Predicting Cognitive Assessment Scores in Older Adults with Cognitive Impairment Using Wearable Sensors

TL;DR

This study demonstrates that wearable sensors combined with AI can accurately and noninvasively predict specific cognitive assessment scores in older adults with cognitive impairment, enabling continuous remote monitoring.

Contribution

It introduces a novel approach using physiological data from wearables and supervised learning to predict cognitive test scores in older adults with impairment.

Findings

Strong correlation between sensor data and cognitive scores (Spearman's ρ 0.73-0.82)

Different sensor combinations optimize prediction for specific cognitive domains

Outperforms naive mean predictor in accuracy

Abstract

Background and Objectives: This paper focuses on using AI to assess the cognitive function of older adults with mild cognitive impairment or mild dementia using physiological data provided by a wearable device. Cognitive screening tools are disruptive, time-consuming, and only capture brief snapshots of activity. Wearable sensors offer an attractive alternative by continuously monitoring physiological signals. This study investigated whether physiological data can accurately predict scores on established cognitive tests. Research Design and Methods: We recorded physiological signals from 23 older adults completing three NIH Toolbox Cognitive Battery tests, which assess working memory, processing speed, and attention. The Empatica EmbracePlus, a wearable device, measured blood volume pulse, skin conductance, temperature, and movement. Statistical features were extracted using wavelet-based and segmentation methods. We then applied supervised learning and validated predictions via cross-validation, hold-out testing, and bootstrapping. Results: Our models showed strong performance with Spearman's \rho of 0.73-0.82 and mean absolute errors of 0.14-0.16, significantly outperforming a naive mean predictor. Sensor roles varied: heart-related signals combined with movement and temperature best predicted working memory, movement paired with skin conductance was most informative for processing speed, and heart in tandem with skin conductance worked best for attention. Discussion and Implications: These findings suggest that wearable sensors paired with AI tools such as supervised learning and feature engineering can noninvasively track specific cognitive functions in older adults, enabling continuous monitoring. Our study demonstrates how AI can be leveraged when the data sample is small. This approach may support remote assessments and facilitate clinical interventions.