Radar Gesture Recognition for Long-Range Privacy-Preserving Control
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Solution Overview
Problem
Ambient computing devices face limitations in recognizing user gestures at long ranges, requiring specific locations, orientations, and wake-up triggers, which can be burdensome and distract users, and raise privacy concerns with sensors like microphones and cameras.
Innovation Solution
A radar-based gesture determination method that enables long-range gesture recognition up to eight meters without specific location or orientation requirements, using a machine-learned model and augmented data, incorporating negative data to improve accuracy, and maintaining user privacy by avoiding personally identifiable information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional sensors (microphones, cameras) are used for gesture recognition, then recognition capability is achieved, but user privacy is compromised and users must provide wake-up triggers
Solution Approach 1:
The patent replaces optical and acoustic sensors with radar technology. Radar uses electromagnetic waves to detect gestures through radio wave reflection, eliminating the need for cameras and microphones that capture personally identifiable information. This substitution maintains gesture recognition capability while preserving user privacy.
Solution Approach 2:
The patent extracts and removes the privacy-intrusive components (cameras and microphones) from the gesture recognition system, retaining only the radar component that detects gestures without capturing personally identifiable information. This extraction eliminates the harmful factor while preserving the essential function.
2Measurement precision
If gesture recognition requires specific location and orientation constraints, then recognition accuracy is improved, but user convenience deteriorates
Solution Approach 1:
The radar-based system provides universal gesture recognition capability that works regardless of user location or orientation relative to the device. The radar can detect gestures from multiple angles and distances, making the system adaptable to various user positions without requiring precise spatial constraints.
Solution Approach 2:
The system dynamically adjusts its detection parameters to accommodate gestures performed at different locations and orientations. Rather than requiring fixed spatial constraints, the radar system adapts to the dynamic nature of user gestures, maintaining accuracy across varying operational conditions.
3Ease of operation
If continuous gesture detection is implemented without wake-up triggers, then user convenience is improved, but system complexity increases
Solution Approach 1:
The radar system operates continuously to detect gestures without requiring wake-up triggers. The radar transmitter and receiver remain active, continuously monitoring for gesture signals, which eliminates the need for users to initiate interaction through specific trigger words or actions.
Solution Approach 2:
The system serves itself by automatically detecting and responding to gestures without requiring user initiation. The continuous radar detection and machine learning-based gesture identification system operates autonomously, eliminating the need for wake-up triggers and reducing the burden on users.
4Adaptability or versatility
If long-range gesture detection is achieved, then adaptability is improved, but measurement precision deteriorates
Solution Approach 1:
The system changes the operating parameters of the radar to optimize both range and precision. By adjusting radar frequency, pulse width, and processing algorithms, the system achieves accurate gesture recognition at long distances, resolving the typical trade-off between detection range and measurement precision.
Solution Approach 2:
The patent introduces machine learning models as an intermediary between raw radar signals and gesture recognition. The machine learning algorithms process and interpret the radar data, enhancing the system's ability to accurately identify gestures at long ranges by learning from trained data patterns.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables seamless and efficient gesture recognition at long ranges, enhancing user convenience and privacy by allowing continuous gesture detection without specific triggers, improving accuracy through machine-learned models and radar-signal augmentation.
Implementation Method 1
transmitting radar-transmit signals from a radar system associated with a computing device; receiving, at the radar system or another radar system associated with the computing device, radar-receive signals
Data Source
AI summary
Techniques and devices for radar-based gesture determination at long ranges are described in this document. The techniques described herein enable a computing device to detect and recognize gestures at long-range extents of up to eight meters. The computing device of this disclosure does not require the user to perform a gestural command at a specific location, in a specific orientation, contingent upon a wake-up trigger, or at a specific time, enabling the user to freely provide commands whenever and wherever is most convenient. This continual recognition of gestures may be enabled by a machine-learned model, generation of augmented data, and inclusion of negative data.