Object Position Prediction Using Statistical Distribution Analysis
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Solution Overview
Problem
In medical environments, motion detection for tracking the position of objects, such as patients, is challenging due to 'noisy' conditions like occlusions and prediction noise, which can lead to sensing errors and algorithmic uncertainty.
Innovation Solution
An apparatus and method that predict the position of an object over time by obtaining multiple predictions with validity indications, selecting subsets based on statistical distributions, and determining changes to select the most accurate prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional position tracking methods are used in medical environments, then the system can detect object position, but the tracking accuracy deteriorates due to noisy conditions including occlusions and prediction noise
Solution Approach 1:
The patent segments the set of predictions into multiple subsets based on statistical distribution analysis. By dividing predictions into different groups and analyzing their distributions separately, the system can identify and select the most reliable subset, thereby improving position tracking accuracy in noisy medical environments.
Solution Approach 2:
The patent implements a feedback mechanism by obtaining validity indications for each prediction and using statistical distribution analysis to evaluate prediction quality. This feedback loop allows the system to continuously adjust and select the most accurate predictions, improving overall tracking reliability despite occlusions and noise.
2Measurement precision
If multiple predictions are obtained and analyzed using statistical distributions, then the position tracking accuracy improves, but the computational complexity and processing time increase
Solution Approach 1:
The patent applies partial action by selecting only the most relevant subsets of predictions for detailed analysis. Instead of processing all predictions equally, the system identifies key subsets based on statistical distributions and focuses computational resources on those, reducing overall processing complexity while maintaining accuracy.
Solution Approach 2:
The patent changes the parameter of prediction evaluation from individual assessment to statistical distribution-based assessment. By transforming the evaluation criterion and organizing predictions into subsets with different statistical characteristics, the system simplifies the selection process and reduces computational burden.
3Measurement precision
If validity indications are obtained for each prediction, then the selection accuracy improves, but the data processing requirements and system complexity increase
Solution Approach 1:
The patent extracts only the essential validity indications needed for statistical distribution analysis, rather than processing all possible prediction attributes. By selecting and focusing on key validity metrics, the system reduces information processing load while maintaining sufficient accuracy for reliable prediction selection.
Data Source
AI summary
Multiple predictions about the position of an object during a time period may each indicate the position of the object at a respective time during the time period. Respective validity indications corresponding to the multiple predictions may each indicate an accuracy of the corresponding prediction. Whether a change has occurred in a distribution of the predictions from a first subset of predictions to a second subset of predictions during the time period may be determined. If the change has occurred, a prediction from the first subset of predictions or the second subset of predictions may be selected, based on the validity of the predictions and/or the detection of a motion, as a best indication of the position of the object.