Remote Sensory Testing Data Analysis Using Sports Tree Classification
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Solution Overview
Problem
Current sensory ability testing and training methods require individuals to be physically present for data collection and analysis, limiting remote testing capabilities and efficiency in generating personalized training plans.
Innovation Solution
A system and method for remote sensory ability testing, where data is collected at a remote location and analyzed at a central location using a sports tree function to identify evaluation levels and generate comparative profiles, enabling the development of tailored training programs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If sensory testing is performed at a remote location with data collected and analyzed centrally, then testing accessibility and efficiency are improved, but data analysis complexity and processing requirements increase
Solution Approach 1:
The system divides the sensory testing process into distinct segments: data collection at remote locations using portable devices, data transmission through networks, and centralized analysis using sophisticated algorithms. This segmentation allows simple remote testing interfaces while handling complex analysis centrally, resolving the contradiction between ease of operation and analysis complexity.
Solution Approach 2:
A centralized server acts as an intermediary between remote testing devices and analysis algorithms. The server receives raw data from portable testers, processes it through statistical models and peer comparison algorithms, and generates comprehensive sensory profiles. This intermediary handles the computational complexity while keeping remote user interfaces simple and accessible.
2Measurement precision
If peer data is collected and analyzed to generate comparative sensory profiles, then measurement precision and personalization are improved, but data processing time and computational resources increase
Solution Approach 1:
The system pre-collects and stores sensory data from multiple peers in a centralized database before individual testing occurs. When a subject is tested, their data is immediately compared against the pre-existing peer database, eliminating the need for real-time data collection and reducing processing time while maintaining high measurement precision through comprehensive peer comparison.
Solution Approach 2:
The system creates standardized data templates and peer comparison models that can be rapidly replicated and applied to individual subjects. By using standardized evaluation frameworks and pre-processed peer data copies, the system achieves precise measurements without requiring extensive custom processing for each subject, thereby reducing overall processing time.
3Measurement precision
If comprehensive demographic and sensory data is collected for each subject, then profile accuracy and training program personalization are improved, but data storage requirements and privacy concerns increase
Solution Approach 1:
The system extracts only the essential and relevant features from comprehensive subject data for storage and comparison, such as key sensory thresholds, response patterns, and demographic identifiers. Non-essential information is either summarized or excluded, reducing storage requirements while maintaining the accuracy needed for meaningful peer comparisons and personalized training program development.
Data Source
AI summary
This invention is related to systems and methods of analyzing sensory ability data. One embodiment of the present invention includes a method comprising the steps of receiving data from a remote location. The data is comprised of sensory ability data and demographic data associated with a subject. The data may then be stored. Further, the method includes identifying a potential evaluation level associated with the subject. The evaluation level is identified, at least in part, utilizing a sports tree function. The method also includes retrieving peer data associated with the potential evaluation level. Additionally, the method includes determining when the peer data is statistically powerful for use in generating a comparative profile of the sensory ability data associated with the subject. Additional embodiments develop training programs based on one or more training program functions.