Learning Controller for Gesture-Based Home Automation
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Solution Overview
Problem
Existing home automation devices often require cumbersome user interfaces, such as carrying smartphones to control appliances, and may need programming or button assignment, limiting convenience and ease of use.
Innovation Solution
A robotic system with a learning controller that associates sensory contexts with user indications, allowing for automatic transmission of control commands to devices based on detected contexts, such as gestures or environmental changes, without the need for continuous user input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If home automation devices use traditional control interfaces (smartphones, remote controls), then users can control appliances, but the operation becomes cumbersome and requires carrying devices or programming
Solution Approach 1:
The system enables appliances to automatically detect user presence and context through sensors (camera, microphone, weight sensors) and execute actions without requiring users to manually operate remote controls or smartphones. The appliance serves itself by interpreting sensory data and autonomously performing tasks like turning on lights or playing music based on detected user behavior and environment.
Solution Approach 2:
The patent replaces traditional mechanical control interfaces (physical buttons, remote controls, smartphone apps) with a sensor-based automated control system. Instead of users physically interacting with control devices, the system uses cameras, microphones, and weight sensors to detect user presence and context, then automatically translates these detections into appropriate appliance actions, eliminating the need for manual interface interaction.
2Adaptability or versatility
If home automation devices require programming or button assignment, then devices can perform specific functions, but the setup process becomes time-consuming and complex
Solution Approach 1:
The system performs preliminary learning of user preferences and environmental contexts during initial setup by observing user behavior patterns through sensors. The camera captures user actions, weight sensors detect presence, and microphones record commands, allowing the system to pre-learn associations between contexts and desired actions. This preliminary observation phase enables the appliance to automatically adapt to user needs without requiring time-consuming manual programming or button assignment during actual use.
Solution Approach 2:
The system continuously monitors user interactions with the appliance and environment through multiple sensors, collecting feedback on user preferences and behavioral patterns. This feedback is processed to automatically adjust and refine the control logic, enabling the appliance to adapt its behavior over time. The feedback loop allows the system to learn from actual usage patterns rather than relying on pre-configured settings, reducing initial setup time while maintaining high adaptability.
Data Source
AI summary
Computerized appliances may be operated by users remotely. In one exemplary implementation, a learning controller apparatus may be operated to determine association between a user indication and an action by the appliance. The user indications, e.g., gestures, posture changes, audio signals may trigger an event associated with the controller. The event may be linked to a plurality of instructions configured to communicate a command to the appliance. The learning apparatus may receive sensory input conveying information about robot's state and environment (context). The sensory input may be used to determine the user indications. During operation, upon determine the indication using sensory input, the controller may cause execution of the respective instructions in order to trigger action by the appliance. Device animation methodology may enable users to operate computerized appliances using gestures, voice commands, posture changes, and/or other customized control elements.