Description:Complete Description
Field of the Invention: This invention pertains to sports training methodologies, specifically focused on a novel integrated training system designed to improve volleyball player performance through the combination of Speed, Agility, Quickness (SAQ) training and Super Circuit training. The system utilizes Artificial Intelligence (AI) and Machine Learning (ML) to optimize training sessions, personalize fitness regimens, monitor performance, predict injury risks, and provide real-time feedback, all tailored specifically for volleyball athletes.
Background: Volleyball is a high-intensity sport where players are required to perform explosive movements repeatedly, such as jumping, serving, spiking, and passing. To achieve peak performance, volleyball players must develop various physical attributes including speed, agility, strength, endurance, and skill proficiency. Jumping ability, for instance, is a critical factor, with players often jumping 36 to 105 times in a 3-set match, placing significant strain on their lower body and joints. Despite the widespread availability of individual training programs focused on improving speed, agility, and strength, there remains a gap for a holistic, integrated system that combines multiple training methodologies, specifically for volleyball. Furthermore, the advent of AI and ML provides an opportunity to enhance the training process, offering personalized, data-driven feedback and continuously adapting training based on real-time performance metrics.
Statement: This invention introduces a novel training system combining SAQ training and Super Circuit training, specifically designed for volleyball players to enhance their physical fitness, physiological health, and skill-based performance. The training regimen leverages AI and ML to create a personalized experience for each player, optimize training intensity, monitor physiological variables, predict injury risks, and deliver real-time feedback on performance.
• SAQ Training aims to enhance an athlete's multi-directional explosive speed, agility, and neuromuscular efficiency, essential for volleyball movements such as jumping, spiking, and quick direction changes.
• Super Circuit Training focuses on building strength, endurance, and agility by alternating between strength exercises (e.g., core and lower body training) and sport-specific agility drills.
• AI and ML technologies enable the system to continuously adapt the training plan for each player, based on real-time data such as movement patterns, heart rate, speed, and technique.
The combined training system provides a novel, personalized approach that not only improves athletic performance but also prevents overtraining and injuries through continuous monitoring and predictive analytics.
Objectives of the Invention
1. To provide a novel training methodology combining SAQ and Super Circuit training with AI and ML technologies to improve volleyball performance.
2. To personalize training plans based on each player’s progress, physical condition, and skill needs.
3. To enhance physical fitness (speed, agility, strength, endurance) and physiological health (heart rate, blood pressure) using data-driven adjustments.
4. To improve skill performance (passing, serving, jumping) through AI-driven feedback and corrective suggestions.
5. To utilize predictive analytics to prevent injuries and overtraining, ensuring long-term player health and performance.
6. To create a dynamic training system that adapts to player progress, ensuring continuous improvement and peak performance.
System Overview
1. SAQ Training System: The SAQ system utilizes progressive learning techniques to enhance the athlete's ability to perform explosive, multi-directional movements that are critical for volleyball. The exercises are designed to improve:
• Speed: Enhancing the player’s ability to accelerate quickly, especially when moving toward the ball or reacting to the opponent.
• Agility: Training quick, directional changes, which are vital for actions like diving, blocking, or evading opponents.
• Quickness: Increasing reaction time, which is essential in volleyball, where players must respond rapidly to fast-moving balls.
Through AI-powered motion capture systems and real-time feedback, the training system monitors the player's movements, comparing them to ideal performance models and suggesting corrective actions or progressions.
2. Super Circuit Training System: Super Circuit training is a high-intensity fitness regimen that alternates between strength exercises (core, lower body) and agility drills tailored to volleyball. It aims to:
• Enhance cardiovascular fitness for sustained energy during matches.
• Build core strength for balance and explosive jumps.
• Increase leg power and lower body endurance to improve jump height and stability.
• Boost agility to facilitate fast, responsive movements on the court.
