Oral Area Positioning via Multi-Algorithm Fusion
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Solution Overview
Problem
Existing oral-area positioning technologies face challenges in achieving accurate positioning results, as they struggle to effectively combine and utilize diverse data sources such as machine learning results, feature information, and inertial measurement unit data.
Innovation Solution
The proposed oral-area positioning device and method incorporate a multi-algorithmic approach, utilizing a deep learning algorithm for the first position estimation, a Hidden Markov Model (HMM) algorithm for the second position estimation based on moving probabilities, and combining these results through a calculation circuit to generate a third position estimation result.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple data sources and algorithms are used for position estimation, then positioning accuracy is improved, but device complexity increases
Solution Approach 1:
The positioning system is divided into multiple independent modules: a first position estimation module using deep learning algorithms, a second position estimation module using HMM algorithms, and a calculation module for integrating results. Each module processes specific data sources independently, allowing the system to achieve high accuracy through multiple algorithms while managing complexity through modular design.
Solution Approach 2:
The system merges multiple data sources including machine learning results, feature information, and IMU information from different oral areas. It combines estimation results from multiple algorithms (deep learning and HMM) through weighted calculation to produce a final position estimation, achieving improved accuracy by integrating diverse information sources.
2Reliability
If multiple algorithms are integrated for position estimation, then positioning reliability is improved, but calculation time increases
Solution Approach 1:
The system performs preliminary position estimations using both deep learning and HMM algorithms before final integration. By pre-calculating position estimates from multiple algorithms and preparing weight coefficients in advance, the system ensures reliable positioning while optimizing calculation efficiency through staged processing.
Solution Approach 2:
The calculation module integrates position estimation results from multiple algorithms using weighted coefficients, where weights can be adjusted based on algorithm performance and data quality. This feedback mechanism allows the system to rely on more accurate algorithms while maintaining computational efficiency by dynamically weighting results.
Data Source
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AI summary
An oral-area positioning device is provided in the invention. The oral-area positioning device includes a storage device, a positioning circuit and a calculation circuit. The storage device stores information corresponding to a plurality of oral areas. The positioning circuit obtains a target image from an oral-image extracting device, and obtains a first position estimation result according to the information corresponding to the plurality of oral areas and a first algorithm. The positioning device obtains a second position estimation result at least according to the information corresponding to the plurality of oral areas, a second algorithm and a reference image position of a reference image, wherein the reference image position is one of the oral areas. The calculation circuit generates a third position estimation result according to the first position estimation result and the second position estimation result.