Abstract This study introduced a groundbreaking approach to address the pressing public health challenges of obesity management and its associated health implications. By establishing a clear link between obesity and various health issues, this study underscores the critical need for effective interventions. Our team developed a pioneering digital therapeutic tool through the application of advanced 3D artificial intelligence technologies. This innovative solution offers a dynamic visual representation of weight loss and health enhancement journeys for individuals with obesity. By providing a platform for users to monitor their progress in real time, digital therapeutics (DTx) foster deeper engagement and strengthen motivation towards health goals. The experimental results showed that the digital therapeutic received high scores in terms of usability, effectiveness, predictiveness and personalization, user satisfaction, and continuous usage and adherence. These findings suggest that DTx can be a valuable tool for the management and treatment of obesity. The effectiveness of this digital approach was thoroughly assessed from multiple dimensions, showing its significant potential and effectiveness in obesity management. These findings advocate ongoing research in this area, projecting that the continuous evolution of DTx will have a profound positive impact on both personal and public health outcomes.

Abstract Increasingly, research has analyzed human behavior in various fields. The fourth industrial revolution technology is very useful for analyzing human behavior. From the viewpoint of the residential space monitoring system, the life patterns in human living spaces vary widely, and it is very difficult to find abnormal situations. Therefore, this study proposes a living space-based monitoring system. The system includes the behavioral analysis of monitored subjects using a deep learning methodology, behavioral pattern derivation using the PrefixSpan algorithm, and the anomaly detection technique using sequence alignment. Objectivity was obtained through behavioral recognition using deep learning rather than subjective behavioral recording, and the time to derive a pattern was shortened using the PrefixSpan algorithm among sequential pattern algorithms. The proposed system provides personalized monitoring services by applying the methodology of other fields to human behavior. Thus, the system can be extended using another methodology or fourth industrial revolution technology.

The shape of the human body plays an important role in many applications, such as those involving personal healthcare and virtual clothing try-ons. However, accurate body shape measurements typically require the user to be wearing a minimal amount of clothing, which is not practical in many situations. To resolve this issue using deep learning techniques, we need a paired dataset of ground-truth naked human body shapes and their corresponding color images with clothes. As it is practically impossible to collect enough of this kind of data from real-world environments to train a deep neural network, in this paper, we present the Synthetic dataset of Human Avatars under wiDE gaRment (SHADER). The SHADER dataset consists of 300,000 paired ground-truth naked and dressed images of 1,500 synthetic humans with different body shapes, poses, garments, skin tones, and backgrounds. To take full advantage of SHADER, we propose a novel silhouette confidence measure and show that our silhouette confidence prediction network can help improve the performance of state-of-the-art shape estimation networks for human bodies under clothing. The experimental results demonstrate the effectiveness of the proposed approach. The code and dataset are available athttps://github.com/YCL92/SHADER.