# Human Pose Detection for Robotic-Assisted and Rehabilitation Environments
## 2 Human Pose Detection and Body Feature Extraction: A State of the Art  
The human body is a very complex system composed of many limbs and joints, and the exact detection of the position of the joints in 2D or 3D is a challenging task [12], as it requires a specific assumption within biomechanics research in robotic rehabilitation environments [13]. In addition, HRI environments are complex and nondeterministic, and it is not easy to ensure the user's safety during interaction with the robot. Currently, this assumption is a research topic in other areas, such as Industry 4.0 [14,15]. The resolution of this issue involves constant position tracking, intention estimation, and action prediction of the user. This problem can be faced by a proper sensory system. On the one hand, some contributions employ inertial measurement units (IMUs) for motion capture, especially in medical applications and motor rehabilitation analysis [16,17]. However, this type of sensor requires the correct placement of passive/active markers on the body before each capture session, and they are insufficient for HRI environments. On the other hand, this issue can also be faced as a computer vision problem, basically using two approaches: marker-based and marker-less. Marker-based approaches,such as motion capture systems (MoCap), have significant environmental constraints (markers in the human body) and are relatively complex, expensive, and difficult to maintain. Marker-less approaches have fewer environmental constraints, and they can give a new understanding about human movements [18]. This issue requires the processing of complex information to develop an algorithm that recognizes human poses or skeletons from images. Therefore, an easy-to-use marker-less motion capture method is desirable for these robotic rehabilitation environments. In this paper, we analyzed the performance of the estimation of shoulder and elbow angles for the development of rehabilitation exercises using CNN (convolutional neural network)-based human pose estimation methods. There is extensive research about marker-less approaches for human tracking motion. In these approaches, depth cameras such as Kinect (RGB-D) provide additional information about the 3D shape of the scene. Kinect has become an important 3D sensor, and it has received a lot of attention thanks to its rapid human pose recognition system. Its low-cost, reliability, and rapid measurement characteristics have made the Kinect the primary 3D measuring device for indoor robotics, 3D scene reconstruction, and object recognition [19]. Several approaches for real-time human pose recognition have been performed only using a single sensor [20; 21; 22], but it can have substantial errors with partial occlusions. In recent years, the use of deep learning techniques for 3D human pose estimation has become a common approach in HRI systems. These computer vision techniques usually train a neural network from labeled images in order to estimate human pose. As a reference, some research works obtain the 3D pose estimation using a single-view RGB camera image or multi-view camera images [23; 24]. These accurate methods encounter fewer problems regarding the cameras' position and calibration issues in comparison to RGB-D approaches. However, 3D human pose detection for assisted and rehabilitation robotic environments needs further improvements to achieve real-time tracking for human motion analysis with enough accuracy [25]. In our comparison, we decided to use the Kinect v2 sensor, which has a high accuracy in joint estimation while providing skeletal tracking, as described in [26]. Additionally, some research about this fact has been presented in [27], where the validity of Kinect v2 for clinical motion was compared with a MoCap system, and the error was reported to be less than 5%.