AI and IoT Revolutionize Sports Training Analysis

In a paper published in the journal Sensors, researchers addressed the challenge of motion training feature recognition in video understanding by employing artificial intelligence (AI) and data mining with Internet of Things (IoT) voice devices to analyze sports training. Sensors accurately measured positions and postures, transmitting real-time data for analysis. The study successfully identified movement patterns and accurately predicted athlete states and postures.

Study: AI and IoT Revolutionize Sports Training Analysis. Image Credit: metamorworks/Shutterstock
Study: AI and IoT Revolutionize Sports Training Analysis. Image Credit: metamorworks/Shutterstock

Background

Past work has extensively explored integrating IoT technology with AI for sports training feature recognition. Traditional methods, reliant on specialized equipment and complex data processing, have limited practical application. IoT voice devices offer real-time data acquisition capabilities in sports training. By employing AI data mining, these devices automatically identify and classify training features, extract key patterns, and provide real-time feedback.

Sports Training Technology

AI data mining, integrated with IoT voice devices, offers real-time data acquisition and processing capabilities, revolutionizing sports training feature recognition. By collecting real-time sound and sensor data during sports activities, AI-driven data mining algorithms extract and analyze key athlete characteristics like movement, strength, and posture. These insights are crucial for establishing models recognizing and classifying sports training features and enhancing training effectiveness through personalized feedback and guidance.

Data mining is pivotal in constructing descriptive and predictive models to uncover hidden patterns from large datasets, aiding decision-making across various sectors such as finance and telecommunications. Techniques include data pre-processing, fusion, extraction, standardization, and model evaluation, essential for refining data for meaningful analysis and visualization. Advanced algorithms like the fuzzy c-means (FCM) algorithm enhance clustering accuracy, effectively categorizing data and improving pattern recognition capabilities.

Voice matching technology facilitates tasks like language-based password verification and wake-up word recognition by comparing speech segments. It utilizes speech recognition to convert speech into text sequences for comparison or directly assesses speech data similarity. Techniques like dynamic time warping (DTW) and N-grams measure similarity and process speech efficiently, supporting applications in diverse fields requiring precise language recognition and matching capabilities.

Advanced Technologies in Recognition

Various technologies play pivotal roles in physical training characteristics recognition in analyzing and distinguishing key features. These technologies encompass AI fusion, cluster analysis fusion, filter tracking line fusion, and pattern recognition. Each method serves distinct purposes, from statistical hypothesis testing to leveraging fuzzy clustering and statistical analysis for multi-sensor data processing.

AI fusion stands out prominently, integrating deep learning and other advancements to mitigate uncertainties and enhance accuracy in information fusion. This approach, encompassing fuzzy logic, neural networks, expert systems, and biological models, represents the forefront of current research in information fusion.

Feature extraction design is crucial in this context, where methods like the histogram of oriented gradients (HOG) and support vector machine (SVM) classifiers are employed to recognize human actions. HOG, originally proposed in 2005, extracts directional gradient histograms from image segments, normalized to mitigate lighting and shadow effects, ensuring robust performance in human body recognition.

Experimental results underscore the effectiveness of integrating features like HOG and improved local binary patterns (LBP), achieving recognition rates as high as 93.1% on datasets like Weizmann and 90.3% on YouTube. This fusion approach surpasses individual feature methods, demonstrating significant advancements in recognition accuracy across diverse databases. The experimental findings further highlight the proposed fusion algorithm's superiority over traditional methods. Enhanced LBP features show notable improvements over regular LBP, emphasizing the value of feature-level fusion in enhancing recognition rates.

The comparative analysis reveals that while individual techniques like HOG and LBP offer respectable accuracy, their fusion significantly boosts overall performance, showcasing the potential for broader applications in human motion recognition and beyond. Thus, the integrated approach not only enhances accuracy but also underscores the efficacy of sophisticated feature fusion techniques in advancing the field of physical training characteristics recognition.

Conclusion

In conclusion, speech-matching technology achieved precise speech fragment recognition for applications like speech passwords and wake word detection. Information fusion technologies, including AI fusion, cluster analysis, filter tracking, and pattern recognition, enhanced IoT voice devices by integrating and processing multi-sensor data, improving system reliability. These advancements enabled personalized sports training services through accurate speech recognition and responsive command execution. Addressing network security challenges, intrusion detection methods combined misuse and anomaly detection with data mining techniques to enhance detection rates while mitigating false positives, exemplified by inte. This israting fuzzy c-means and support vector machine algorithms for analyzing network attack behaviors.

Additionally, semantic information from action tags guided the transfer learning approach, overcoming challenges in training complex sports training action recognition models with limited labeled data. Furthermore, integrating blockchain technology has bolstered data integrity and security in IoT environments, ensuring robust protection against tampering and unauthorized access. These innovations underscore a holistic approach to leveraging advanced technologies for enhanced performance and reliability in diverse applications.

Journal reference:
Silpaja Chandrasekar

Written by

Silpaja Chandrasekar

Dr. Silpaja Chandrasekar has a Ph.D. in Computer Science from Anna University, Chennai. Her research expertise lies in analyzing traffic parameters under challenging environmental conditions. Additionally, she has gained valuable exposure to diverse research areas, such as detection, tracking, classification, medical image analysis, cancer cell detection, chemistry, and Hamiltonian walks.

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