Department of Human Movement and Sport Sciences, University of Rome Foro Italico, Piazza Lauro de Bosis 6, Rome, Italy.
J Electromyogr Kinesiol. 2010 Aug;20(4):767-72. doi: 10.1016/j.jelekin.2010.02.007. Epub 2010 Mar 29.
The problem of the identification of the muscle contraction timing by using surface electromyographic signal is addressed. The timing detection of the muscular activation in dynamic conditions has a real clinical diagnostic impact. Widely used single threshold methods still rely on the experience of the operator in manually setting that threshold. A new approach to detect the muscular activation intervals, that is based on discontinuities detection in the wavelet domain, is proposed. Accuracy and precision of the algorithm were assessed by using a set of simulated signals obtaining values lower than 11.0 and 8.7 ms for biases and standard deviations of the estimation, respectively. Moreover an experimental application of the algorithm was carried out recruiting a population of 10 able-bodied subjects and processing the myoelectric signals recorded from the lower limb during an isokinetic exercise. The algorithm was able to reveal correctly the timing of muscular activation with performance comparable to the state-of-the-art methods. The detection algorithm is automatic and user-independent, it manages the detection of both onset and offset activation, it can be fruitfully applied even in presence of noise and, therefore, it can be used also by unskilled operators.
本文针对使用表面肌电信号识别肌肉收缩时机的问题进行了研究。在动态条件下检测肌肉激活的时机对实际的临床诊断具有重要影响。广泛使用的单阈值方法仍然依赖于操作员的经验，需要手动设置该阈值。本文提出了一种基于子波域不连续检测来检测肌肉激活区间的新方法。通过使用一组模拟信号评估算法的准确性和精度，得到的偏差和估计标准偏差分别低于 11.0 和 8.7ms。此外，还对 10 名健康受试者进行了实验应用，记录了等速运动过程中下肢的肌电信号。该算法能够正确揭示肌肉激活的时机，其性能可与现有技术相媲美。该检测算法是自动的且不依赖于用户，可以检测激活的起始和结束，即使存在噪声也能有效地应用，因此也可以由非熟练操作人员使用。
