## 1 Introduction
FF: Foot Flat; HO: Heel Off; TO: Toe Off; IC: Initial Contact; PICAFO: Passive Controlled Ankle Foot Orthoses; FLC: Fuzzy Logic Controller; MR: Magnetorheological. changes, zero crossing, and waveform length [16]. They demonstrated that extracting EMG signals using TD was promising in prosthesis control. After that, many types of TD features were applied for bio-signal-based control in assistive devices, as discussed in [17]. The classification accuracy of TD features was not only higher than FD features, but also allowed grouped classification according to mathematical properties into four main types: energy information, frequency information, prediction model, and time-dependence methods [18]. In comparison with TFD features, TD features achieved classification accuracy of approximately 97% in discriminating upper limb movements [19], which were higher than TFD features that only gained average accuracy 78.1% [20]. However, an underlying assumption of TD features is that the signal analysed is stationary, making it inappropriate to be employed for EMG signals that are non-stationary in nature. Furthermore, it has been observed that EMG signals are stationary for short periods of time [21]. In many studies, EMG stationary state signals are seldom tested and are more often presumed. A stationary signal is characterised as an equilibrium condition, whereas the statistical properties are time invariant. There are four categories involving stationary process: first-order, second-order, wide sense (WSS) and strict stationaries [22]. In practice, WSS is deemed adequate for signal analysis when the statistical moments that describe the signals do not change over time, as implemented by [PERSON] et al. in 1997 [21]. That approach was represented as weakly stationary used in the present studies. Run, reverse arrangement (RA), and modified reverse arrangement (MRA) tests are mostly adopted to determine whether EMG signals satisfy the stationarity conditions [23]. Nonetheless, conflicting results of the stationarity of EMG signals have been observed for different window sizes, types of contraction, and the load exerted. In general, there are three types of contractions: isometric, isotonic, and isokinetic. Isotonic contraction involves a change in muscle length (e.g., shorten and lengthen) produced during dynamic movements or exercise. If the speed remains constant, the contraction is defined as isokinetic contractions. Specific equipment is required to measure these contractions; for example, Humac Norm testing and rehabilitation using trunk module [24]. The study revealed that the stationarity of EMG signals decreases as trunk extension angular velocity increases as much as 30 degrees with 1000 ms window size based on RA and MRA tests. In the case of isometric contraction which occurs during static positions (the joint angle and muscle length do not change), the stationarity level of EMG signals depends on the window size [25]. With 375 ms and 125 ms window size, EMG signals are reported as stationary during isometric and isotonic contractions, respectively [26]. On the other hand, the reported classifier performance was influenced by the segment length or window size. [PERSON] and [PERSON] pointed out that the classifier performance degraded when using a window size less than 125 ms, as observed in the case of high non-stationarity [27]. This statement was proven by [PERSON] et al., as the accuracy of classification increased by about 2-3% and \(\leq\)1% for segment length from 125 to 250 ms and from 250 to 500 ms, respectively [28]. They stated that the result was due to a larger segment providing additional information while yielding small bias and variance in the estimation of the feature. Similarly, with the research conducted in [29], the window size between 128 ms and 256 ms gained lower percentages of error compared to the segment length that was less than 128 ms during steady and transient states.