Radar Gesture Interpretation Using Contextual Recognition Cues
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Solution Overview
Problem
Radar-enabled computing devices struggle to accurately interpret ambiguous gestures due to variations in spatial and temporal features caused by user haste or tiredness, leading to difficulties in determining the intended gesture.
Innovation Solution
The use of contextual information, including operation status, user history, and location, to improve the interpretation of ambiguous gestures by correlating radar-signal characteristics with stored data and machine-learned models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If radar-based gesture detection is used to enable touchless control, then convenience and hygiene are improved, but gesture recognition accuracy deteriorates when users perform gestures in ambiguous states (hasty, tired, or incorrect performance)
Solution Approach 1:
The patent transitions from analyzing only spatial features (hand shape, position) to incorporating temporal dimensions (gesture duration, speed variations, acceleration patterns). This multi-dimensional analysis allows the system to distinguish between intentional gestures and ambiguous movements by examining the time-based characteristics of the gesture performance.
Solution Approach 2:
The system dynamically adjusts recognition parameters based on contextual information such as user history, time of day, and environmental factors. When ambiguous gestures are detected, the system modifies threshold parameters and weighting factors to better accommodate variations in user performance states, thereby maintaining accuracy across different user conditions.
2Measurement precision
If strict gesture recognition thresholds are applied to ensure accuracy, then gesture interpretation precision is improved, but system adaptability to natural user variations deteriorates
Solution Approach 1:
The system performs preliminary analysis of gesture characteristics before final recognition, using machine learning models to predict the intended gesture based on partial observation. This preliminary action allows the system to adjust recognition thresholds dynamically, maintaining precision while accommodating natural user variations in gesture performance.
Solution Approach 2:
The system incorporates feedback loops where recognition results are continuously refined based on user corrections and contextual information. When the system misinterprets a gesture, it learns from the correction and adjusts future recognition parameters, thereby improving both precision and adaptability over time through iterative feedback.
3Measurement precision
If contextual information processing is added to resolve ambiguous gestures, then gesture recognition accuracy is improved, but computational complexity and processing time increase
Solution Approach 1:
The contextual information processing is divided into multiple independent modules: user history analysis, environmental context processing, gesture pattern recognition, and machine learning inference. Each module handles specific aspects of context independently, reducing overall computational complexity while maintaining comprehensive analysis for improved recognition accuracy.
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
Enhances the accuracy of gesture recognition by distinguishing registered users and tailoring user experiences, reducing the need for precise repetition and improving convenience.
Implementation Method 1
An apparatus is also described that includes a radar system capable of transmitting and receiving radar signals
Data Source
AI summary
Techniques and devices for ambiguous gesture determination using contextual information are described in this document for radar-enabled computing devices. Contextual information may include a status of operations that are performed by the radar-enabled computing device or an associated device at a current time, past time, or future time. Contextual information may also or instead include foreground and background operations, a history of operations saved to a memory, scheduled or anticipated operations, a location of a user or device, room-related context, user habits, and so forth. Two or more computing devices may coordinate this contextual information across a communication network to form a computing system.