Toward assessing functional decline in mild cognitive impairment using wearable sensors and explainable machine learning.

Objectives: Mild cognitive impairment (MCI) poses serious threats to health, safety, and independence in older adults by elevating fall risk and undermining the ability to complete essential tasks. Progression from MCI to dementia is common but often underdetected until significant decline has occurred. Functional decline serves as a proxy for cognitive decline, yet current assessments lack the ecological validity to capture real-world performance in instrumental activities of daily living (IADLs). This work investigates methods for detecting IADL subtasks as a foundation for longitudinal monitoring of cognitive and functional health. Methods: We developed an ecologically valid grocery shopping task in which 26 older adults (12 with MCI, 14 without), instrumented with inertial sensors, took part. Subtask detection was performed using an interpretable deep learning framework trained on pooled data. Shapley Additive Explanations (SHAP) were used to interpret detection performance across subtask categories. Results: Detection performance depended on subtask granularity: the framework excelled at identifying broad movement categories such as Walk and Turn but struggled with more granular subtasks, including discrete actions with ambiguous signatures and differentiating between similar subtasks like reaching to different heights. Model explanations revealed that detection errors were driven by overlapping motion signatures. Conclusion: SHAP analysis revealed that orientation angles, particularly yaw and roll dominated classification. The cognitively unimpaired and MCI groups differed in how these features were weighted, suggesting population-specific motor signatures that may serve as candidate digital biomarkers for early functional decline.