrate that TR-MUSHRA is an effective tool for assessing perceived sound quality relatively objectively, but there is no relationship between perceiving sound quality differences and measures of self-reported musical engagement and its importance. Subjective music experience may represent different domains beyond the perception of sound quality. Therefore, successful auditory rehabilitation requires personalized strategies that consider the multifaceted nature of music perception beyond simple perceptual judgments. Full article

Freezing of gait (FoG) impairs mobility and daily functioning and increases the risk of falls, leading to a reduced quality of life (QoL) in people with Parkinson’s disease (PD). The Cue2walk, a wearable smart cueing device, can detect FoG and hereupon provides rhythmic cues to help people with PD manage FoG in daily life. This study investigated the user experiences and device usage of the Cue2walk, and its impact on health-related QoL, FoG and daily activities. Twenty-five users of the Cue2walk were invited to fill out an online survey, which included a modified version of the EQ-5D-5L, tailored to the use of the Cue2walk, and its scale for health-related QoL, three FoG-related questions, and a question about customer satisfaction. Sixteen users of the Cue2walk completed the survey. Average device usage per day was 9 h (SD 4). Health-related QoL significantly increased from 5.2/10 (SD 1.3) to 6.2/10 (SD 1.3) (p = 0.005), with a large effect size (Cohen’s d = 0.83). A total of 13/16 respondents reported a positive effect on FoG duration, 12/16 on falls, and 10/16 on daily activities and self-confidence. Customer satisfaction was 7.8/10 (SD 1.7). This pilot study showed that Cue2walk usage per day is high and that 15/16 respondents experienced a variety of positive effects since using the device. To validate these findings, future studies should include a larger sample size and a more extensive set of questionnaires and physical measurements monitored over time. Full article

This study builds on Davis’ TAM by integrating environmental and psychological variables relevant to AI medical diagnostics. This study developed a mathematical theoretical model called the “AI medical diagnosis-acceptance evaluation model” (AMD-AEM) to better understand acceptance behavior intention. Using mathematical modeling, we established reflective measurement model indicators and structural equation relationships, where linear structural equations illustrate the interactions among latent variables. In 2025, we collected empirical data from 2380 patients and medical staff who have experience with AI diagnostic systems in teaching hospitals in central Taiwan. Smart PLS 3 was employed to validate the AMD-AEM model. The results reveal that perceived usefulness (PU) and information quality (IQ) are the primary predictors of acceptance behavior intention (ABI). Additionally, perceived ease of use (PE) indirectly influences ABI through PU and attitude toward use (ATU). AI emotional perception (AEP) notably shows a significant positive relationship with ATU, highlighting that warm and positive human–AI interactions are crucial for user acceptance. IQ was identified as a mediating variable, with variance accounted for (VAF) coefficient analysis confirming its complete mediation effect on the path from ATU to ABI. This indicates that information quality enhances user attitudes and directly increases acceptance behavior intention. The AMD-AEM model demonstrates an excellent fit, providing valuable insights for academia and the healthcare industry. Full article

Data over sound (DoS) is an established technique that has experienced a resurgence in recent years, finding applications in areas such as contactless payments, device pairing, authentication, presence detection, toys, and offline data transfer. This study introduces CardiaWhisper, a system that extends the DoS concept to the medical domain by using a medical data-over-sound (MDoS) framework. CardiaWhisper integrates wearable biomedical sensors with home care systems, edge or IoT gateways, and telemedical networks or cloud platforms. Using a transmitter device, vital signs such as ECG (electrocardiogram) signals, PPG (photoplethysmogram) signals, RR (respiratory rate), and ACC (acceleration/movement) are sensed, conditioned, encoded, and acoustically transmitted to a nearby receiver—typically a smartphone, tablet, or other gadget—and can be further relayed to edge and cloud infrastructures. As a case study, this paper presents the real-time transmission and processing of ECG signals. The transmitter integrates an ECG sensing module, an encoder (either a PLL-based FM modulator chip or a microcontroller), and a sound emitter in the form of a standard piezoelectric speaker. The receiver, in the form of a mobile phone, tablet, or desktop computer, captures the acoustic signal via its built-in microphone and executes software routines to decode the data. It then enables a range of control and visualization functions for both local and remote users. Emphasis is placed on describing the system architecture and its key components, as well as the software methodologies used for signal decoding on the receiver side, where several algorithms are implemented using open-source, platform-independent technologies, such as JavaScript, HTML, and CSS. While the main focus is on the transmission of analog data, digital data transmission is also illustrated. The CardiaWhisper system is evaluated across several performance parameters, including functionality, complexity, speed, noise immunity, power consumption, range, and cost-efficiency. Quantitative measurements of the signal-to-noise ratio (SNR) were performed in various realistic indoor scenarios, including different distances, obstacles, and noise environments. Preliminary results are presented, along with a discussion of design challenges, limitations, and feasible applications. Our experience demonstrates that CardiaWhisper provides a low-power, eco-friendly alternative to traditional RF or Bluetooth-based medical wearables in various applications. Full article