Wearable sensing for quantifying cognitive and balance functions in naturalistic movements of older adults with mild cognitive impairment in therapeutic environments
Discuss this preprint
Start a discussion What are Sciety discussions?Listed in
This article is not in any list yet, why not save it to one of your lists.Abstract
Mild cognitive impairment (MCI) is a clinically important stage preceding Alzheimer’s disease and related dementias, in which cognitive and balance functions are commonly evaluated using standard clinical assessments such as the Montreal Cognitive Assessment (MoCA) and Mini-Balance Evaluation Systems Test (Mini-BESTest). These assessments are administered episodically by clinicians and may miss functional changes during everyday movement. Recent studies and prior work in the Charlie and Harriet Shaffer Cognitive Empowerment Program (CEP), a therapeutic environment supporting lifestyle intervention and naturalistic social interaction, suggest that wearable and passive behavioral sensing can monitor movement patterns associated with cognitive and balance function in older adults with MCI. However, it remains unclear whether passive waist-mounted IMU data collected during naturalistic movement and social interaction can quantify clinician-rated cognitive and balance outcomes, particularly at the subdomain level, in an interpretable and demographically fair manner. To address this gap, we analyzed weekly IMU recordings collected over 6 months from 44 older adults with MCI in the CEP and trained tree-based ensemble regression models to estimate MoCA and Mini-BESTest total and subdomain scores, with interpretability and demographic fairness evaluation. Our models achieved RMSEs of 3.677 for MoCA and 3.672 for Mini-BESTest, benchmarked against Minimal Detectable Change and Minimal Clinically Important Difference thresholds. Feature importance analysis showed distinct movement signal properties across assessments, with general movement intensity features most informative for MoCA and temporal gait features led by cadence most informative for Mini-BESTest. Demographic bias analysis identified sex-related model bias, mitigated through post-processing while maintaining performance. This study supports the feasibility of wearable-based estimation of clinical assessment scores in older adults with MCI during naturalistic activity, with comparable performance between sexes after bias mitigation. This advances the validation of passive sensing for home monitoring to support clinical decision-making and personalized interventions.
Author summary
Mild cognitive impairment (MCI) is an early stage of cognitive decline that may precede Alzheimer’s disease and related dementias. Cognitive and balance changes are usually evaluated during clinical visits, but clinical assessments may miss functional changes that occur during everyday movement. In this study, we examined whether waist-worn motion sensors could be used to estimate standard cognitive and balance assessment scores in older adults with MCI during naturalistic activity in a therapeutic program. We used machine learning models to analyze movement patterns collected over six months and evaluated whether the models could estimate total and subdomain scores from the Montreal Cognitive Assessment and Mini-Balance Evaluation Systems Test. We also examined which movement features were most informative, whether estimations differed by sex or race, and whether post-processing bias mitigation reduced these differences. Our findings suggest that passive wearable sensing may provide useful information about cognitive and balance function of older adults with MCI in naturalistic settings. With further validation, this approach could support future monitoring and triage tools by helping identify individuals with MCI who exhibit concerning movement or balance changes and may benefit from more detailed clinical evaluation.
