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
Affiliations
Koji L. Ode
Department of Systems Pharmacology, Graduate School of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan; Laboratory for Synthetic Biology, RIKEN Center for Biosystems Dynamics Research, Suita, Osaka 565-0871, Japan
Shoi Shi
Department of Systems Pharmacology, Graduate School of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan; Laboratory for Synthetic Biology, RIKEN Center for Biosystems Dynamics Research, Suita, Osaka 565-0871, Japan
Machiko Katori
Department of Information Physics and Computing, Graduate School of Information Science and Technology, The University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan
Kentaro Mitsui
Department of Information Physics and Computing, Graduate School of Information Science and Technology, The University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan
Shin Takanashi
Technology Strategy Office, Sony Mobile Communications Inc., Shinagawa-ku, Tokyo 140-0002, Japan
Ryo Oguchi
Technology Strategy Office, Sony Mobile Communications Inc., Shinagawa-ku, Tokyo 140-0002, Japan
Daisuke Aoki
Product Design Sec. 3, Product Design Department, Product Development Div., Sony Mobile Communications Inc., Shinagawa-ku, Tokyo 140-0002, Japan
Hiroki R. Ueda
Department of Systems Pharmacology, Graduate School of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan; Laboratory for Synthetic Biology, RIKEN Center for Biosystems Dynamics Research, Suita, Osaka 565-0871, Japan; Department of Information Physics and Computing, Graduate School of Information Science and Technology, The University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan; Corresponding author
Summary: 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.
