Adaptive Basketball Hoop Control for Skill-Matched Training
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing basketball training systems lack integration of physical and virtual features, player skill level computation and tracking, dynamic challenge adjustment, and network connectivity for multiplayer interactions.
Innovation Solution
A hybrid basketball training system incorporating sensors for player and ball location tracking, a centralized controller for skill level computation and matching, and network connectivity for multiplayer interactions, with adjustable hoop parameters and virtual opponents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a basketball training system uses only physical equipment without virtual integration, then the system structure remains simple, but the training functionality and player engagement are limited
Solution Approach 1:
The patent combines physical basketball training equipment with virtual reality components, sensors, and digital processing systems into a unified hybrid system. This merging enables comprehensive training functionality including virtual opponents, real-time performance tracking, and adaptive challenge adjustment while maintaining system integration through a centralized controller that coordinates both physical and virtual elements.
Solution Approach 2:
The training system is designed to perform multiple functions: physical basketball shooting practice, virtual reality interaction, skill level assessment, dynamic challenge generation, and multiplayer competition. The system adapts to different skill levels and provides customized training programs, making it universally applicable to players of varying abilities while consolidating diverse training modes into a single platform.
2Adaptability or versatility
If the system tracks and computes player skill levels in real-time, then the training personalization improves, but the computational requirements and system complexity increase
Solution Approach 1:
The system automatically computes player skill levels by analyzing sensor data from ball trajectory, shooting accuracy, and performance metrics without requiring external intervention. The centralized controller continuously processes this data to dynamically adjust challenge parameters and generate personalized training programs, enabling the system to self-adapt to player ability levels in real-time.
Solution Approach 2:
The system implements continuous feedback loops where sensor data from player performance is immediately processed to update skill level assessments. This feedback mechanism allows the system to adjust virtual opponent difficulty, modify challenge parameters, and personalize training content based on real-time performance analysis, creating an adaptive learning environment that responds to player progression.
3Adaptability or versatility
If the system integrates network connectivity for multiplayer interactions, then the competitive capability enhances, but the network infrastructure and system complexity increase
Solution Approach 1:
The patent employs a centralized controller as an intermediary that manages network communications between multiple training systems and players. This mediator coordinates multiplayer matches, synchronizes virtual opponents across different locations, and handles competitive interactions through a centralized server architecture, simplifying the network infrastructure while enabling complex multiplayer and competitive functionalities.
4Measurement precision
If the system uses sensors for precise ball and player location tracking, then the measurement accuracy improves, but the system cost and complexity increase
Solution Approach 1:
The system uses multi-functional sensors that simultaneously track ball location, player position, and shooting parameters through a unified sensing architecture. The same sensor array serves multiple measurement purposes including trajectory analysis, distance calculation, and performance metric collection, reducing overall system complexity while maintaining high measurement precision across all tracking functions.
Data Source
AI summary
According to another embodiment, a method may include: receiving, at a centralized controller, a play data for players using a plurality of basketball hoop devices, the play data comprising shot data comprising ball flight trajectory data, shot angle data, shot distance data, and shot success data; calculating, by the centralized controller, a skill level for each of the plurality of players using the play data; applying, by the centralized controller, a skill leveling algorithm to the shot data to determine parameters for each basketball hoop device based on the skill level of each of the players, wherein the parameters comprise a hoop height, a hoop angle, and a hoop distance; and communicating, by the centralized controller, the parameters to local controllers at the respective basketball hoop device, wherein the local controllers control actuators at the respective basketball hoop devices to implement the parameters.