Gesture Recognition Using Relative Coordinate System Boundaries
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Solution Overview
Problem
Conventional gestural systems have poor recognition rates for detecting gestures that resemble characters or words, as they fail to accurately identify start and stop portions of gestures within a relative coordinate system.
Innovation Solution
The use of multiple sensors, such as MEMS sensors, accelerometers, gyroscopes, and capacitive touch sensors, integrated into a single device or wearable form factor, to detect when a user enters or leaves a boundary space associated with a relative coordinate system, allowing for the reconstruction and recognition of gestures and micro-gestures, and enabling efficient power management to conserve energy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional gestural systems detect gestures resembling characters or words, then gesture detection capability is provided, but recognition rates are poor
Solution Approach 1:
The gesture detection process is segmented into distinct phases: detecting entry into boundary space (start portion), tracking movement within boundary space, and detecting exit from boundary space (stop portion). This segmentation allows precise identification of gesture boundaries and improves recognition accuracy by treating each phase with specific detection logic.
Solution Approach 2:
The system performs preliminary detection of boundary space entry before fully recognizing the gesture. By detecting when a user enters the boundary space associated with a relative coordinate system, the system prepares for gesture recognition in advance, establishing the start point and context before the actual gesture movement occurs.
2Measurement precision
If multiple sensors are integrated into a single device, then gesture detection capability is improved, but device complexity increases
Solution Approach 1:
Multiple sensors (MEMS sensors, accelerometers, gyroscopes, capacitive touch sensors) are merged into a single integrated device or wearable form factor. This consolidation improves gesture detection accuracy by combining data from multiple sensor types while managing complexity through unified processing architecture.
Solution Approach 2:
The integrated sensor device performs multiple functions: detecting boundary space entry/exit, tracking gesture movement, reconstructing gesture sequences, and enabling power management. This multi-functionality reduces the need for separate dedicated devices for each function, managing overall system complexity.
3Measurement precision
If sensors continuously monitor gesture data, then gesture recognition accuracy is maintained, but energy consumption increases
Solution Approach 1:
Sensors operate in periodic cycles rather than continuously. The system activates sensors to detect boundary space entry, then monitors for gesture completion, and deactivates when no gesture is detected. This periodic operation maintains recognition accuracy for active gestures while conserving energy during idle periods.
Solution Approach 2:
The system uses capacitive touch sensors and detector signals to automatically trigger and terminate gesture monitoring without continuous power consumption. When the detector signals that the user has left the boundary space, the system self-manages power states, switching to low-power mode automatically.
Data Source
AI summary
Described are apparatus and methods for reconstructing a gesture by aggregating various data from various sensors, including data for recognition of start and/or stop portions of the gesture using a detection of an intersection with a relative coordinate system boundary.