Software Implementation of Real-Time EMD-Based Algorithm in Embedded Microprocessors for Wearable Devices

Empirical mode decomposition (EMD) is a nonlinear, data-dependent method that decomposes a nonstationary signal into a few oscillation modes, which are referred to as intrinsic mode functions (IMFs). However, EMD often suffers from mode-mixing phenomena. Therefore, many disturbance-assisted EMD (DA-EMD) algorithms, such as ensemble EMD (EEMD) and uniform phase EMD (UPEMD), have been developed to resolve this problem. DA-EMD algorithms are powerful signal-processing tools with widespread use in numerous applications. In particular, many emerging industrial or biomedical applications require measuring features by the decomposition of a signal using DA-EMD algorithms in an online, real-time embedded microprocessor (MCU) of a wearable or standalone device. However, due to the high computational and memory costs of DA-EMD algorithms, it is difficult to implement a DA-EMD algorithm using a pure software approach in a resource-limited MCU. Moreover, the quality of the IMFs must be maintained. Computation latency and power consumption must be minimized as well. In this article, we combine fast EMD and the recently developed low-memory EMD (LMEMD) implementation to significantly reduce the run time and memory to meet the real-time constraints. We also develop the adaptive phase number UPEMD to further reduce the run time of UPEMD. In addition, we develop a theory to estimate the relationship between data latency and boundary error so that we can determine the required data latency within the accepted boundary error. Different DA-EMD algorithms are realized in a wearable system to extract clean electrocardiogram (ECG) signals and ECG-derived respiration (EDR) for demonstration. The performances of different DA-EMD algorithms are also compared. The proposed software approach has the advantage of being able to be implemented on various existing embedded platforms as an available and practical signal decomposition solution for edge computing.