A jerk-based algorithm ACCEL for the accurate classification of sleep-wake states from arm acceleration

Koji L Ode; Shoi Shi; Machiko Katori; Kentaro Mitsui; Shin Takanashi; Ryo Oguchi; Daisuke Aoki; Hiroki R Ueda

doi:10.1016/j.isci.2021.103727

A jerk-based algorithm ACCEL for the accurate classification of sleep-wake states from arm acceleration

iScience. 2022 Jan 1;25(2):103727. doi: 10.1016/j.isci.2021.103727. eCollection 2022 Feb 18.

Authors

Koji L Ode^{1

2}, Shoi Shi^{1

2}, Machiko Katori³, Kentaro Mitsui³, Shin Takanashi⁴, Ryo Oguchi⁴, Daisuke Aoki⁵, Hiroki R Ueda^{1

2

3}

Affiliations

¹ Department of Systems Pharmacology, Graduate School of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan.
² Laboratory for Synthetic Biology, RIKEN Center for Biosystems Dynamics Research, Suita, Osaka 565-0871, Japan.
³ Department of Information Physics and Computing, Graduate School of Information Science and Technology, The University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan.
⁴ Technology Strategy Office, Sony Mobile Communications Inc., Shinagawa-ku, Tokyo 140-0002, Japan.
⁵ Product Design Sec. 3, Product Design Department, Product Development Div., Sony Mobile Communications Inc., Shinagawa-ku, Tokyo 140-0002, Japan.

Abstract

Arm acceleration data have been used to measure sleep-wake rhythmicity. Although several methods have been developed for the accurate classification of sleep-wake episodes, a method with both high sensitivity and specificity has not been fully established. In this study, we developed an algorithm, named ACceleration-based Classification and Estimation of Long-term sleep-wake cycles (ACCEL) that classifies sleep and wake episodes using only raw accelerometer data, without relying on device-specific functions. The algorithm uses a derivative of triaxial acceleration (jerk), which can reduce individual differences in the variability of acceleration data. Applying a machine learning algorithm to the jerk data achieved sleep-wake classification with a high sensitivity (>90%) and specificity (>80%). A jerk-based analysis also succeeded in recording periodic activities consistent with pulse waves. Therefore, the ACCEL algorithm will be a useful method for large-scale sleep measurement using simple accelerometers in real-world settings.

Keywords: Chronobiology; Diagnostic technique in health technology; Health technology.