Automatic mild cognitive impairment estimation from the group conversation of coimagination method

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Abstract
The coimagination method (Otake, 2009) is designed to prevent dementia in individuals with mild cognitive impairment (MCI) by utilizing the brain’s natural processes. This method involves participants sharing their thoughts and feelings through group conversations centered around shared photos. The coimagination method contains two phases: (1) each participant talk about their memories and experiences related to the photos they bring, and (2) other participants ask questions about the photos. Automating the MCI estimation could be helpful for assisting individuals with MCI during coimagination. However, previous MCI estimation methods rarely focused on group conversation scenarios, despite the potential of multimodal features observed in these scenarios in revealing cognitive states. This study focuses MCI individuals defined by cognitive test scores (e.g., Mini-Mental State Examination (MMSE)). We explore MCI estimation from three aspects. First, we clarify whether MCI can be effectively estimated by constructing estimation models using linguistic and acoustic features from coimagination sessions. Second, we evaluate the impact of using data from the two distinct phases, as they may activate participants’ cognitive functions differently. Finally, we analyze the effects of incorporating subtasks including participants’ conversational customary and engagement level during coimagination via multitask learning. The experimental results demonstrated that individuals with MCI can be effectively estimated from group conversations from coimagination, with the highest macro F1 score of 0.693. The results also demonstrated that the performance improved when using data from the phase that highly activates cognitive functions and when considering conversation customary as a subtask.

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