Title: Regression and Machine Learning Methods for Predicting Human Movements Based on Skeletal Data
Abstract: In recent years, the advancement of computer vision technologies has facilitated the extraction of skeletal data from human movements captured in video sequences. Leveraging this wealth of information, this article explores the application of regression and machine learning methods to predict human movements based on skeletal data. The objective is to develop models that can accurately forecast and understand the dynamics of various human motions.The methodology involves the use of computer vision techniques for skeleton detection and tracking, followed by careful preprocessing of the skeletal data to extract relevant features. These features, representing key aspects of the skeletal structure and motion, serve as inputs to regression and machine learning models. Various algorithms, including linear regression, support vector machines, random forests, and deep neural networks, are employed to learn the intricate relationships between skeletal features and corresponding movements.A crucial aspect of the study is the creation of a comprehensive training dataset that captures diverse human movements. Data augmentation techniques are applied to enhance the variability and robustness of the training set. The trained models are then rigorously evaluated using metrics such as mean squared error to quantify their predictive performance.Results indicate that the proposed regression and machine learning approaches exhibit promising capabilities in predicting human movements. The article discusses the strengths and limitations of different algorithms, highlighting their applicability in various contexts, including virtual reality, augmented reality, security systems, and robotics. Furthermore, the study emphasizes the importance of continuous refinement and adaptation of models to accommodate the complexity and variability inherent in human motion.This research contributes to the growing body of knowledge at the intersection of computer vision and human movement prediction, paving the way for innovative applications in fields such as human-computer interaction, biomechanics, and artificial intelligence. The findings underscore the potential of regression and machine learning methods in enhancing our understanding and prediction of human movements based on skeletal data.
Keywords: human movement prediction;skeletal data analysis;computer vision;regression methods;machine learning;motion forecasting;predictive modeling;virtual reality
Review score: Low
Review comment: The contribution of the paper is not clear. This paper has several critical problems as follows. 

1. Literature review should have been provided in the Introduction section. Authors have not properly stated contribution of their paper with respect to previous published literature. 

2. There is no theoretical contribution since the paper does not have proper mathematical description of the problem statement together with proposed framework. 

The formula (3) has quite inappropriate style. It is advised for authors to write the mathematical description of the problem statement together with the "pseudocode" of the proposed Algorithms for Human motion prediction in the Section 2 and in Section 3 (results) describe the experimental set-up and simulation results.

3. There is no proper technological contribution and therefore it is hard to validate models for human pose prediction. It is advised for authors to actually proceed with real implementation of their proposed scheme with appropriate code generation files. 

4. Adstract is too long. It is advised for authors to make their abstract precise and concise. Also your paper has 7 pages (6 can be maximum).