Sensor-Guided Training Data Adjustment for Adaptive Task Difficulty
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Solution Overview
Problem
Existing wellness management programs lack personalization and adaptability, failing to effectively monitor user performance and adjust training programs based on individual capabilities and feedback, leading to suboptimal health improvement outcomes.
Innovation Solution
A system that utilizes sensors to monitor user performance on physical and cognitive tasks, adjusts training data based on performance metrics, and incorporates user feedback to customize the difficulty level of exercises, using machine learning models to refine predictions and recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If online systems provide customization based on user parameters (age, gender), then some personalization is achieved, but the systems cannot monitor or evaluate user performance and adjust training programs accordingly
Solution Approach 1:
The system continuously collects sensor data during exercise performance, processes this feedback to evaluate user performance, and automatically adjusts training program parameters based on the evaluation results, creating a closed-loop adaptive system
Solution Approach 2:
The system autonomously monitors user performance through integrated sensors, evaluates progress against goals, and modifies training recommendations without requiring manual input or professional intervention, enabling self-directed wellness management
2Reliability
If the training program is highly customized and adaptive, then effectiveness is improved, but the system complexity and computational requirements increase
Solution Approach 1:
The training program is divided into modular exercise components with adjustable parameters (intensity, duration, frequency), allowing selective customization based on individual performance data without requiring complete program redesign
Solution Approach 2:
The training program transitions from static to dynamic, automatically adjusting exercise parameters in real-time based on sensor feedback, enabling the system to adapt to changing user capabilities and goals while maintaining manageable complexity through algorithmic automation
3Productivity
If the system monitors and adjusts training data based on performance, then user performance improves, but the difficulty of detecting and measuring performance increases
Solution Approach 1:
The system employs multi-functional sensors that simultaneously capture multiple performance metrics (position, orientation, movement speed, exercise form) through a single integrated measurement framework, simplifying the detection and measurement process while enriching performance data
Data Source
AI summary
Disclosed are embodiments for facilitating generation of training data, which includes information regarding a set of physical tasks or cognitive tasks to be performed by a user (e.g., separately or concurrently), and adjusting the training data based on a performance of the user. The tasks may be directed to improving a specific health condition (e.g., balance capabilities of the user). The system obtains sensor data that is representative of user actions in performance of the tasks from sensors of a user device. The sensor data may be processed to determine performance data (e.g., physical or cognitive performance) that is indicative of the performance of the user in performing the tasks. The training data may be adjusted based on the performance data (e.g., one or more tasks or task parameters are adjusted to increase or decrease a difficulty level for the user in performing the tasks).