In order to improve the effect of muscle fatigue detection, a dual-sensor fusion method is proposed to make up for the shortcoming that information is easily lost in single-sensor mode. The method realizes a new dual-sensor fatigue detection mode by integrating the time-frequency domain features of the surface EMG signal with the muscle thickness feature of the A-type ultrasound signal in multiple dimensions. Using support vector machine and neural network multi-model training, the detection accuracy of surface EMG and A-type ultrasonic dual-sensor fusion in three fatigue states can reach 85%. Compared with using only the time-frequency domain features of surface EMG signals ( 76. 99%) and the muscle thickness of A-mode ultrasound ( 74. 87%) for fatigue detection, the accuracy is increased by 8% ~ 13%. For fatigue detection, the results show that the dual-sensing fusion mode of surface EMG signal and ultrasonic signal is more accurate and effective than the single-sensing mode.