Grip Sensor Activity Modeling for Left-Right Fitness Coaching
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Solution Overview
Problem
Current fitness and health trackers lack contextual information and fail to provide active user feedback or coaching, limiting their value in personalized health and performance improvement.
Innovation Solution
A human internet of things platform that utilizes embedded sensors in grip-based devices to capture and process contextual data, integrating motion, physiology, and environmental information to provide personalized coaching and predictive analytics through a multi-dimensional information modeling system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If general-purpose data capture is used to collect user activities data, then data collection is simple and passive, but contextual information is lacking and user engagement is limited
Solution Approach 1:
The patent segments the data capture system into multiple specialized sensor modules (motion sensors, physiological sensors, environmental sensors) that each capture specific types of contextual information. This segmentation allows comprehensive data collection while maintaining modularity and manageable complexity in the overall system architecture.
Solution Approach 2:
The grip-based device serves multiple functions: it acts as both a data collection interface and a processing unit, while also serving as a universal platform that can adapt to various activities and user needs. The system integrates multiple sensor types and processing capabilities into a single multi-functional device that can handle diverse monitoring requirements.
2Ease of operation
If passive data capture is used, then device operation is simple, but user feedback and coaching capabilities are absent
Solution Approach 1:
The system implements multi-level feedback mechanisms where captured data is processed to generate real-time insights, personalized coaching recommendations, and performance feedback delivered to users. The feedback loop transforms raw sensor data into actionable guidance, enabling users to improve their activities while the system continues to operate automatically.
Solution Approach 2:
The system performs self-processing of captured data through embedded processing units that automatically analyze sensor inputs, generate insights, and provide coaching without requiring constant user intervention. The device serves itself by autonomously converting raw data into valuable user feedback, reducing operational complexity while enhancing productivity.
3Device complexity
If simple dashboard representation is used, then data presentation is easy to implement, but data value and user engagement are limited
Solution Approach 1:
The patent transforms data presentation from traditional two-dimensional dashboards to multi-dimensional visualizations that incorporate temporal, spatial, and contextual dimensions. This dimensional expansion allows the system to present complex relationships and patterns in the data that would be invisible in simple flat displays, thereby preserving more actionable information while remaining visually accessible.
Data Source
AI summary
A method performed in a computer-implemented platform includes obtaining sensor data from sensors embedded in a first grip and a second grip of an item of fitness equipment while a user exercises using the item of fitness equipment. The user interacts with the first grip and the second grip while exercising using the item of fitness equipment. The method also includes analyzing the sensor data to determine differences in performance between the left side of the user's body and the right side of the user's body and providing personalized activity for the user to compensate for the differences.