Sensor-Based Ball-Delivery Training for Dynamic Feedback
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Solution Overview
Problem
Existing basketball training methods lack dynamic adjustment and personalized feedback mechanisms, leading to suboptimal skill development and repetitive training practices.
Innovation Solution
A basketball training system that utilizes sensors and machine learning to generate workout signatures, providing real-time evaluation and personalized feedback through audio and visual indicators, allowing players to compete against previous performances and adjust training accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If sensor data is collected and processed during workouts to generate workout signatures and provide real-time feedback, then training effectiveness and personalization are improved, but data storage requirements and processing complexity increase
Solution Approach 1:
The system extracts only the most relevant features from sensor data to create workout signatures, rather than storing and processing all raw sensor data. This selective extraction reduces data storage requirements while maintaining training effectiveness by focusing on key performance indicators.
Solution Approach 2:
The system transforms raw sensor data into processed workout signatures by changing the parameters from raw measurements to meaningful performance metrics. This parameter transformation reduces data complexity and storage needs while improving training effectiveness through actionable insights.
2Adaptability or versatility
If real-time sensor data processing and workout signature generation are implemented, then personalized feedback and dynamic workout adjustment are improved, but system complexity and computational requirements increase
Solution Approach 1:
The system implements feedback loops where workout signatures are generated from sensor data and used to provide real-time personalized feedback and adjust workouts dynamically. This feedback mechanism improves adaptability by continuously adapting training based on measured performance.
Solution Approach 2:
The system automatically generates workout signatures and provides feedback without requiring manual intervention or complex external processing. This self-service approach reduces system complexity by handling data processing and analysis internally through automated algorithms.
3Measurement precision
If comprehensive sensor data is collected during workouts including biomechanical and biometric inputs, then measurement precision and performance evaluation are improved, but data processing time and energy consumption increase
Solution Approach 1:
The system collects comprehensive sensor data including biomechanical and biometric inputs to ensure measurement precision, but processes only the essential features needed for workout signature generation. This partial processing approach maintains evaluation accuracy while reducing energy consumption by avoiding unnecessary processing of all collected data.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for a basketball training system. One method can include, while a ball delivery system is executing a workout for a user, obtaining sensor data from one or more sensors of the ball delivery system; generating a workout signature using the sensor data, the workout signature including representations of actions that occurred at different times during the workout; evaluating the generated workout signature; and providing a result of evaluating the generated workout signature using an indicator of the ball delivery system.