## 1 Introduction  
In recent years, there has been an increasing interest in the analysis of electromyography (EMG) signals in clinical and engineering applications. EMG signals reflect electrical currents emanating in muscles during neuromuscular activities such as muscle activation, contraction, or relaxation. These EMG signals are the summation of motor unit action potential controlled by the central nervous system which can be obtained through two techniques--namely, invasive and non-invasive. The non-invasive approach is preferable, as it offers benefits such as a minimal risk of infection and avoids discomfort for amputees [1]. Surface EMG signals are not only used to diagnose patients with neuromuscular diseases [2, 3] and detect muscle fatigue [4], but also assist individuals who suffer from limb amputations and are using robotic devices or assistive technological devices during rehabilitation [5, 6]. The major difficulty of applying these bio-signals in a pattern recognition system in real time is that they are unstable and vary in time [7]. A conflicting result was found inthe study conducted in [8]; AndroidTM-based mobile devices were developed for stationary bicycle exercise using EMG and electrocardiography (ECG) signals and demonstrated that the devices were able to calculate the cadence and heart rate for biking in real-time application, with detection rates of 91% and 94%, respectively. In addition, assistive devices such ankle-foot orthoses were developed by [PERSON] et al. utilising the concept of controlling output voltages based on EMG signals and ankle position as shown in Figure 1[9]. Their system successfully controlled the output voltage according to the required needs. However, a problem arose in analysing EMG signals when EMG signals were interpreted through general observation. Numerous studies have proposed various techniques to obtain a higher accuracy in detecting surface EMG pattern recognition [1]. In the development of the prosthetic hand, the usage of multiple EMG sensors is not practical and makes it difficult for amputees due to electrode shift/movement, and amputees often feel discomfort in wearing the surface EMG sensor. This problem was resolved by applying independent component analysis and Icasso clustering to select the optimum number of sensors, and two sensors gave a higher average accuracy than others (96.6%) [10]. Generally, the main processes involved in pattern recognition are feature extraction and classification. Features are extracted from the surface EMG signals and trained into a classifier to recognise different patterns of muscle activities. It has been pointed out that feature extraction plays a crucial role in achieving better classification performances [11]. This process involves the transformation of raw EMG signals into a feature vector. Various features based on either time domain (TD) or frequency domain (FD) and time-frequency domain (TFD) are utilised. According to [PERSON] et al., the relative changes in EMG signal features over time become progressively smaller when the number of days during which the subjects perform the pre-defined motions increases [12]. Meanwhile, a self-recalibration using TFD features and a convolutional neural network classifier was proposed in [13] that can be automatically updated to maintain a stable performance over time without the need for a user retraining base. Similarly, with the study conducted in [14], the principal component analysis of TFD features showed the best linear relationship with the grasp types compared to TD and FD features for grasp recognition. However, TFD features yield a high-dimensional feature vector [1]. Thus, TD features are mostly adopted in the literature, as discussed in [15]. TD features were evaluated based on signal amplitude that varied with time. In 1993, [PERSON] et al. proposed five TD features--namely, mean absolute value, mean absolute value slope, slope sign  
Figure 1: An overview process on the development of AFO as conducted in [9].