Unsupervised Gesture Classification via Neuromuscular Signal Clustering
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Solution Overview
Problem
Existing systems for recognizing and modeling human gestures require large sets of labeled data, which are prone to human errors and difficult to acquire, and fail to capture meaningful features like force applied during gestures.
Innovation Solution
An apparatus comprising neuromuscular sensors and a processor that uses unsupervised machine learning to train a classification model based on unlabeled neuromuscular signals, allowing for rapid learning of gestures from few training samples and capturing features indicative of force applied, such as through clustering techniques and principal component analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If supervised learning models are trained with large sets of labeled data, then gesture recognition accuracy is improved, but data acquisition time and human labeling effort increase significantly
Solution Approach 1:
The system performs self-labeling by automatically identifying gesture types and force levels from neuromuscular signals without human annotation. The unsupervised learning algorithm clusters signals into gesture categories and extracts force information, eliminating the need for human labelers while maintaining recognition accuracy.
Solution Approach 2:
The patent changes the fundamental parameter of training data requirements by using unsupervised learning instead of supervised learning. This allows the system to learn from unlabeled data, dramatically reducing data acquisition time while still achieving accurate gesture recognition and force estimation through automatic feature extraction and clustering.
2Measurement precision
If supervised learning with human-labeled data is used, then gesture classification is achieved, but human bias and labeling errors are introduced
Solution Approach 1:
The system eliminates human involvement in the labeling process by using unsupervised learning algorithms that automatically cluster neuromuscular signals into gesture categories. This self-service approach removes human bias and labeling errors entirely, as the clustering is performed objectively by the algorithm based on signal characteristics.
Solution Approach 2:
The patent replaces the mechanical process of human labeling with an automated computational process. Instead of human experts manually annotating data (mechanical system), an unsupervised learning algorithm performs clustering and classification (computational system), eliminating human errors and bias while improving data quality and reliability.
3Loss of information
If traditional machine learning approaches are used, then gesture recognition is achieved, but force information during gesture performance is not captured
Solution Approach 1:
The patent segments the gesture recognition task into two distinct components: gesture classification and force estimation. By separating these functions, the system can extract force information as a distinct feature from neuromuscular signals while maintaining gesture recognition accuracy. This segmentation allows force information to be captured without overwhelming system complexity.
Solution Approach 2:
The patent adds a new dimension to gesture recognition by incorporating force estimation alongside gesture classification. Instead of only identifying what gesture is being performed, the system also estimates the force level, creating a multi-dimensional output that provides more comprehensive information about gesture performance without requiring fundamentally different sensing hardware.
Data Source
AI summary
Methods and apparatus for training a classification model and using the trained classification model to recognize gestures performed by a user. An apparatus comprises a processor that is programmed to: receive, via a plurality of neuromuscular sensors, a first plurality of neuromuscular signals from a user as the user performs a first single act of a gesture; train a classification model based on the first plurality of neuromuscular signals, the training including: deriving value(s) from the first plurality of neuromuscular signals, the value(s) indicative of distinctive features of the gesture including at least one feature that linearly varies with a force applied during performance of the gesture; and generating a first categorical representation of the gesture in the classification model based on the value(s); and determine that the user performed a second single act of the gesture, based on the trained classification model and a second plurality of neuromuscular signals.