5 papers
Task-Centric Multimodal Learning for Cognitive Impairment Detection
Sankalp Vaish, Rishank Singh, Sumukh Veeramalla +3
Dementia affects millions of older adults worldwide, yet early detection remains difficult outside clinical settings. Traditional models primarily rely on text or voice-based respo…
CogDrift-4D: Hierarchical Multimodal Modeling of Temporal Behavioral Decay for MCI Detection
Sankalp Vaish, Sumukh Veeramalla, Rishank Singh +3
Early detection of mild cognitive impairment remains a critical bottleneck in dementia care, demanding scalable tools capable of capturing subtle behavioral decay. To capture the s…
Cog-TiPRO: Iterative Prompt Refinement with LLMs to Detect Cognitive Decline via Longitudinal Voice Assistant Commands
Kristin Qi, Youxiang Zhu, Caroline Summerour +2
Early detection of cognitive decline is crucial for enabling interventions that can slow neurodegenerative disease progression. Traditional diagnostic approaches rely on labor-inte…
Analyzing Multimodal Features of Spontaneous Voice Assistant Commands for Mild Cognitive Impairment Detection
Nana Lin, Youxiang Zhu, Xiaohui Liang +2
Mild cognitive impairment (MCI) is a major public health concern due to its high risk of progressing to dementia. This study investigates the potential of detecting MCI with sponta…
Exploiting Longitudinal Speech Sessions via Voice Assistant Systems for Early Detection of Cognitive Decline
Kristin Qi, Jiatong Shi, Caroline Summerour +2
Mild Cognitive Impairment (MCI) is an early stage of Alzheimer's disease (AD), a form of neurodegenerative disorder. Early identification of MCI is crucial for delaying its progres…