Figure 1 presents the number of publications on EMG onset detection methods along the years. After analyzing all papers selected, the pre-processing and EMG onset detection categories were defined according to the methods used and their relevance in terms of papers in the literature that applied each of them.

Number of publications on EMG onset detection methods per year reviewed in this study (period: 1978–2022; total articles: 156)

Pre-processing methods, used to improve the quality of EMG signals towards the extraction of meaningful information, usually add more computational time, which means a delay in real-time implementations. The pre-processing methods evaluated in this review were classified in the following categories: EMG Envelope, Teager-Kaiser Energy Operator (TKEO), Wavelet Transform and Others, which included those that did not fit in none of these categories. Different pre-processing methods were applied 90 times in the papers reviewed. Calculating the EMG envelope was the method most frequently used, followed by the TKEO method.

According to the CEDE project, EMG envelope is a smooth curve that tracks changes in the amplitude of an EMG signal over time [8]. Calculating the EMG envelope is a pre-processing method that can be obtained in several ways, as shown in Fig. 2.

Comparison of the most common approaches to obtain the EMG envelope from the raw EMG signal

To obtain the EMG envelope from raw signals, two main options are available: (1) low-pass filtering of the rectified signal; (2) root-mean-square (RMS) on raw EMG signal.

Low-pass filtering of the rectified signal One of the most common approaches to calculate the EMG envelope is to use a discrete version of traditional low-pass filters such as Butterworth or Chebyshev on the rectified EMG signal (obtained by computing the absolute value of the raw signal).. These filters can be considered as Infinite Impulse Response filters [15]. This approach was applied in: [16,17,18,19,20,21,22,23,24,25,26], with the Butterworth being the most predominant filter used.

Moving average (MA) According to the CEDE project, MA is defined as a method to smooth EMG data, that acts as a low-pass filter, reducing random fluctuations in the rectified or squared EMG signal [8].

This method was first used in the context of EMG onset detection by Maple-Horvart and Gilbey in 1992 [27]. After that, MA was applied to calculate EMG envelopes for EMG onset detection in several other papers: [13, 28,29,30,31,32,33,34,35,36,37,38,39,40,41].

The MA is calculated with a series of averages from successive segments, with or without overlapping windows. The consequence of its use is the attenuation of rapid variations through local averaging, but retention of slow variations [28], smoothing the signal and acquiring its envelope.

Root-mean-square (RMS) on raw EMG signal This approach ([28, 33, 42,43,44,45,46,47,48,49,50,51]) computes the RMS value of the signal within a window that “moves” across the raw EMG signal.

The RMS value measures the square root of the signal’s power. Therefore, it has a physical meaning. RMS is useful in many other applications [42]. EMG envelopes can be calculated from the RMS according to Eq. 1.

$$\begin \begin X_=\sqrt\sum _^x_i^} \end \end$$

(1)

where \(x_\) is the EMG value in the \(i^\) sample and N is the number of samples.

The TKE operator method ([17, 25, 38,39,40, 48, 52, 53, 53,54,55,56,57,58,59,60,61,62,63,64,65,66,67]) was first proposed by Teager in 1982 [68,69,70]. The results obtained in these studies suggested that the production of speech involved nonlinear processes. As a result, Teager derived the TKE operator in the discrete-time domain to compute the energy of a sound. This method has been extended to cover other continuous signals such as EMG [53].

The discrete TKE operator \(\psi\) is defined in the time domain as:

$$\begin \begin \psi _[x(n)]=x^(n)-x(n+1)x(n-1) \end \end$$

(2)

where n is the sequence index and x the raw EMG signal. Considering a signal defined by Eq. 3:

$$\begin \begin x(n)=A cos[\omega _(n)+\theta ] \end \end$$

(3)

where A is the amplitude, \(\omega _(n)\) is the angular frequency, and \(\theta\) is the initial phase, the energy operator can be rewritten as defined in Eq. 4:

$$\begin \begin \psi _[x(n)]\approx A^sin^(\omega _) \end \end$$

(4)

Equation 4 shows that the TKEO is proportional to the instantaneous amplitude (A) and frequency (\(\omega _\)) of the input signal. Therefore, TKEO is usually applied on EMG signals to extract motor unit activity by making the action potential spikes sharper and narrower, enhancing the muscle activation points [53].

Several studies have demonstrated that pre-processing using TKEO can improve the EMG onset detection with respect to different pre-processing methods [17, 52, 53, 57, 71].

Pre-processing of raw EMG using the wavelet transform was applied in the following papers: [32, 48, 52, 59, 62, 64, 67, 72,73,74,75,76,77,78].

The WT is one of many time-frequency representations used in signal processing. These transforms deconstruct a time domain signal into a sum of signals of different scales and time shifts, to produce a time-frequency representation of a time domain signal. WT is an effective tool to extract useful information from the EMG signal.[79].

The other pre-processing methods found in the literature were the Hilbert filter [9, 80,81,82], the Kalman filter [83], the Morphological Close Operator [38, 55], the Morphological Open Operator [38], the Multi Objective Optimization Genetic Algorithm [84], the Adaptative Linear Energy Detector [85], the use of an statistical criterion based on the amplitude distribution of EMG signal [86], the Constant False Alarm Rate method [87] and the Empirical Mode Decomposition [82].

EMG onset detection methods are those that, when applied to the EMG signal (raw or pre-processed signal), allow the identification of the beginning of muscle activation. In the pasts, the onset of muscle activation could be detected using mainly the following methods: Visual inspection, Threshold-based and Statistical. Recently, other studies have tested different methods (especially Machine Learning based) to determine muscle activation onset, reporting promising results. In our study, all EMG onset detection methods that do not fit into none of the previously mentioned categories were classified as ”Other EMG onset Detection Methods”. Figure 3 shows the number of papers that applied each of these categories within each different application domain (Robotics, Clinical, Research and Others). EMG onset detection has been applied in the application domain ’Research’ more than in all the other domains together.

Number of publications included in each of the different EMG onset detection categories (Visual inspection—black; Threshold—light gray; Statistical—dashed grey; Machine Learning—bold gray squares; and Others—dashed bold gray) within each different application domain. Application domains considered were Robotics, Clinical, Research and Others

Visual inspection entails subjectivity and needs to be performed by an expert. There are no criteria established on how to carry out the visual inspection technique, although it is usually employed to detect the earliest rise in EMG activity above the steady-state (i.e., basal activity) [50, 88,89,90,91,92,93,94].

Despite being a subjective technique, visual inspection can be used to validate automatic EMG onset detection methods, serving as a gold-standard to develop computerized EMG onset detection methods. The visual detection of EMG onset has been widely referred in the literature: [