Golf Swing Evaluation System Using Distributed Sensor Segmentation
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Solution Overview
Problem
Current systems lack comprehensive solutions for providing golfers with personalized feedback and community-based data analysis to improve their golfing techniques and enjoyment, as they do not effectively collect, store, and utilize golf swing dynamics and ball flight data to offer targeted coaching, equipment recommendations, and community interaction.
Innovation Solution
A golf swing evaluation system that collects and analyzes golf swing dynamics and ball flight data, using sensors in golf clubs, shoes, and apparel to provide real-time feedback and store data in a community hub, allowing for equipment recommendations, coaching, and community interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If comprehensive golf data collection systems are implemented using multiple sensors, then measurement precision and data quality improve, but device complexity and cost increase
Solution Approach 1:
The system divides the golf swing evaluation into multiple independent measurement components: club head sensors detect impact parameters, foot sensors measure weight transfer, and apparel sensors track body rotation. Each sensor type focuses on a specific aspect of the swing, allowing high measurement precision without requiring a single overly complex device.
Solution Approach 2:
The system employs universal data processing architecture that handles multiple types of sensor inputs (club, foot, apparel sensors) through a common analysis platform. This multi-functional approach allows the same processing system to evaluate various swing parameters simultaneously, reducing overall system complexity while maintaining comprehensive measurement capabilities.
2Productivity
If real-time feedback systems are deployed with multiple sensors, then productivity and immediate coaching capability improve, but use of energy and device complexity increase
Solution Approach 1:
The system implements periodic data sampling and processing cycles rather than continuous real-time analysis. Sensors collect data at specific intervals during the swing motion, and processing occurs in discrete batches. This approach provides sufficiently fast feedback for coaching purposes while significantly reducing energy consumption compared to continuous processing.
Solution Approach 2:
The system performs preliminary data filtering and preprocessing at the sensor level before transmission to the central processing unit. Basic validation and initial analysis are conducted locally on each sensor device, reducing the computational burden on the main system and enabling faster feedback delivery with lower overall energy requirements.
3Adaptability or versatility
If comprehensive golf swing analysis and community features are integrated, then adaptability and user benefit improve, but device complexity and loss of information increase
Solution Approach 1:
The system segments user data into distinct categories: individual swing metrics, community statistics, equipment profiles, and coaching preferences. Each data category is managed independently with its own storage and processing rules. This segmentation enables高度 personalization through targeted data analysis while preventing information overload and management complexity.
Solution Approach 2:
The system introduces standardized data interchange formats and protocols as intermediaries between different data sources (sensors, community inputs, equipment databases). These standardized interfaces facilitate seamless integration of diverse data types while maintaining data integrity and reducing management complexity through consistent handling procedures.
Data Source
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AI summary
Systems and methods are described for providing coaching, training, or equipment specification information to individual golfers based on data generated during their individual golf swings. Additionally, data hubs are described that provide information and services to individuals based on data collected for a community of multiple golfers. Such community data hub systems and methods may provide one or more of the following: (a) storage of scoring data, swing data, ball flight data, and/or equipment data for multiple golfers; (b) at least some level of individual access to the stored data for the community; and/or (c) electronic interaction between golfers within the community.