Self-Learning Neuromorphic Gesture Models for Rare Gesture Recognition
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Solution Overview
Problem
Existing gesture recognition technologies struggle to accurately recognize under-represented, complex, or diverse gestures without requiring custom models for each new gesture, especially in changing environments and user populations with different preferences.
Innovation Solution
A self-learning gesture recognition model deployed on a neuromorphic processor that continuously updates its parameters using learning rules, allowing it to adapt to new gestures and environments in real-time, leveraging spiking neural networks and event-based data processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a custom gesture model is developed for each new gesture, then gesture recognition accuracy for that specific gesture is improved, but device complexity and development time increase significantly
Solution Approach 1:
The gesture recognition system performs self-learning by automatically updating its model parameters using continuously collected gesture data from users. The system monitors its own performance and autonomously adjusts its internal representations without requiring external intervention or custom model development for each new gesture, thereby maintaining high accuracy while reducing complexity
Solution Approach 2:
The system dynamically updates model parameters through continuous learning processes. Instead of creating entirely new models for each gesture type, the system modifies existing parameter values based on accumulated data, allowing the same model structure to adapt to various gestures and user preferences over time
2Loss of time
If a gesture recognition model is trained on limited labeled data, then training time and computational resources are reduced, but recognition accuracy for under-represented gestures deteriorates
Solution Approach 1:
The system engages in continuous learning by continuously collecting and processing gesture data in the background. This ongoing data accumulation allows the model to learn from under-represented gestures over extended periods without requiring intensive batch training, thereby improving accuracy for rare gestures while maintaining efficient training operations
Solution Approach 2:
The system incorporates feedback mechanisms where recognized gestures are fed back into the learning process. By monitoring recognition performance and using this feedback to guide parameter updates, the system prioritizes learning from under-represented gestures, improving their accuracy without requiring additional training time
3Measurement precision
If the gesture recognition system uses complex processing algorithms, then recognition accuracy for complex movements is improved, but computational energy consumption increases
Solution Approach 1:
The system dynamically adjusts processing complexity based on the specific gesture being recognized and the current model state. Instead of applying uniformly complex algorithms to all gestures, the system adapts its computational approach, using simpler processing for well-represented gestures and more sophisticated analysis only when necessary for complex or ambiguous movements, thereby reducing overall energy consumption
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for updating a trained gesture recognition model deployed on a neuromorphic processor that has been trained to process data that characterizes the new gesture and to determine a gesture classification for the gesture are described. A method includes receiving data that characterizes a new gesture and processing the data to generate a new embedding in a latent space. For each of multiple clusters of reference embeddings in the latent space, a respective distance in the latent space between the cluster of reference embedding and the new embedding is determined. A determination is made, based on applying one or more learning rules to the distances, one or more procedures to update the gesture recognition model. A determination is made, in accordance with the determined procedure(s), an update to values of one or more parameters of the gesture recognition model.