Additionally, considering the fact that each person has a unique gait makes it challenging for implementing gait-analysis systems for any random subject. However, a walking motion is comprised of cyclic leg motions where the bone segments move in a correlated way with each other. Hence, the walking motion can be mapped or reconstructed from the motion of a single bone segment. The nonlinear relation that exists among the bone segments could be possibly approximated by neural networks. Various algorithms have been used to estimate human poses. However, with the ability to reconstruct human poses from fewer sensor quantities and the ability to generalize across subjects, neural networks have been the center of attention in recent years. This has been demonstrated by our previous study, where we investigated the estimating leg joints from only one IMU sensor fixed onto the pelvis of a subject using a neural network [23]. Another data-driven technique by [24] gathered data from five people with one IMU sensor unit fixed on the shank of the right leg to train a recurrent neural network (RNN) that approximates the gaits of construction workers. They made a special rectangular wooden frame to perform data measurement experimentation. Then subjects were instructed to walk on top of it while carrying all the computing equipment. Similarly, [25] also used a shank-mounted single IMU sensor to estimate the sagittal-plane lower-limb-joint angles. Their data collection was performed by instructing subjects to walk in a straight line of a 5-m distance inside a laboratory. The existing methods explained above proved one or two sensors can be enough to estimate the leg-joint angles with good accuracy. This is possible due to the periodicity and kinematically constrained biomechanical walking of humans. Reduced sensor quantity not only helps reduce the complexity but also contributes to a more natural gait performed by subjects. Despite increased research in this field, there is a paucity of information investigating the most suitable single IMU placement for leg-joint estimation. As the need for portable and simple wearable sensors for motion analysis is growing, identifying the best possible sensor-fixing body locations is the critical part. The position of the fixed single inertial sensor highly affects the estimation result of the neural networks. There is no consensus regarding the position of the sensors on the body as previous studies fix inertial sensors on the pelvis [20; 23], thigh [21; 22], shank [21; 22; 24; 25], and foot [20]. Hence, in this study, the placement of a single sensor on different parts of the body for joint-angle estimation of both legs will be investigated by employing various neural-network algorithms. This is essential to understand the optimal inertial sensor placement on the lower half of the body when reduced inertial sensors are needed for lower-body motion analysis. This study will contribute to healthcare physiotherapists and motion analysts in the sports field. The most dominant sensor positions in many of the existing studies will be the potential candidates for the inertial sensor placement to estimate two lower-limb-joint angle sets. These include the pelvis, thigh, shank, and foot. According to [26], CNN is a better candidate for only prediction tasks while LSTM is desired for sagittal-plane joint-angle prediction and real-time joint-angle estimation over multilayer perceptron networks. Hence, four neural networks including convolution-based ones and LSTM networks were selected. These include a unidirectional LSTM, a bidirectional long short-term memory (BLSTM), a convolutional neural network (CNN), and a wavelet neural network (WNN). For the neural-network training, walking data were collected from 16 subjects. The data measurement was performed in an outdoor setting where subjects were told to walk freely and naturally. This study was accomplished with easier mounting labor and significantly lower sensor setup cost.