Stabilization of Signal Decomposition Based on Frequency Entrainment Phenomena

With advancements in the miniaturization and lightweight design of computers and electronic devices, wearable sensors are now widely utilized in fields such as healthcare and medicine. Signals obtained from wearable sensors often contain a mix of biological signals and noise. Typically, wearable sensor measurements focus on a single signal of interest (SoI), treating other signals as noise. While methods for separating multiple signals exist, the stable tracking of frequency variations during signal separation remains an unresolved challenge. Biological signal and human motion measurements often face issues such as noise, temporal disconnections, dropouts, and frequency variations. To address these challenges, we developed a method that can stably separate and extract SoI from measurement data. We demonstrated the effectiveness of the proposed method through simulations replicating common measurement issues. By applying the method, we show that SoI frequency estimates can be obtained with a high accuracy. Furthermore, we confirm that the method can separate multiple SoIs from a single measurement dataset, highlighting its utility. Finally, we validate that the proposed method can reliably extract multiple SoIs, such as heart rate, walking rhythm, and breathing rate, from actual data measured using stretch sensors, achieving results consistent with simulations.