Radar Kinematic Data Augmentation for Gesture Classification Shift
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Solution Overview
Problem
The accuracy of machine-learning models for radar-based gesture classification is often poor due to variations in deployment scenarios, leading to incorrect estimations and covariate shift issues.
Innovation Solution
A method of populating a training dataset by applying transformations to time-resolved kinematic observables extracted from radar measurement data, using data augmentation techniques tailored to the deployment configuration, to enhance the accuracy of machine-learning models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If machine-learning models are trained on radar measurement data from limited measurement campaigns, then training time and resources are reduced, but the accuracy of gesture classification deteriorates due to covariate shift between training and deployment scenarios
Solution Approach 1:
The patent applies data augmentation transformations to training data before model training to pre-adapt the model to various deployment conditions. By transforming kinematic observables (position, velocity, acceleration) through simulations of different deployment configurations, the model is preliminarily exposed to covariate shifts that will occur in real deployment, thereby improving accuracy without requiring extensive measurement campaigns for each scenario
Solution Approach 2:
The patent transforms kinematic observables by applying parameter changes that simulate different deployment configurations. Specifically, it modifies position, velocity, and acceleration parameters through mathematical transformations that represent variations in radar sensor placement and orientation. This allows the model to learn invariant features across different deployment scenarios without requiring re-measurement campaigns
2Reliability
If extensive measurement campaigns are conducted to cover all deployment scenarios, then gesture classification accuracy is improved, but the cost and complexity of data collection increases significantly
Solution Approach 1:
The patent creates synthetic copies of training data by applying transformations to existing measurement data. Instead of conducting separate measurement campaigns for each deployment scenario, it generates virtual copies of the data with transformed kinematic observables that simulate different sensor positions, orientations, and environmental conditions. This copying approach maintains data diversity while eliminating the need for complex multi-scenario measurement campaigns
Solution Approach 2:
The patent develops a universal data augmentation framework that can handle multiple deployment scenarios using a single set of original measurements. The transformation functions are designed to be scenario-agnostic, allowing the same base dataset to be adapted to various radar configurations and environmental conditions through parameter transformations, thereby making the data collection process universally applicable across different deployment contexts
3Measurement precision
If manual annotation of radar measurement data is performed extensively to improve training data quality, then gesture classification accuracy is improved, but the resource consumption and time required for annotation increases
Solution Approach 1:
The patent implements self-service data augmentation where the system automatically generates transformed training samples without requiring manual annotation for each transformed version. The transformation pipeline automatically processes kinematic observables through various simulated deployment conditions, and the ground truth labels are automatically computed based on the transformation parameters, eliminating the need for expensive manual re-annotation of augmented data
4Productivity
If the training dataset is small and specific to one deployment configuration, then data processing time is reduced, but the model's adaptability to different deployment scenarios deteriorates
Solution Approach 1:
The patent creates a dynamic training dataset through data augmentation that adapts to different deployment scenarios. Instead of using a static, scenario-specific dataset, it dynamically generates transformed samples that simulate various deployment configurations by applying transformations to kinematic observables. This dynamic approach maintains dataset compactness while significantly improving adaptability to unseen deployment scenarios
Data Source
AI summary
In an example implementation, a method includes populating a training dataset for training a machine-learning model to provide estimations associated with at least one object by obtaining a predetermined input sample comprising one or more sets of time-resolved values for one or more observables of the at least one object, generating a further input sample based on the predetermined input sample by applying a transformation over a time interval of at least one of the one or more sets of the time-resolved values of the predetermined input sample, and adding the further input sample to the training dataset to provide an augmented training dataset.