# Glove Prototype for Feature Extraction Applied to Learning by Demonstration Purposes
## 2 Literature Review  
Human motion tracking (HMT) systems can be divided into two categories: computer-vision-based (CV) and wearable-sensor-based (WS) [4]. The advantages of CV-basedtechniques stem from the use of network-depth cameras to monitor human activity and have been applied to real-world applications such as filming and security surveillance [4]. Tech companies are also working on research and development of professional human motion tracking systems, namely Vicon and Optotrak. They work with high precision in controlled scenes but are highly dependent on the environment and its conditions, such as lighting and shooting angles. The nature of this method makes it only suitable for small and controllable cases [4]. CV-based solutions are used in teleoperation systems. It is fairly easy for visual systems to capture contours of the human in the majority of environments, but the captured images cannot provide enough information for tracking hands in three-dimensional space. It is because the derivation of the spatial position information can lead to multiple 2D-3D mapping solutions [5]. Furthermore, with a single source of capturing images, it is possible that some fingers are occluded and result in a non-observable problem, leading to faulty or undefined calculation of the pose [5]. Nonetheless, it can be integrated in a multi-sensory system to complement other's sensors weaknesses, such as the inertial measurement unit (IMU) drift problem. WS-based HMT systems, on the other hand, do not need to deploy devices in scenarios and do not rely as much on environment factors. These type of systems tend to be more reliable and accurate. Therefore, they can be more suitable for large-scale and dynamic applications. However, these systems also tend to be pricey and use specialized equipment. Another popular way is developed by the data glove. This method adopts technologies, such as inertial sensors, contacting electromagnetic tracking sensors, and glove instruments with angle sensors, to track the operator's hand or arm motion which completes the required task. Until now, different types of data gloves have been developed over time, both commercial and prototype ones. Some of the commercial examples: 5 th Glove, the Cyber Glove, the HumanGlove, the P5 Glove, and the ShapeHand [5]. As for the technology available right now, there are a lot of implementations and concepts of WS-based data gloves but there is no accepted standard for production. In addition, teleoperation systems still have limitations and room for improvement, especially in high precision tasks. Solutions developed for virtual reality (VR) applications are the most known but tend to be noisy because of the range of movement the application requires. Gloves developed for sign language interpretation do not need to be as accurate and are more focused on the interpretation of the pose estimation. So, there is a need for an accurate enough solution to make teleoperation of a hand manipulator desirable, and at its best as capable as a real human hand. There is a need for teleoperation in applications where it is dangerous for a human to manipulate, but the accuracy and dexterity of a human hand is needed. It can also replace some single-purpose teleoperation interfaces, such as joysticks and buttons, because of its general and intuitive nature of using a hand. At the moment, IMUs are the main approach to WS-based HMT systems, such as gyroscopes and accelerometers. Several solutions are presented by other authors, such as in the articles [6; 7]. The basic principle is to measure the triaxial angular velocity and acceleration of the motion by these sensors and obtain the orientation through integral operations. However, inertial sensors are known for the inevitable errors that accumulate over time [8; 9]. The drift error is the biggest challenge faced by IMU-based HMT systems [10; 11].