Radar Gesture Recognition Feedback for Robust Micro-Gesture Classification
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Solution Overview
Problem
Radar-based gesture recognition systems face challenges in distinguishing micro-gestures due to user dependency, confusion between similar gestures, differentiation from undesired motions, and temporal correlation in wrong predictions, particularly in differentiating single-finger and multiple-finger movements and handling non-gestures.
Innovation Solution
Utilizing contextual information from user feedback, radar data, and usage patterns to refine the gesture classifier, optimize parameter choices, and balance complexity and performance, enhancing the robustness of the gesture recognition system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If radar-based gesture recognition is used, then power efficiency is improved and privacy concerns are reduced, but accuracy in distinguishing micro-gestures deteriorates
Solution Approach 1:
The system implements a feedback mechanism where users can correct misclassified gestures, and this feedback is used to retrain the classifier. The processor determines whether to obtain feedback for target gestures, and when feedback is provided, it uses this information to update the gesture classifier model, thereby improving accuracy over time while maintaining radar's inherent power efficiency
Solution Approach 2:
The system dynamically adjusts classification parameters by validating gesture samples against a dataset of pre-existing validated samples and using distance thresholds to determine validity. The classifier model is updated based on validated feedback, changing its internal parameters to better distinguish between similar micro-gestures while maintaining the power-efficient radar-based approach
2Measurement precision
If gesture classifier model is updated with more training data, then recognition accuracy is improved, but system complexity increases
Solution Approach 1:
The system performs preliminary validation of gesture samples by comparing them against a pre-existing dataset of validated samples before incorporating them into training. This preliminary action filters out low-quality data, ensuring that only validated gesture samples are used for model updates, thereby improving accuracy without proportionally increasing the complexity of processing large datasets
Solution Approach 2:
The system automatically determines whether to update the model based on the validity of gesture samples and the distance threshold comparison. The processor autonomously manages the training process by selecting which samples to use for updating the gesture classifier, reducing the need for manual intervention and simplifying the overall system operation while maintaining high accuracy
3Reliability
If feedback collection is implemented, then gesture recognition reliability is improved, but processing time increases
Solution Approach 1:
The system implements partial feedback collection by determining whether to obtain feedback for target gestures based on predefined criteria. Not all gestures require feedback collection, only those that meet specific conditions, thereby improving reliability for critical gestures while avoiding the time cost of collecting feedback for all gestures uniformly
Solution Approach 2:
The system uses distance thresholds and validation parameters to efficiently determine whether gesture samples are valid before incorporating feedback. By changing these parameters dynamically, the system balances the trade-off between collecting sufficient feedback for high reliability and minimizing processing time through efficient filtering of gestures that require feedback
Data Source
AI summary
A method includes obtaining a gesture sample via a gesture classifier. The gesture sample includes parameters of features extracted from the radar signals based on user motion in performance of a gesture. The method includes determining whether to obtain feedback for a target gesture associated with the gesture sample. The method includes in response to a determination to obtain the feedback, obtaining the feedback for the target gesture. The feedback includes a label indicating a type of gesture. The method includes determining whether the gesture sample associated with the label is valid based on comparison to a dataset of pre-existing validated gesture samples and a distance threshold. The method includes in response to determining that the gesture sample associated with the label is valid, determining whether to update a model for the gesture classifier using the validated gesture sample as a training sample.