The 3D human pose is captured in a specialized indoor setting, such as laboratories and studios, using a high level of position accuracy optical motion capture systems [12]. These methods have long been considered the industry standard methods. Another type of nonwearable system which is a pressure-sensing carpet was proposed by the Massachusetts Institute of Technology. It is used to estimate the 3D human pose using the pressure data acquired from the tactile carpet. The system includes a carpet of 36 ft2 areas with 9216 sensors, readout circuits, and two cameras [13]. Moreover, vision-based methods by [14; 15; 16] developed a 3D reconstruction of a human pose from 2D still images and movies while [17] computed walking speed and stride length from a Kinect camera depth data. Despite their excellent performance, nonwearable systems only operate inside controlled laboratory settings, which makes them difficult for physiotherapists and sports scientists who are looking to bridge the lab-to-field gap. On top of that, such systems are expensive and demand longer setup time and substantial skill. These limitations are currently being eased owing to the technological advancement of wearable sensor miniaturization. Inertial measurement units (IMUs), electromyography, and other wearable sensors have opened the way for practical indoor/outdoor motion capture systems for long-term use. The continuous digitization progress and the high demand for motion analysis in various fields such as rehabilitation centers have made inertial sensors to be the center of the topic over the last few years. Even though they enable us to assess movements in a real-world setting with easier portability, wearable sensors are not yet a standard practice in motion analysis because of a lack of examination related to accuracy and reliability. However, recent works by [18; 19] performed an investigation on the reliability and validity of the commercially available inertial sensors called Xsens inertial sensors. They evaluated them for different activities including walking, squatting, and jumping. As a result, they concluded reliability and validity were fair to excellent in the sagittal plane for hip, knee, and ankle joint angles and the system can be used by a clinician to quantify leg-joint angles. For their convenient accompanying software, these inertial sensors were used in this study as well. However, many of the inertial capture systems vary in terms of sensor quantity, sensor positioning, and estimation method [20; 21; 22]. The study by [20] adopted an extended Kalman filter method for lower-limb segment position and orientation estimation from two (fixed only to the feet) and three (attached to the pelvis and the feet) sensor sets. For the three-sensor set, they achieved an overall root mean square error (RMSE) of 5.0 \(\pm\) 1.0, 8.2 \(\pm\) 2.2, and 5.9 \(\pm\) 1.6 for the hip, knee, and ankle, respectively. A study by [21] developed a microcontroller with two inertial sensors mounted to the thigh and the shank for the computation of the knee joint angle. Their system claimed to have achieved an RMSE of 0.04\({}^{\circ}\) with a mean average percentage error of 2.95% compared to a Vicon motion capture system. Similarly, [22] used one inertial sensor fixed to the thigh to target the knee joint angle and two inertial sensors fixed to the shank and thigh to target the ankle joint angles during walking. They have achieved an MAE of 1.69 \(\pm\) 1.43\({}^{\circ}\), 1.29 \(\pm\) 1.0\({}^{\circ}\), and 0.82 \(\pm\) 0.69\({}^{\circ}\) for the knee, talocrural joint, and subtlar joint, respectively. In the existing systems, there is a lack of information on how many inertial sensors are enough to correctly estimate the lower-limb-joint angles during walking locomotion. Certainly, multiple inertial sensors would make the subject uncomfortable and the system complex and thus expensive to run. Therefore, any method which employs a reduced sensor quantity while not sacrificing the performance of the system is favorable.