Generative AI Kinematic Measurement With Athlete Feedback Tagging
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing systems struggle to accurately determine the underlying reasons for performance changes in athletes, as they primarily rely on transducer-based sensors that fail to account for subjective factors such as health, nutrition, or injury, making it difficult to discern the actual effect of internal and external factors on athletic performance.
Innovation Solution
A measurement system that integrates an accelerometer, gyroscope, and generative AI model to capture kinematic data, request and process natural language feedback from athletes, and tag the data with objective measures, enabling a machine learning model to classify performance factors without further user input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If transducer-based sensors are used to measure athlete performance, then objective kinematic data can be captured, but subjective factors such as health, nutrition, or injury cannot be accounted for
Solution Approach 1:
The patent combines objective transducer-based sensor data with subjective natural language feedback from athletes into a unified measurement system. The system integrates accelerometer and gyroscope data with AI-processed textual responses to create a comprehensive performance profile that accounts for both physical metrics and subjective factors like health status, nutrition, and injury conditions.
Solution Approach 2:
The patent introduces an intermediary AI processing layer that converts subjective natural language feedback into structured data formats compatible with objective sensor measurements. This intermediary component enables the system to reconcile and correlate subjective athlete reports with objective kinematic data, creating a unified performance analysis framework.
2Loss of information
If natural language feedback is collected from athletes during activity, then subjective factors can be captured, but the system complexity increases
Solution Approach 1:
The patent replaces complex manual data collection and processing mechanisms with an AI-based automated system. Instead of requiring complex interfaces for athletes to input health, nutrition, and injury information, the system uses natural language processing to automatically extract and structure this subjective data, simplifying the user interface while maintaining comprehensive data capture.
Solution Approach 2:
The system enables athletes to self-report their subjective state through natural language during or after activity. The AI processing component automatically interprets and structures this self-provided information without requiring external intervention or complex data entry procedures, allowing athletes to serve their own data needs through intuitive feedback mechanisms.
3Productivity
If real-time feedback requests are made during athletic activity, then performance adjustments can be made, but the athlete's attention is diverted from the activity
Solution Approach 1:
The patent implements periodic feedback requests triggered by specific conditions such as completion of workout intervals, rather than continuous interruptions. The system asks for natural language feedback at predetermined periods or when specific performance thresholds are reached, allowing athletes to provide information at convenient moments without constant distraction from their activity.
Solution Approach 2:
The system incorporates feedback mechanisms that allow athletes to provide natural language responses about their state, and the AI processes this feedback to generate personalized performance insights and recommendations. This feedback loop enables real-time optimization by incorporating athlete self-assessments into performance analysis and adjustment strategies.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system effectively provides objective measurements of athlete performance by correlating subjective feedback with kinematic data, allowing for real-time adjustments and improved pacing strategies based on health and injury detection.
Implementation Method 1
a housing that includes an accelerometer and a gyroscope
Implementation Method 2
a housing that includes an accelerometer and a gyroscope
Data Source
AI summary
The technology relates to measurement systems and methods for measure kinematic data of an athlete during athletic activities. In an example, the system includes a housing that includes an accelerometer and a gyroscope; at least one processor; and at least one memory. The system performs operations including generating kinematic data for the athlete during the athletic activity; detecting an occurrence of a feedback trigger condition; surfacing a feedback request for a natural language response from the athlete during the athletic activity; receiving a natural language response from the athlete during the athletic activity; generating an athlete-response prompt for a generative artificial intelligence (AI) model; providing the athlete-response prompt as input to the generative AI model; receiving, from the generative AI model in response to the athlete-response prompt, a standardized tag for the defined categories; and tagging the kinematic data with the standardized tag.