A signal-adaptive measurement matrix construction algorithm for compressed sensing of sEMG data

Abstract

Abstract Surface electromyography (sEMG), serving as a pivotal wearable technology, is a promising tool to assess and monitor muscle function. Yet, the efficacy of a sEMG system faces inevitable constraints, primarily stemming from the challenges of transmission and energy consumption induced by big data. Compressed sensing (CS) is a promising data acquisition solution that takes advantage of the signal sparseness in a particular basis to significantly reduce the number of samples. Current CS methods usually employ random or deterministic measurement matrix to compress sEMG signal. However, these measurement matrices do not integrate the signal feature, which limits the performance of these CS methods. To address this problem, this paper proposes an improved CS method for sEMG data compression. This proposed method introduces a measurement matrix construction algorithm to produce a deterministic matrix tailored for processing sEMG signals. The deterministic measurement matrix integrates the characteristics of the magnitudes of sEMG signals. The simulated signals and experimental signals acquired from the upper limb muscles of the stroke survivors were applied to evaluate the proposed CS method, with results showing that it achieves better reconstruction accuracy and robustness than the CS methods with other measurement matrices. The proposed method employing basis pursuit in the signal reconstruction presents better performance than that employing orthogonal matching pursuit. Hence, we can conclude that the proposed CS algorithm is of key importance for the popularization of sEMG in the wearable health monitoring devices.