Fingerprinting walking using wrist-worn accelerometers

Lily Koffman; Yan Zhang; Jaroslaw Harezlak; Ciprian Crainiceanu; Andrew Leroux

doi:10.1016/j.gaitpost.2023.05.001

Fingerprinting walking using wrist-worn accelerometers

Gait Posture. 2023 Jun:103:92-98. doi: 10.1016/j.gaitpost.2023.05.001. Epub 2023 May 4.

Authors

Lily Koffman¹, Yan Zhang², Jaroslaw Harezlak³, Ciprian Crainiceanu², Andrew Leroux⁴

Affiliations

¹ Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe St., Baltimore, MD, USA. Electronic address: lkoffma2@jh.edu.
² Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe St., Baltimore, MD, USA.
³ Department of Epidemiology and Biostatistics, Indiana University, 1025 E. 7th St, Bloomington, IN, USA.
⁴ Department of Biostatistics and Informatics, Colorado School of Public Health, 13001 East 17th Place, Aurora, CO, USA.

PMID: 37150053
DOI: 10.1016/j.gaitpost.2023.05.001

Abstract

Background: Identifying an individual from accelerometry data collected during walking without reliance on step-cycle detection has not been achieved with high accuracy.

Research question: We propose an open-source reproducible method to: (1) create a unique, person-specific "walking fingerprint" from a sample of un-landmarked high-resolution data collected by a wrist-worn accelerometer; and (2) predict who an individual is from their walking fingerprint.

Methods: Accelerometry data were collected during walking from 32 individuals (23-52 y.o., 19 females) for at least 380 s each. For this study's purpose, data are not landmarked, nor synchronized. Individual walking fingerprints were created by: (1) partitioning the accelerometer time series in adjacent, non-overlapping one-second intervals; (2) transforming all one-second interval data for a given individual into a three-dimensional (3D) image obtained by plotting each one-second interval time series by the lagged time series for a series of lags; (3) partitioning these resulting participant-specific 3D images into a grid of cells; and (4) identifying the combinations of cells (areas in the 3D image) that best predict the individual. For every participant, the first 200 s of data were used as training and the last 180 s as testing. This approach does not use segmentation methods for individual strides, which reduces dependence on complementary algorithms and increases its generalizability.

Results: The method correctly identified 100 % of the participants in the test data and highlighted unique features of walking that characterize the individuals.

Significance: Predicting the identity of an individual from their walking pattern has immediate implications that can complement or replace those of actual fingerprinting, voice, and image recognition. Furthermore, as walking may change with age or disease burden, individual walking fingerprints may be used as biomarkers of change in health status with potential clinical and epidemiologic implications.

Keywords: Accelerometry; Biometrics; Identification; Machine learning.

MeSH terms

Accelerometry / methods
Exercise*
Female
Humans
Walking
Wrist Joint
Wrist*