Detection of muscle activities in the sEMG signal by using frequency features and adaptive decision threshold

Reliable detection of muscle activities from the surface electromyography (sEMG) signal is an important factor that makes the sEMG controlled orthotic devices a practical tool for assisting disabled people. In spite of the advantages of employing the sEMG signal as a control signal, the changes in the amplitude characteristics of this signal due to many factors and consequent variations in the required decision threshold may impede this control paradigm from being a reliable control method for such devices. Therefore, the performance of the algorithms intended to detect muscle activities should be immune against the involuntary amplitude variations of the sEMG signal. Moreover, the decision threshold value must be adaptive to the changes in the sEMG signal characteristics to reduce the number of false alarms that may arise with the fixed threshold and lead to unintended movements to these devices. In this paper, an amplitude-independent algorithm had been developed with an adaptive decision threshold; the algorithm employed only frequency features of the sEMG signal to detect muscle activities. These features are the previously developed Adaptive Zero Crossing feature and the new proposed Adaptive Wilson Amplitude feature. The Mean Instantaneous Frequency value of the sEMG signal was used as an adaptive decision threshold value to improve the detection performance and to minimize the number of false alarms produced with the utilization of inappropriate fixed decision threshold value. A comparison with an amplitude-independent algorithm that employed fixed decision threshold had revealed an improved performance regarding the resistance against false alarms.

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