Our work brings a new contribution to discuss hand tremor suppression control with a comparison of adaptive techniques, more precisely, filtered-x based, for active vibration control applied to the suppression of movements arising from hand tremors. Among the advantages of the algorithms are faster convergence in ideal conditions and tracking capability. The comparisons take into account complexity analysis, convergence time, and mean square error (MSE) in the steady state. As is known, there are many challenges in research of this nature, especially in the difficulty of collecting and evaluating data on the intensity and frequency of tremors without the need to use invasive methods. In our analysis, we used mathematical and computational models to analyze the attenuation effect. Therefore, tests in humans are not considered in this study. The results presented in this work enable the performance analysis of the tested statistical learning algorithms involving Monte Carlo simulations. It is important to note that simulations are not perfect representations of real-world situations, and there may be some discrepancies between the simulation and reality. Therefore, it is essential to carefully evaluate and validate simulation results before making any important decisions based on simulation outcomes [11; 12]. The rest of this work is organized as follows. In Section 2, the mathematical models used to represent the hand tremor signal are presented and discussed. Section 3 presents the structure of the active vibration control and the filters used to compose the solution to the problem. Section 5 presents the results, as well as a discussion of the findings. In Section 6, final considerations and new directions for research in the area are presented. Related Publications  
Active vibration control (AVC) consists of applying a force of opposite intensity to an external source of vibration. To reduce hand tremors, [PERSON] [13] developed a tremor-suppression prototype consisting of a flexible, portable glove. This prototype has a micro-electro-mechanical vibration simulation module based on an accelerometer-gyroscope embedded in the glove that mimics hand vibration. The vibration signal is collected and sent to a microcontroller so that it can be processed, and the vibration motors present in the fingers are activated to reduce the tremor in 40%. Another method to eliminate human arm tremors was developed by [PERSON] and [PERSON] [14], who used the self-balancing technique. In this technique, a rotating unbalanced mass actuator was used to reproduce arm tremors with the same frequency range obtained from clinical sources. In addition to the composition of the counterbalance device designed to hold the dummy arm, a proportional derivative integral controller and an algorithm programmed in LabVIEW were used to control the rotary unbalanced mass actuator, anti-shaker, in the order to combat tremor. The method used by [PERSON] et al. [15] consists of the mathematical modeling of a system with 4 degrees of freedom of a biomechanical system that represents the human hand. Active control is performed by combining a proportional and derivative controller and tested, via MATLAB-Simulink, in three different types of actuators: piezoelectric, magnetic, and electric. Finally, the efficiency of each actuator is compared separately, with the piezoelectric actuator demonstrating greater efficiency in suppressing tremors, such as those characteristics of [PERSON]'s disease. The paper [16] describes a novel deep neural network-based motion predictor that is both user and task independent. In comparison to existing estimators and predictors, the proposed one-dimensional convolutional-multilayer perceptron (1D-CNN-MLP) model demonstrated superior performance in both simulation and experimental assessments, especially in reducing the time delay required for real-time tremor management using wearable assistive devices.