Electronic Keyboard Hand Tracking With Generative Finger Correction
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Solution Overview
Problem
Existing techniques struggle to accurately estimate the shape of a user's hand while playing a musical instrument, particularly in situations where fingers are hidden or blurred, leading to inaccuracies in performance analysis.
Innovation Solution
An information processing method that utilizes a trained generative model to correct finger position data based on image and performance data, incorporating a deep neural network to enhance accuracy by supplementing unclear or hidden hand positions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image analysis is used to estimate hand shape, then the system can capture performance data, but the measurement precision deteriorates when fingers are hidden or blurred
Solution Approach 1:
The patent combines multiple data sources (image data, performance data from sensors, and initial finger position data) into a unified input for the generative model. This merging of information from different modalities compensates for the weaknesses of individual sources, allowing accurate hand shape estimation even when image data alone is insufficient due to hidden or blurred fingers.
Solution Approach 2:
The generative model acts as an intermediary that processes and reconciles conflicting or incomplete information from image data and performance data. It uses the performance data as a mediator to infer the positions of hidden fingers, bridging the gap between what is visible in images and the actual hand configuration during performance.
2Measurement precision
If only image data is used for finger position detection, then the system complexity is low, but the measurement precision deteriorates in challenging conditions
Solution Approach 1:
The generative model serves multiple functions: it corrects finger position data, infers hidden finger positions, and integrates information from different data sources. This multi-functionality allows the system to achieve high measurement precision without adding separate specialized modules for each function, thereby managing complexity while improving accuracy.
Solution Approach 2:
The system transforms the problem from direct image analysis to a generative modeling approach that operates in a different parameter space. By using performance data and initial finger position data as additional parameters, the system changes the input characteristics to enable more accurate finger position estimation under challenging imaging conditions.
3Measurement precision
If a generative model is used to correct finger position data, then the measurement precision improves, but the device complexity increases
Solution Approach 1:
The generative model is trained offline using labeled data, and once trained, it automatically corrects finger position data without requiring manual intervention or complex real-time processing infrastructure. This self-service capability allows the system to achieve high measurement precision while keeping the runtime complexity manageable, as the heavy computational work is performed during the training phase rather than during actual performance analysis.
Data Source
AI summary
An information processing method is realized by a computer system, and includes acquiring input data including image data representing an image including at least a hand of a user playing a musical instrument, first finger position data representing a position of each of a plurality of analysis points on the hand, and performance data representing a performance of the musical instrument, and processing the input data using a trained generative model, thereby generating second finger position data in which the position of each of the plurality of analysis points in the first finger position data is corrected in accordance with a position of the hand represented by the image data and the performance represented by the performance data.