AI is integrated to monitor the intensity of exercises based on physiological data, adjusting the workload to ensure optimal performance without causing fatigue. Machine learning algorithms track the player’s progress over time and predict future training needs.
3. AI and Machine Learning Integration: The incorporation of AI and ML enables the training system to become adaptive, responsive, and data-driven, allowing for real-time adjustments and long-term optimization. Key features of AI and ML integration include:
• Personalized Training Plans: AI analyzes data from wearables (heart rate, jump height, speed, etc.) to create personalized training schedules. As the player improves, the system adapts by increasing training intensity or shifting focus to areas needing improvement.
• Real-Time Performance Monitoring and Feedback: Using AI-powered motion capture and sensor-based systems, the training program can monitor biomechanics, movement patterns, and technique. This provides players with instant feedback to correct form or enhance technique, such as adjusting foot placement during an agility drill or optimizing posture during a jump.
• Predictive Analytics for Injury Prevention: By analyzing patterns in player data (e.g., movement efficiency, heart rate variability), the AI system can predict potential injuries before they occur. If a player’s form deteriorates or if there are signs of fatigue or strain, the system can adjust the training program to reduce the risk of injury.
• Physiological Monitoring: AI tracks critical physiological variables like systolic and diastolic blood pressure, heart rate, and respiratory rate in real-time. If the data indicates overtraining or physical stress, the system can automatically suggest rest periods or a reduction in training intensity.
• Skill Development Feedback: AI-powered systems can analyze volleyball-specific movements such as passing, serving, and spiking, and provide data-driven insights for skill improvement. For example, the system can assess the trajectory of a serve and recommend adjustments based on the player’s current form.
4. Data Dashboard for Coaches and Athletes:
• Visualization and Reports: Coaches and athletes can access a user-friendly dashboard that visualizes performance data, training progress, and physiological health metrics. The dashboard provides insights into areas of strength, as well as areas needing improvement, to facilitate data-driven decision-making.
• Performance Analysis: The dashboard also includes performance analytics, such as improvement in jump height, agility, and skill execution, along with predictive injury analytics, enabling coaches to make informed decisions about future training sessions.
5. Adaptive Learning System: The ML algorithms continuously learn from the player’s progress, dynamically adjusting the difficulty of exercises, rest periods, and the mix of training types (e.g., strength vs. agility) to maximize improvements while minimizing risk. The system adapts based on:
• Performance trends (e.g., improvement in jump height, agility, or passing accuracy).
• Physiological data (e.g., signs of fatigue or overtraining).
Feedback from the player (e.g., subjective assessment of training difficulty or soreness).
Key Components
1. Sensors and Devices:
1. Motion Sensors: Attached to key body parts like wrists, ankles, and torso to monitor movement dynamics such as speed, acceleration, and orientation.
2. Heart Rate Monitors: Integrated into a chest strap or wristband to continuously measure and record the athlete’s heart rate during training.
3. Muscle Activity Sensors (EMG): Placed on major muscle groups to assess muscle engagement and fatigue levels.
4. Pressure Sensors: Embedded in shoes to measure force distribution and impact when jumping, landing, or changing direction.
2. Data Transmission Units
1. Wireless Transmitters: Embedded within each device to facilitate real-time data transmission to the central processing unit or cloud-based storage.
2. Receivers: Part of the central system that collects data from all sensors for analysis.
3. User Interface
1. Wearable Displays: Small screens on wristbands or integrated into clothing to provide real-time feedback and alerts directly to the athlete.
2. Mobile App: Connects with the sensor system to display comprehensive training data, allowing athletes and coaches to review performance metrics and receive personalized advice.
Functionalities
1. Real-Time Data Collection: Capture and transmit data on various physiological and biomechanical parameters as the athlete perform. Allow for immediate adjustments in training based on live feedback.
2. Performance Analysis: Uses AI algorithms to analyze collected data to determine strengths, weaknesses, and areas requiring improvement. Provide insights into athlete performance trends over time.
