## 1 Introduction
Conventional gait analysis is performed using multi-camera systems or a pressure-sensitive walkway [18; 19; 20; 21]. These two systems have been signified to be the gold standard for analyzing and validation of spatiotemporal gait parameters. Although they offer the measurement accuracy needed for healthcare professionals to evaluate gait quality, the high cost of the equipment is a limiting factor. Thus, to counter this problem, gait analysis system that are versatile and accessible outside of clinical settings, while still achieving accurate measurement, have been proposed in literature. Two general distinctions of the proposed measurement system can be identified: wearable and non-wearable gait measurement systems. For wearable systems, the commonly used approach is the use of inertial measurement units (IMU) equipped with gyro and accelerometer sensors that gets the acceleration and angles of the attached body parts, in this case the legs. The system has been utilized in areas like sport science, rehabilitation,  
Figure 1: Gait phases. pose tracking, among others [22, 23]. The biggest appeal is on the ease-of-use that wearable measurement units afford the user. On the other hand, IMU systems, as reported in various studies, face some challenges. The obtained IMU data may produce low accuracy and noisy data that must be pre-processed using a fusion algorithm and calibrated beforehand [24]. Moreover, the relative orientation between the sensors and the subject body cannot be fixed over different data acquisition sessions [25]. In a system developed by [26], the accuracy of IMU in estimating 3D orientation was investigated by using a simple pendulum motion. The authors reported an RMS error of between 8.5\({}^{\circ}\) and 11.7\({}^{\circ}\) of the IMU raw data processed by the supplied Kalman filter algorithm supplied by the vendor. However, with the custom fusion algorithm developed by the authors, the RMS error was reduced to between 0.8\({}^{\circ}\) and 1.3\({}^{\circ}\). For the wearable sensors, to increase the accuracy, a fusion algorithm that suits the physical activities tracking and the multiple use of body-worn sensors can potentially reduce the ease of use. One of the solutions to encounter the limitations face by body-worn sensors is the use of a non-wearable system. This approach employs alternative sensing elements, such as cameras and lasers, for gait monitoring. For non-wearable systems, camera-based methods have been widely studied by researchers [27, 28, 29]. For camera-based methods, there are two different approaches: marker-based and marker-less. A study by [30] indicated that marker-based systems, such as Vicon Nexus, offer a more accurate result compared to marker-less systems. Vicon's motion-capture systems use high-speed and high-resolution imaging cameras positioned around the space where the action is taking place to detect the reflective markers attached to the body in real-time. Equipped with specialized software, Nexus can precisely determine where the cameras are positioned in space. Thus, marker coordinates are computed faster and effortlessly. However, this system requires carefully configured camera settings in a laboratory setting with sufficient markers on the subject. In addition, this type of system requires more time, technical expertise, and equipment to assess the analysis outcomes [31]. Hence, there has been a growing interest in developing marker-less-based technologies. For a marker-less-based system, an RGB-depth camera that can extract kinematic information has been developed by Microsoft Kinect and Intel RealSense, among others [32, 33, 34, 35]. The pros for this type of method would be the absence of markers, which maximize the subject's movement comfortability and reduction in preparation time prior to the experiment. Furthermore, by extracting the kinetic information, the accuracy can be improved.