# Human Pose Estimation Using MediaPipe Pose and Optimization Method Based on a Humanoid Model
## 1 Introductions  
Due to improvements in medical technology and appropriate nutrition, the senior population is continuously growing all over the world. Seniors are monitored by health managers or caregivers in nursing facilities, homes, or hospitals. Seniors who live alone at home are at risk of falling and injuring themselves unless a family member, a social worker, or a caregiver is with them. A mobile robot that moves around the house, takes pictures of an elderly person's pose from appropriate positions, and automatically analyzes their current pose or activity to alert relevant people when a dangerous situation or issue arises would be very useful. To this end, full-body joint angle data for Activities of Daily Living (ADL) is very efficient information for recognition, transmission to server, and restoration to DB as historical data [1]. To estimate joint angles, the classical approach is to solve inverse kinematics (IK) with a given set of 3D joint coordinates of a humanoid robot with a structure mimicking the human body. To solve IK, there are no closed-form IK equations for the entire joints of such complex robots. IK equations are derived from six parts: two arms, two legs, the torso, and the head [2]. In [1, 3], joint angles are calculated for each part using a geometric relationship, which is difficult to apply in all variety of poses. On the other hand, heuristic optimization methods called \"Firefly Algorithm\" have been applied to solve IK equations for a three-link articulated planar system [4]. Recently, attempts to replace inverse kinematics formulae for human models with deep learning methods are being actively pursued [5, 6]. Furthermore, motion capture systems that are quite expensive and take up a lot of space are the only way to obtain precise 3D joint coordinates for a subject. Therefore,the authors decided that the most realistic and simplest way would be to photograph a subject with a 2D camera and estimate each joint angle using a fast optimization algorithm based on a 3D humanoid model. This technology belongs to the field of 3D human pose estimation with monocular image or video. Human pose estimation technology is being actively researched around the world in the areas of sports, surveillance, work monitoring, home elderly care, home training, entertainment, gesture control, and even metaverse avatar. In general, human pose estimation is classified into 2D and 3D coordinate estimation methods, single-person- and multiple-person-based methods according to the number of target subjects, monocular image- and multi-view image-based methods according to the number of shooting cameras, and single-image- and video-based methods according to the input type [7; 8; 9; 10; 11]. Specifically, according to the structure of deep learning process, human pose estimation is classified into single-stage methods and two-stage methods. The single-stage methods that directly map input images to 3D body joint coordinates can be categorized into two classes: detection-based methods [12; 13] and regression-based methods [14; 15]. The detection-based methods predict a likelihood heatmap for each joint which location is determined by taking the maximum likelihood of the heatmap, while the regression-based methods directly estimate the location of joints relative to the root joint location [14] or the angle of joints by introducing a kinematic model consisting of several joints and bones [15]. Because 2D pose estimation has a greater number of in-the-wild datasets with ground-truth joint coordinates than 3D pose estimation, two-stage methods of leveraging 2D pose estimation findings for 3D human pose estimation, also known as lifting from 2D to 3D, are being developed extensively [16]. The relationships between joints have been exploited by long short-term memory (LSTM) [17], and generative adversarial networks (GANs) are often used to produce a more realistic 3D human pose [18].