Mobile Flight-Path Data Processing With Camera-Limit Compensation
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Solution Overview
Problem
Existing sports performance tracking devices, such as launch monitors and golf simulators, lack comprehensive data analysis capabilities, are expensive, bulky, and limited to controlled environments, failing to provide mobile, cost-effective solutions with personalized insights and interactive features.
Innovation Solution
A system architecture that transforms a standard mobile device into a versatile launch monitor and simulator, leveraging machine learning and AI to capture, process, and deliver real-time data analytics, including video editing and personalized improvement suggestions, while compensating for mobile device limitations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional launch monitors and golf simulators are used, then measurement precision and data capture capability are improved, but device complexity, cost, and portability deteriorate
Solution Approach 1:
The patent introduces a cloud-based processing platform as an intermediary between mobile devices and comprehensive data analysis. Mobile devices capture raw data using simple cameras, then transmit to cloud servers that perform complex processing, object detection, and analytics. This mediator approach allows high measurement precision without requiring complex hardware on the mobile device side.
Solution Approach 2:
The patent replaces complex mechanical/optical measurement systems with computational approaches. Instead of using sophisticated physical sensors and optical systems on mobile devices, the system uses software-based object detection algorithms (computer vision) to extract measurement data from simple video recordings, substituting mechanical complexity with computational intelligence.
2Adaptability or versatility
If comprehensive data analysis and personalized insights are added, then adaptability and functionality are improved, but device complexity and cost worsen
Solution Approach 1:
The patent creates a universal platform that handles multiple functions through a single integrated system. The cloud-based platform provides object detection, trajectory analysis, statistics tracking, video editing, and personalized insights all through one service, making the system adaptable to various needs without requiring separate specialized devices for each function.
Solution Approach 2:
The system implements feedback loops where captured data is processed to generate insights and recommendations that feed back to improve future measurements. The platform analyzes performance data and provides personalized suggestions for improvement, creating an adaptive system that learns from user interactions and improves its analysis capabilities over time.
3Measurement precision
If mobile device limitations are compensated for, then measurement precision is improved, but processing time and computational resources worsen
Solution Approach 1:
The patent segments the processing workload into two distinct phases: data collection on mobile devices and comprehensive analysis on cloud servers. The mobile device only performs simple video capture, while time-consuming computational tasks like object detection, trajectory calculation, and statistics generation are segmented to the cloud environment, minimizing local processing time while maintaining precision.
Data Source
AI summary
A system includes a platform configured to receive a video recording of a flight path of an object and initiate back-end processing of the video recording. The back-end processing may include data modeling, object detection operations, normalizing operations, adjusting normalize data based on meta_camera specifications to compensate for limitations of the user device used to create the video recording, and applying mathematical techniques to the adjusted data to derive metrics relating to the flight path of the object and to generate an enhanced video clip of the flight path. In an embodiment, the back-end processing may further include generating a trace line of the flight path of the object, adding the trace line to the enhanced video clip, transmitting the enhanced video clip and the metrics to the user device, and storing the enhanced video clip and the metrics in back-end storage.