3. Feedback Mechanism: Generate instant feedback on form, technique, and exertion to help athletes adjust their movements in real-time. Offer long-term suggestions for improvement based on aggregated data.
4. Injury Prevention: Monitor signs of fatigue and strain to predict and prevent potential injuries. Alert athletes and coaches to unsafe biomechanical patterns.
System Working procedure:
1. Initial Setup: Upon enrollment, players are equipped with wearable devices (e.g., heart rate monitor, smart shoes) and motion capture sensors. The AI system gathers baseline data, including physical fitness, movement patterns, and skill levels.
2. Data Collection and Performance Monitoring: During training sessions, the system collects data on the player’s physical performance (e.g., speed, agility, strength, heart rate) and skill execution (e.g., passing, serving). AI algorithms analyze this data in real-time, adjusting the workout intensity and focus areas to ensure optimal progress.
3. Personalized Adjustments: Based on the performance data, the AI system personalizes the training regimen, adjusting intensity, drill complexity, and recovery periods. If a player shows signs of fatigue or poor form, the system automatically lowers the intensity and recommends rest.
4. Feedback and Skill Improvement: Real-time feedback is delivered to the player via the system interface, whether through mobile apps or wearable devices. This feedback helps players refine their techniques, improve their performance, and track their improvements over time.
5. Injury Prevention: By monitoring key physiological variables and analyzing patterns in the data, the system detects potential signs of injury or overtraining. The AI algorithms adjust the training plan to minimize risks and optimize the player's physical conditioning.
6. Continuous Monitoring and Adaptation: As training progresses, the system continuously adapts the regimen to reflect the player's improvement, ensuring that the training remains effective and challenging. The system also provides coaches with insights and analytics to make data-driven decisions regarding training strategies.
, Claims:Claim 1: A combined training system for enhancing physical fitness, physiological health, and skill performance in varsity volleyball players, comprising: A Speed, Agility, and Quickness (SAQ) training module, a Super Circuit training module, an Artificial Intelligence (AI) powered motion capture system for tracking player movements, a real-time physiological monitoring system for tracking player health metrics, a machine learning (ML) algorithm that dynamically adapts training intensity and program customization based on the player’s physiological data and skill performance, an injury prediction mechanism based on player data, including movement patterns, jump height, passing, and serving skills, a feedback system that provides real-time corrective suggestions to improve player technique and performance.
Claim 2: The system of claim 1, wherein the AI-powered motion capture system provides real-time monitoring and analysis of a player’s movement patterns during training sessions.
Claim 3: The system of claim 1, wherein the machine learning algorithm continuously analyzes player data to predict potential injury risks and adjust training intensity to mitigate overtraining.
Claim 4: The system of claim 1, further comprising a player-specific dashboard that delivers personalized recommendations for improving performance based on continuous data analysis from the AI and ML systems.
Claim 5: The system of claim 1, wherein the Super Circuit training module includes a combination of cardiovascular, strength, and skill-focused exercises designed to enhance overall volleyball-specific performance.
Claim 6: The system of claim 1, wherein the real-time physiological monitoring system includes sensors for measuring heart rate, muscle fatigue, jump height, and other physiological parameters relevant to the player’s performance.
Claim 7: A method for enhancing the physical fitness, physiological health, and skill performance of volleyball players, comprising the steps of: Implementing a combined SAQ and Super Circuit training program, using AI-powered motion capture to monitor player movements during training, monitoring player physiological data in real-time using a monitoring system, Analyzing player data using machine learning algorithms to predict injury risks and adjust training intensity dynamically, providing real-time corrective feedback on skill performance, including jump height, passing, and serving.
Claim 8: The method of claim 7, wherein the real-time feedback provided to the player includes adjustments to training exercises based on their current physical and skill performance metrics.
Claim 9: The system of claim 1, wherein the training system is capable of evolving based on ongoing player performance data, optimizing training plans over time for sustained improvement.