# Glove Prototype for Feature Extraction Applied to Learning by Demonstration Purposes
## 2 Literature Review
### Orientation and Position Estimation  
IMU Hand Configuration To answer the questions such as where the sensors will be placed, how many sensors are needed, and what grade of sensors is suitable for the application, all options must be weighted. The main features in this human-machine interface motion capture system are performance and accuracy; therefore, the sensor choices and configuration must accommodate and maximize those two parameters of the system [31]. A possible solution is to use inertial and magnetic measurement units placed on the hand and finger segments. For example, placing an IMU in each phalanx and one reference on the back of the hand for a total of sixteen sensors to detect the movements of the fingers and at least one on the back of the hand to detect the wrist movement, hand pose, and serve as reference for the fingers [5].  
The sensor configuration mentioned in [5] minimizes mistakes in determining the orientation of the fingers and allows the organization of an optimal human-machine interface for the control of bionic prostheses, since this number and arrangement of the sensors makes it easy to determine the length of the operator's fingers as well as the angles between the phalanxes. This solution ensures maximum accuracy, and the system gets rid of some ambiguities [15]. In this configuration, there is no need to integrate any positioning system because every angle of the fingers is measured and forwards kinematics calculate the pose of the hand.  
In addition, the works developed by [PERSON] and [PERSON] [32; 33] present two different approaches for gloves with different purposes. The first one is dedicated to rehabilitation,using tendon-inspired methods, and the multi-sensor glove presents biological signal data collection.  
Another way to configure the sensors can be achieved by placing sensors only at the tips of the fingers and one reference on the back of the hand. In this configuration, the orientation alone is not enough because there is no measurements of adjacent phalanges, and the fingertips orientation does not define a single pose which is why a tracking solution is needed to solve ambiguity. With the accelerometer data, it is technically possible to estimate the position of the fingertip in 3D space in reference to the IMU at the back of the hand by integrating twice. This solution has a drift problem too and even worse than the IMU orientation estimation because the error grows in a quadratic manner as it becomes the first integration error and then integrates again on top of the first error. This way, errors will also accumulate and deviate from the true position of the fingers very quickly, making this solution not good even with the best sensors. To tackle this, other position tracking solutions are needed, such as time of arrival (TOA) or computer vision (CV).  
The option to use several IMUs is to create a different system from the ones presented in the literature that can pass over the occlusion issue regarding CV-based solutions without using the typical flexometer-based solutions, allowing to know exactly the angular position of each phalange.