With increasing public attention to health and wellness, treadmill exercise in gyms has become increasingly popular. However, the risk of falling due to loss of balance during treadmill use remains a significant safety concern. To address this issue, we propose a gait stability prediction framework that leverages a deep learning neural network BiLSTM-KAN trained on multimodal data. Specifically, we use MediaPipe to extract 3D skeletal key points and smart insoles to capture essential plantar features and then perform data preprocessing to synchronize the postural features with the plantar data. The trained model is capable of predicting 15 biomechanical indicators related to foot stability based on pose data obtained from a camera, thereby enabling indirect assessment of fall risk without requiring the user to wear any devices during use. Experimental results demonstrate that the proposed system achieves high accuracy in predicting key stability-related parameters such as Gait-Line CPE and Impulse. This study validates the feasibility of a non-contact, continuous monitoring approach based on multimodal fusion for gait stability assessment, providing a practical solution for safer exercise and intelligent health monitoring.

Rearview mirrors provide critical driving information, especially in the process of departing from a parking space and merging onto the main road (DPMM condition). However, existing research has not sufficiently explored how the interaction between driving experience and gender influences rearview mirror observation behavior during this process. This study investigates real-world driver observation behavior toward rearview mirrors. Eye movement data were collected from 10 participants using Tobii Pro Glasses 3 while they performed vehicle departure and merging tasks on a closed road under the DPMM condition. Fixation Count (FC) was used as the primary evaluation metric. Results from a two-way ANOVA revealed a significant interaction effect between driving experience and gender for the right-side mirror. Novice female drivers exhibited a higher fixation frequency, whereas experienced male drivers showed increased fixation counts with more driving experience. These findings suggest that gender and driving experience interact to influence mirror-checking strategies under the DPMM condition. By addressing individual differences in observation behavior, this study contributes to personalized driver training and improved road safety.

When language barriers exist, sketching as a form of non-verbal visual expression can facilitate cross-cultural communication. However, many people lack sketching skills, especially the ability to quickly convey visual ideas. To address this, we conducted experiments collecting eye movement data from individuals with varying sketching experience as they imagined and drew object shapes. Based on this data, we built a predicting model using a Bidirectional Long Short-Term Memory network (Bi-Lstm) and a Kolmogorov-Arnold Networks (KAN). The model achieved a validation loss of 0.0152, an accuracy of 0.9667, and an F1 score of 0.9634, demonstrating strong classification performance and generalization on imbalanced data. This study integrates Deep Learning technology with artistic expression, offering intelligent feedback tools for sketch learners and helping non-professionals improve visual communication. Additionally, it provides new insights for art education, cross-cultural interaction, and visual cognition research.

In recent years, the global aging population has intensified, leading to a sharp increase in social security benefits and caregiving costs. The elderly face greater health risks, and their behaviors often indicate signs of crises and illnesses. The rapid development of IoT technology offers new solutions for detecting abnormal signs, thereby promoting healthy aging, independent living, and social participation for the elderly. However, the diversity of IoT devices has led to the phenomenon of personal data silos. When the elderly leave their usual IoT environment, the issue of continuity in healthcare services becomes more pronounced. To address this issue, this study proposes a decentralized Ubi-Care framework. The framework aims to achieve complete separation of data and applications. It is based on the ActivityPub protocol and integrates various data from wearable devices, smart home sensors, and social networks, storing this data in a ubiquitous personal online data store (UPOD) and assigning different roles based on data type. UPOD applications support bidirectional following, allowing users to access data associated with relevant roles, addressing issues related to data categorization, sharing, privacy, and security. Additionally, this study proposes a method for utilizing complete UPOD data to perform anomaly detection based on the hidden Markov model (HMM). By effectively integrating IoT data into UPOD and enhancing data interoperability. The data-sharing model proposed in this study also facilitates elderly individuals and their family members in sharing relevant data as needed, while ensuring privacy protection. The methods proposed in this study will help prevent accidental injuries and enable early diagnosis of diseases, providing strong technical support for elderly healthcare.

In daily driving, reverse parking into a garage often leads to collision accidents. However, current studies mostly focus on analyzing drivers' eye movements while using a certain specific parking style, lacking comparative research on different parking behaviors. In this study, 200 experiments were conducted with 20 participants of varying driving experience to collect their eye movement data during different types of reverse parking into the garage. Based on the collected eye-tracking data, we try to analyze how driving experience impacts reverse parking behaviors. The findings shown that reveal significant differences in gaze behavior and fixation positions between novice and experienced drivers. Novice drivers exhibit more erratic gaze patterns, focusing more on the right door mirror and frequently shifting their gaze between areas of interest. To be specific, in situation A (entering the garage from the right side), novice drivers feel insecure due to their inability to visually assess road conditions directly, prompting them to rely more on the right door mirror. On the other hand, in situation B (entering the garage from the left side), reliance on interior mirrors is reduced for both novice and experienced drivers. Insights from these findings could enhancing overall driving safety and efficiency.

