Entropy Based Fatigue Identification in Spinal Surface Electromyographic Signals using Wavelets

Abstract

There are two general areas where the detection and evaluation of muscle fatigue are important. The first one is socioeconomic and it is related to ergonomics (human workload in factories, workman compensation, insurance, etc.) and the second one is the study of the musculoskeletal disorders. In both areas, the understanding of the characteristics of the surface electromyographic (SEMG) signal is necessary before muscle evaluation criteria will be developed and supporting devices can be designed. Up to date, spectral shift changes of the SEMG signal have been used by researchers to detect muscle fatigue during the performance of a specific static task. More specifically, two measures of the power spectrum have been investigated, the mean (MNF) and the median (MDF) frequency. It was observed that although both MNF and MDF are shifted toward the lower frequency region of the spectrum (during the task performance), the shifts were inconsistently subject sensitive. From the two methods, MDF is less sensitive to noise thus it is the preferred method. These finding raised questions about the usage of MNF and MDF as muscle fatigue indicators. Other researchers have used another method known as the zero crossings (ZC) method. A ZC is defined as an event when a signal changes polarity. In practice, it is difficult to obtain the exact number of ZCs especially if the amplitude of the background noise is higher than that of the SEMG signal.