Walking Fingerprinting Using Wrist Accelerometry During Activities of Daily Living in NHANES
Lily Koffman, John Muschelli III, Ciprian Crainiceanu

TL;DR
This study introduces a novel wrist accelerometry-based method for individual identification during daily activities, leveraging walking patterns and image transformation techniques on large-scale NHANES data.
Contribution
The paper presents a new approach combining ADEPT and image transformation for walking fingerprinting, applied to a large, unlabeled, real-world dataset.
Findings
High accuracy in participant identification, with 96% in top 1% predictions.
Method effective on large, heterogeneous, unlabeled dataset.
Demonstrates feasibility of individual identification using wrist accelerometry in free-living conditions.
Abstract
We propose a method for identifying individuals based on their continuously monitored wrist-worn accelerometry during activities of daily living. The method consists of three steps: (1) using Adaptive Empirical Pattern Transformation (ADEPT), a highly specific method to identify walking; (2) transforming the accelerometry time series into an image that corresponds to the joint distribution of the time series and its lags; and (3) using the resulting images to construct a person-specific walking fingerprint. The method is applied to 15,000 individuals from the National Health and Nutrition Examination Survey (NHANES) with up to 7 days of wrist accelerometry data collected at 80 Hertz. The resulting dataset contains more than 10 terabytes, is roughly 2 to 3 orders of magnitude larger than previous datasets used for activity recognition, is collected in the free living environment, and…
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Taxonomy
TopicsGait Recognition and Analysis · Context-Aware Activity Recognition Systems · Balance, Gait, and Falls Prevention
