Vibol Yem

Conventional footbaths promote relaxation but require water management and hygiene control, limiting portability and reproducibility. We propose a waterless virtual footbath system that reproduces immersion-like sensations using airflow-based tactile stimulation and controlled thermal contrast in virtual reality (VR). A membrane-guided airflow structure promotes surface-conforming flow around the feet, while a vertical thermal gradient simulates the boundary sensation of warm-water immersion. An exploratory within-subject study (n = 8) compared four conditions combining airflow and thermal stimulation. Subjective ratings (presence, comfort, discomfort) and physiological measures (heart rate and HRV) were recorded. Stimulation conditions were associated with reductions in heart rate relative to visual-only presentation, with effects depending on the combination of sensory stimuli. While airflow and thermal stimulation each reduced heart rate when presented individually, their combination did not produce an additional reduction. The combined airflow and thermal condition yielded the high frquency presence and comfort ratings without significantly increasing discomfort. These findings demonstrate the feasibility of reproducing footbath-like experiences without water and highlight the potential of airflow-based multisensory VR for relaxation-oriented applications.

Controlling multiple agents in parallel is an important challenge in human–robot interaction, with growing applications in remote collaboration, telepresence, and multi-agent coordination. However, empirical work on distributed embodiment remains limited, particularly in scenarios where independently controlled avatars operate in distinct environments. To address this gap, we propose the concept of Dual Body Sensation (DBS): a novel theoretical construct of distributed embodiment, describing a state in which a single operator simultaneously experiences embodiment for two independent robots in distinct environments during their control. We conducted a within-subject experiment to investigate the cognitive feasibility of DBS using a 2×3 factorial design that varied visual presentation (independent view, superimposed view) and tactile presentation (no tactile, bilateral tactile, unified tactile). The results indicated that DBS emerged across all conditions and was significantly enhanced under the unified tactile condition. The combination of the superimposed view and unified tactile conditions also increased the sense of body ownership and the sense of agency, while the superimposed view condition reduced cybersickness and cognitive load. These findings provide empirical support for DBS and inform the design of future multi-agent interaction systems that support flexible distribution of bodily control and awareness.

In the past decade, there has been a significant increase in the growth of the global technology employment market, primarily owing to the digital age, artificial intelligence expansion, cloud computing, and cybersecurity requirements. Consequently, the demand for IT professionals has increased. Introductory programming is a crucial early course in IT higher education as it aids in developing problem-solving skills. However, such courses tend to result in substantial failure and attrition rates. Some studies have proposed solutions such as peer learning and personalized feedback, but challenges persist. One key assumption is that teacher support is likely to help mitigate these rates. However, with limited class time, teachers find it challenging to support all the students. This study aims to automatically assess the programming logic understanding level (PLUL) during introductory programming courses. We proposed a method for classifying PLUL through a block-coding learning platform based on mouse-tracking heatmaps using machine learning techniques. We collected actual learning-task data from first-year university students and compared the performance of five machine learning models. The decision tree algorithm achieved an accuracy score of 76.00%, F1 score of 77.36%, sensitivity score of 75.93%, and specificity score of 76.09%. These findings demonstrate that our method can effectively classify students based on their PLUL of tasks on a block coding learning platform.

Electrotactile stimulation provides tactile feedback by directly stimulating tactile nerves with electrical currents. However, variations in individual skin impedance hinder the practical application of this technology, and developing an algorithm to accurately estimate the sensation threshold in real-time remains challenging. In this study, we explored four methods to predict the electrotactile sensation threshold across all five fingers. First, we measured the correlation between skin impedance (resistance R and capacitance C) and the sensation threshold. Second, we applied Random Forest regression using R and C related data as inputs. Third, we employed an LSTM (Long Short-Term Memory) neural network with an attention mechanism, trained on raw voltage waveforms during charging and discharging phases at the detectable sensation threshold (LSTM-1). Fourth, similar to the third method, we trained the model on waveforms recorded at both detectable and undetectable sensation thresholds (LSTM-2). The results indicate that the LSTM-2 model, enhanced with attention, outperformed the other methods, achieving an R² of 0.910 and a mean squared error (MSE) of 0.661 mA. By utilizing voltage data from both charging and discharging phases, the LSTM-2 model captured the dynamic properties of skin impedance more effectively than methods relying solely on resistance and capacitance measurements. Furthermore, the LSTM-2 model provided stable tactile feedback over a 20-minute continuous stimulation period, maintaining consistent perception intensity and adapting to changes in skin impedance. Our results demonstrate that LSTM-based models can enhance the accuracy of current threshold estimation and provide stability during long-term stimulation, reducing the need for manual recalibration. The results also suggest that voltage waveforms during the discharging phase and at undetectable thresholds play a critical role in improving stimulation accuracy and should not be overlooked.