# Glove Prototype for Feature Extraction Applied to Learning by Demonstration Purposes
## 2 Literature Review
To improve HMT accuracy, the most common methods to tackle the drift error problem are as follows:  
* The hardware aspect using high precision sensors to minimize the drift error, such as XSens and Invensene tracking units;
* The algorithm aspect enhances the system by using multi-sensor data fusion. The availability and advancements of Micro Electrical Mechanical Systems (MEMS) technology enables the inertial sensors integrated into single chips.The company has made inertial sensors so small and low-power that they have already become a common practice in ambulatory motion analysis. For the time being, the magnetic sensors are used together with inertial sensors for accurate and drift-free orientation estimation [12; 13; 14]. IMMU has been proved to be an approach accurate enough in estimating body segment orientations without external equipment [5]. It is easy to use and set up, non-intrusive, and relatively cost effective. It demonstrates higher correlation and lower error estimation compared with a research-used visual motion capture system when recording the same motions. For this reason, the low-cost and small wearable inertial and magnetic sensors are becoming increasingly popular for the development of the data glove [5,15]. Thanks to MEMS technology, sensors can achieve great accuracy and speed at a low cost. The features of the presented IMU sensor are typical of low-cost alternatives. In addition, the _MPU9_250 sensor already has open-source libraries that should cover basic functionalities. Given the above sensor packages, a sensor fusion algorithm is required to estimate orientation. Extended Kalman Filters (EKF) are frequently used in nonlinear estimation problems. The EKF can recursively estimate the system states from system measurements affected with Gaussian noises. The gyro has drift, and the accelerometer has a Gaussian-like noise which is what EKFs are mostly designed for [16]. * General Extended Kalman Filter The Kalman filter is only suitable for linear systems and requires that the observation equation is linear. Most of practical applications are nonlinear systems; therefore, the research of a nonlinear filter is very important. However, a lot of the applications are nonlinear, so the research for a nonlinear filter is also important. One of these is the extended Kalman filter [17]. The basic idea of EKF is to focus on the value of first-order nonlinear Taylor expansion around the status of the estimated, then transform the nonlinear system into a linear equation. The EKF algorithm is commonly used in nonlinear filter systems, and the calculation is easy to implement. However, the Taylor expansion belongs to a linear process, so the system status and observation equations need to be close to linear and continuous, otherwise the EKF will not estimate the true value well. In addition, the status and measurement noise affect the filtering result [18]. The covariance matrix of the system status and observation noise do not change in the process of EKF. If the estimation of the covariance matrix of status and observation are not accurate enough, the error will accumulate and cause a divergence of the filter [19,20]. Firstly, EKF is based on the first-order Taylor series expansion for a nonlinear equation. Secondly, EKF assumes that the observations and status noise are independent in white noise processing, but the noise characteristics may not be coincident with the characteristics of the assumed white noise. Because first-order Taylor series expansion can change the observation and status noise, the assumption for noise will also be inconsistent with reality. Finally, for each EKF process, the EKF needs to re-calculate the Jacobian matrix of the current time observation equation. Because the calculation of the matrix is very complex, it is difficult to solve the Jacobian matrix in some systems [19]. * Quaternion-Based Extended Kalman Filter Unit quaternions have many applications in state estimation problems because they are simple to work with.