Recognizing hand movements by using surface electromyography (sEMG) signal in real time and accurately is an important aspect of the application of sEMG signal. Hand movements onset detection from sEMG signals is the precondition of real-time hand movements recognizing. In this paper, The study aims at detecting the hand movement onset based on the consecutive sEMG signal. The sEMG signal is preprocessed by using the teager-kaiser energy (TKE) operator and a sEMG signal state binary function is designed to detect the hand movement onset from the consecutive sEMG signal. Then we design a heuristic filter according to the actual sEMG signal character. The detected results are filtered further by the heuristic filter which can cancel the effects of noise. We compare the application several methods for the simulation model. It proves the validity of TKE operator method. In the end, using sEMG signal acquisition and processing system for experimental verification, the results show that the detection method can rival action initiated for high precision of real-time detection.%实时且准确地识别手部动作，是表面肌电信号应用的重要方面，而通过其进行手部动作的发起检测是一个技术难点。为了解决这一问题，该文提出一种通过表面肌电信号进行手部动作发起检测的方法，将TKE算子应用于sEMG信号的预处理，通过设计二值化状态函数，并针对消除噪声对肌电信号的影响，提出启发式滤波策略。建立手部动作发起的表面肌电仿真模型，通过比对应用几种算子对仿真模型进行发起检测，证明TKE算子方法的有效性。利用肌电信号采集与处理系统进行实验验证。结果表明，该检测方法能够对手部动作发起进行高精度的实时检测。
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