Custom Gesture Recognition Using Multi-Chain Signals and ML Acceleration
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Solution Overview
Problem
Current input methods for user-machine interaction, such as touch screens and voice control, have limitations, particularly in recognizing gestures for complex applications like games, and do not allow for easy customization of gestures.
Innovation Solution
A gesture recognition system comprising a transmission unit, reception chains, a customized gesture collection engine, and a machine learning accelerator that generates feature map data and performs machine learning to recognize and customize gestures, enabling accurate and personalized gesture recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional gesture detection methods are used, then the system can perform basic gesture control, but the gesture recognition accuracy is poor and customization is not allowed
Solution Approach 1:
The system performs preliminary action by collecting gesture data from multiple users and pre-training machine learning models before deployment. The gesture collection engine gathers training data during a setup phase, and the machine learning accelerator pre-processes this data to create customized recognition models, enabling both high accuracy and customization without compromising real-time performance.
Solution Approach 2:
The system applies dynamics by making the gesture recognition model adaptable and changeable. The machine learning accelerator allows the system to dynamically update and customize gesture recognition models based on collected user data, transforming a static recognition system into one that can evolve and adapt to individual user preferences and needs.
2Measurement precision
If machine learning processing is performed in real-time, then gesture recognition accuracy improves, but power consumption and processing time increase
Solution Approach 1:
The system segments machine learning processing into two distinct phases: an offline training phase and an online inference phase. The gesture collection engine and machine learning accelerator handle heavy computational training work offline, while the deployed system performs only lightweight real-time inference, dramatically reducing power consumption during actual gesture recognition while maintaining high accuracy.
Solution Approach 2:
The machine learning accelerator serves as an intermediary component between data collection and gesture recognition. It pre-processes and optimizes gesture data during training, creating optimized models that can be efficiently executed with minimal power consumption during real-time operation, acting as a bridge that reduces the computational burden on the main system.
3Measurement precision
If multiple reception chains are used to collect gesture data, then recognition accuracy improves, but device complexity increases
Solution Approach 1:
The system merges multiple reception chains into a unified gesture collection engine that processes data from all chains simultaneously. The machine learning accelerator consolidates the processing of multiple data streams into a single optimized computational flow, maintaining the benefits of multi-chain data collection while reducing overall system complexity through integrated processing.
Data Source
AI summary
A gesture recognition system includes a transmission unit, a first reception chain, a second reception chain, a customized gesture collection engine and a machine learning accelerator. The transmission unit is used to transmit a transmission signal to detect a gesture. The first reception chain is used to receive a first signal and generate first feature map data corresponding to the first signal. The second reception chain is used to receive a second signal and generate second feature map data corresponding to the second signal. The first signal and the second signal are generated by the gesture reflecting the transmission signal. The customized gesture collection engine is used to generate gesture data according to at least the first feature map data and the second feature map data. The machine learning accelerator is used to perform machine learning with the gesture data.