Qiuxin Du

Jiaxiong Hu, Ruowen Niu, Qiuxin Du et al.

Neurocognitive disorders (NCDs), such as Alzheimer's disease, are globally prevalent and require scalable screening methods for proactive management. Prior research has explored the potential of technologies like conversational AI (CAI) to administer NCD screening tests. However, challenges remain in designing CAI-based solutions that make routine NCD screening socially acceptable, engaging, and capable of encouraging early medical consultation. In this study, we conducted interviews with 36 participants, including clinicians, individuals at risk of NCDs, and their caregivers, to explore the speculative future of adopting CAI for NCD screening. Our findings reveal shared expectations, such as deploying CAI in home or community settings to reduce social stress. Nonetheless, conflicts emerged among stakeholders, for example, users' need for emotional support may conflict with clinicians' preference for CAI's professional and standardized administration. Then, we look into the user journey of NCD screening based on the current practice of manual screening and the expected CAI-supported screening. Finally, leveraging the human-centered approach, we provide actionable implications for future CAI design in NCD screening.

Jiayi Zhao, Dongdong Weng, Qiuxin Du et al.

Human motion generation involves creating natural sequences of human body poses, widely used in gaming, virtual reality, and human-computer interaction. It aims to produce lifelike virtual characters with realistic movements, enhancing virtual agents and immersive experiences. While previous work has focused on motion generation based on signals like movement, music, text, or scene background, the complexity of human motion and its relationships with these signals often results in unsatisfactory outputs. Manifold learning offers a solution by reducing data dimensionality and capturing subspaces of effective motion. In this review, we present a comprehensive overview of manifold applications in human motion generation, one of the first in this domain. We explore methods for extracting manifolds from unstructured data, their application in motion generation, and discuss their advantages and future directions. This survey aims to provide a broad perspective on the field and stimulate new approaches to ongoing challenges.