Abstract Previous studies have shown that about 90% of traffic accidents are due to human error, which means that human factors may affect a driver's braking behaviors and thus their driving safety, especially when the driver makes a braking motion. However, most studies have mounted sensors on the brake pad, ignoring to some extent an analysis of the driver's behavior before the brake pad is pressed (pre-braking). Therefore, to determine the effect of different human factors on drivers' pre-braking behaviors, this study focused on analyzing drivers' local joints (knee, ankle, and toe) by a motion capture device. A Hilbert–Huang Transform (HHT)-based local human body movement analysis method was used to decompose the realistic complex pre-braking actions into sub-actions such as intrinsic mode functions (IMF1, IMF2, etc.). Analysis of the results showed that IMF1 is a common and necessary action when pre-braking for all drivers, and IMF2 may be the safety assurance action that allows right-foot transverse movement at the beginning part of the pre-braking process. We also found that the experienced, male, and Phys.50 groups may have consistent characteristics in the HHT scheme, which could mean that such drivers would have better performance and efficiency during the pre-braking process. The results of this study will be useful in decomposing and discerning the specific actions that lead to accidents, providing insights into driver training for novice drivers, and guiding the construction of daily automated driver assistance or accident prevention systems (advanced driver assistance systems, ADASs).

In mobile crowdsensing activities, it is usually necessary for participants to upload sensing data and related locations. The existing location privacy-preserving mechanisms cannot well protect a user's trajectory privacy because attackers can mine the user's trajectory features through data analysis techniques. Aiming at the trajectory privacy protection problem, this article proposes a differential location privacy-preserving mechanism based on trajectory obfuscation (LPMT). LPMT first extracts the stay points as the features of a trajectory based on the sliding window algorithm, and then obfuscates each stay point to a target obfuscation subregion through the exponential mechanism, and finally performs the Laplace sampling in the target obfuscation subregion to obtain the obfuscated GPS points. Compared with the baseline mechanisms, LPMT can reduce data quality loss by more than 20% while providing the same level of obfuscation quality, which indicates that LPMT has the advantages of strong security and high quality of service.

The driver's driving experience is one of the important factors affecting his or her behaviors. Prior studies have noted the effect of driving experiences on drivers' eye-movements when right-turning. However, related studies usually focused on the accident scenarios, for drivers' eye-movements on daily driving when facing right-turn, the effects of drivers' experience remain unknown. Therefore, according to design and apply a set of experiments, this paper focused on the analysis of driver's eye-movements during the daily right-turn task to compare the differences between Experienced and Novice drivers. A total of 10 drivers were invited and be classified into two groups (Experienced and Novice) to participate in the experiments. All of the drivers are driving on the right-hand side of the road, and the steering wheel is on the left side of the vehicle. The aimed data were collected by a set of glasses type eye-tracker and be further classified into four driving vision-based AOI (Areas of Interest). The results of Mann-Whitney U-tests showed that Novice drivers have a disordered line of sight and tend to spend more attention on their right view and switch their line of sight back and forth between the AOIs. Moreover, Experienced drivers more tend to keep their view directly in front of their heads instead of using the peripheral vision. The results of this study may provide guidelines to prevent accidents in Advanced Driver Assistance Systems (ADAS) and offers useful insights for the training of new drivers.

Given the prevalence of loyalty programs' implementation in service industries, in order to create a difference in the eyes of the customer from other competitors, examining new loyalty program designs become more and more important for most firms. Compare with Zhang and Breugelmans' research of the item-based loyalty program (IBLP), this research studies a more complicated IBLP design, in which customers can earn different extra points for purchases made on different items. The main purpose of this research is to examine the short-term impact of items with different points in this new IBLP design on different types of customers' purchase behavior. Using data from a Japanese grocery store chain, this study shows that those customers who were heavy customers at the beginning of the IBLP are more affected by this new IBLP design. Then, instead of higher-point items, a middle-level-point, 25-point items has the highest impact on customers' purchase behavior. These findings suggest this special tactic can enhance the value of firm's loyalty program, and help managers to further improve the effect of the IBLP by arranging more targeted items to different types of customers.

While social computing related research has focused mostly on how to provide users with more precise and direct information, or on recommending new search methods to find requested information rapidly, the authors believe that network users themselves could be viewed as an important social resource. This study concentrates on analyzing potential and dynamic user correlations, based on topic-aware similarity and behavioral influence, which may help us to discover communities in social networking sites. The dynamically socialized user networking (DSUN) model is extended and refined to represent implicit and explicit user relationships in terms of topic-aware features and social behaviors. A set of measures is defined to describe and quantify interuser correlations, relating to social behaviors. Three types of ties are proposed to describe and discover communities according to influence-based user relationships. Results of the experiment with Twitter data are used to show the discovery of three types of communities, based on the presented model. Comparison with six different schemes and two existing methods demonstrates that the proposed method is effective in discovering influence-based communities. Finally, the scenario-based simulation of collective decision-making processes demonstrates the practicability of the proposed model and method in social interactive systems.