Multi-Modality User Interface with Gaze and Gesture Recognition
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Solution Overview
Problem
Current human sensing technologies for consumer electronics are limited in providing an intuitive user interface that effectively combines multiple modalities, such as visual, auditory, and gestural inputs, to control complex scenes like multimedia content, lacking user customization and efficient data processing.
Innovation Solution
An interfacing device and method that includes a parameter obtainer for scene parameters, a multi-modality recognizer to process various inputs like gaze, hand gestures, and speech, and a scene control information generator, which interprets these inputs based on user customization parameters to generate control commands for multimedia scenes, utilizing a system-on-chip or processor for algorithm execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple modalities (visual, auditory, gestural inputs) are combined to control complex scenes, then the intuitiveness and control capability of the user interface is improved, but the device complexity and data processing requirements increase
Solution Approach 1:
The system is divided into distinct functional modules: a parameter obtainer for scene parameters, a multi-modality recognizer for processing different input types, and a scene control information generator for creating control commands. This segmentation allows each module to handle specific tasks independently, managing complexity through modular architecture while supporting multiple modalities.
Solution Approach 2:
The scene control information generator acts as an intermediary that receives data from the multi-modality recognizer and translates it into control commands for the scene. This mediator component simplifies the overall system by providing a standardized interface between input recognition and scene control, reducing the complexity of direct multi-modality processing.
2Ease of operation
If user customization parameters are implemented to map control aspects to preferred modalities, then the ease of operation and user preference matching is improved, but the device complexity and processing overhead increase
Solution Approach 1:
User customization parameters including mapping information are obtained and stored in advance for each user. The system pre-configures the mapping between control aspects of scenes and preferred modalities before actual interaction occurs. This preliminary setup reduces processing complexity during operation, as the system simply applies pre-determined mappings rather than making complex decisions in real-time.
Solution Approach 2:
The system changes parameters by applying user-specific customization parameters to the scene control process. Different users have different mapping information that alters how their inputs are interpreted and mapped to scene controls. This parameter-based approach allows flexible user customization without requiring complex structural changes to the system architecture.
3Measurement precision
If scene parameters and user customization parameters are processed together to generate control commands, then the measurement precision and control accuracy are improved, but the loss of time for data processing increases
Solution Approach 1:
The scene control information generator processes only the necessary subset of parameters required for generating control commands, rather than analyzing all possible input data. By focusing on essential parameters and using pre-obtained user customization information, the system achieves accurate control without unnecessary processing overhead, balancing precision with efficiency.
Data Source
AI summary
An interfacing device for providing a user interface (UI) exploiting a multi-modality may recognize at least two modality inputs for controlling a scene, and generate scene control information based on the at least two modality inputs.