Hierarchical Gesture Capture Using Sensor Composition Filtering
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Solution Overview
Problem
Existing gesture capture technologies for wearable devices are constrained by limited sensor availability, energy, and computational power, requiring higher processing power, memory-intensive techniques, and often relying on cumbersome and expensive hardware, which limits the recognition of gestures and is not adaptable to specific users without engineering intervention.
Innovation Solution
A hierarchical gesture capture method using a wrist-worn device with a sensor array, accumulator, filter, and classifier, employing lightweight clustering algorithms to identify poses and gestures efficiently, reducing false positives and allowing for user-defined gestures without extensive engineering resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If advanced gesture recognition techniques are used, then gesture recognition accuracy is improved, but computational power requirements and memory usage increase
Solution Approach 1:
The gesture recognition process is divided into distinct stages: data collection by sensor array, composition creation by accumulator, filtering by filter component, and classification by classifier. This segmentation allows each component to perform specialized functions with optimized computational requirements, improving overall efficiency while maintaining accuracy.
Solution Approach 2:
The system performs preliminary filtering and pose detection before final gesture classification. The filter component processes sensor compositions to identify poses, and only then does the classifier perform detailed gesture recognition. This preliminary action reduces the computational burden on the final classification stage by pre-processing and eliminating obvious non-gesture movements.
2Adaptability or versatility
If memory-intensive techniques are used, then gesture recognition capability is improved, but device memory requirements increase
Solution Approach 1:
The system extracts only the essential features from sensor data through the accumulator and filter components before passing them to the classifier. By taking out and processing only relevant pose and motion information rather than storing all raw sensor data, the system achieves versatile gesture recognition with reduced memory requirements.
3Measurement precision
If sophisticated hardware is used, then gesture capture accuracy is improved, but device complexity and cost increase
Solution Approach 1:
The sensor array is designed to perform multiple functions: collecting data for both pose detection and gesture recognition, providing both first and second sensor compositions. This multi-functionality allows accurate gesture capture using a single versatile sensor array rather than requiring separate specialized sensors, thereby reducing hardware complexity while maintaining accuracy.
Data Source
AI summary
System and techniques for gesture capture are described herein. A first representation of a sensor array may be collected into a first sensor composition. The first sensor composition may be filtered to output a pose status. Here, the pose status corresponds to a pose in a library of poses. A second representation of the sensor array may be collected into a second sensor composition. A fit of the second sensor composition to a model may be measured. The fit may be provided to a gesture consumer based on the fit and the pose.