CogAx: Early Assessment of Cognitive and Functional Impairment from Accelerometry

• Published in: Proceedings of the IEEE International Conference on Pervasive Computing and Communications pp. 66 - 76
• Main Authors: Ramamurthy, Sreenivasan Ramasamy, Chatterjee, Soumyajit, Galik, Elizabeth, Gangopadhyay, Aryya, Roy, Nirmalya, Mitra, Bivas, Chakraborty, Sandip
• Format: Conference Proceeding
• Language: English
• Published: IEEE 21.03.2022
• Subjects: Accelerometers; Activity recognition; Cognitive Impairment Assessment; Conferences; Contrastive Learning; Current measurement; Data collection; Functional; Multi-task learning; Multitasking; Wearable computers
• ISSN: 2474-249X
• DOI: 10.1109/PerCom53586.2022.9762401

An individual's cognitive and functional abilities are commonly assessed through physical and mental status examination, observational performance measures, surveys and proxy reports of symptoms. These strategies are not ideal for early impairment detection as the individual needs to be present physically at the clinic to avail the assessments, especially for older adults who require assistance from a caregiver, and experience mobility, cognitive and functional disabilities from neurodegenerative disorders. Moreover, these strategies rely on self-reporting and proxy reports for evaluation which often leads to under-reporting of symptoms and decrease the validity of these measures. We argue that an early assessment of functional, and cognitive health impairment can be obtained from the individual's daily activities captured through accelerometry. In this work, we postulate to learn high-level motion related representations from accelerometer data to better correlate with underlying functional and cognitive health parameters of older adults using a contrastive and multi-task learning framework. In particular, we posit a novel indicator, Impairment Indicator using the proposed multi-task learning framework that can indicate functional or cognitive decline as neurodegenerative disease progresses. An extensive 24-hour data collection from 25 older adults with the clinician in-the-loop was carried out in a retirement community center with IRB approval. We collected the activity patterns using wearables in their homes in addition to survey-based assessments and observational performance measures recorded by a clinical evaluator to infer their current cognitive and functional impairment status. Our evaluation on the acquired dataset reveals that the representations learned using contrastive learning aids in improving the detection of activities, activity performance score, and stage of dementia to 92%, 97%, and 98%, respectively.