Methods Inf Med 2022; 61(03/04): 099-110
DOI: 10.1055/s-0042-1756649
Original Article

Automated Cognitive Health Assessment Using Partially Complete Time Series Sensor Data

Brian L. Thomas
1   School of Electrical Engineering and Computer Science, Washington State University, Pullman, Washington, United States
,
Lawrence B. Holder
1   School of Electrical Engineering and Computer Science, Washington State University, Pullman, Washington, United States
,
Diane J. Cook
1   School of Electrical Engineering and Computer Science, Washington State University, Pullman, Washington, United States
› Author Affiliations
Funding This work is supported in part by National Institutes of Health grant R41EB029774.

Abstract

Background Behavior and health are inextricably linked. As a result, continuous wearable sensor data offer the potential to predict clinical measures. However, interruptions in the data collection occur, which create a need for strategic data imputation.

Objective The objective of this work is to adapt a data generation algorithm to impute multivariate time series data. This will allow us to create digital behavior markers that can predict clinical health measures.

Methods We created a bidirectional time series generative adversarial network to impute missing sensor readings. Values are imputed based on relationships between multiple fields and multiple points in time, for single time points or larger time gaps. From the complete data, digital behavior markers are extracted and are mapped to predicted clinical measures.

Results We validate our approach using continuous smartwatch data for n = 14 participants. When reconstructing omitted data, we observe an average normalized mean absolute error of 0.0197. We then create machine learning models to predict clinical measures from the reconstructed, complete data with correlations ranging from r = 0.1230 to r = 0.7623. This work indicates that wearable sensor data collected in the wild can be used to offer insights on a person's health in natural settings.



Publication History

Received: 20 January 2022

Accepted: 06 July 2022

Article published online:
11 October 2022

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