Learning Controller for Gesture-Based Home Appliance Automation
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Solution Overview
Problem
Existing home automation systems require users to carry devices like smartphones to control appliances, and often necessitate programming or button arrangement, which is cumbersome and limits the form of user interface.
Innovation Solution
A robotic system with a learning controller that receives wireless transmissions, determines context, and associates control instructions with sensory data, allowing for automatic execution of actions without continuous user input through a touch-activated interface, camera-based gesture recognition, or voice commands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users carry smartphones and perform programming or button arrangement to control home appliances, then control functionality is achieved, but user convenience deteriorates and interface flexibility is limited
Solution Approach 1:
The system performs automatic appliance identification and control signal generation without requiring user programming. The appliance transmits its own identification information, and the controller automatically matches it with stored control signals to execute operations, making the system self-servicing rather than requiring manual configuration by users
Solution Approach 2:
Control signals for various appliances are pre-stored in the controller's memory during system setup. When an appliance is activated, the controller retrieves the corresponding pre-stored control signal based on the appliance's identification information, eliminating the need for real-time programming or button arrangement by users
2Productivity
If automated control is implemented without user intervention, then productivity improves, but adaptability to different user contexts deteriorates
Solution Approach 1:
The system incorporates sensors that detect user presence, gestures, and environmental context, providing feedback to the controller. The controller processes this feedback information to determine whether automatic control should be activated, allowing the system to adapt its automation level based on real-time contextual conditions
Solution Approach 2:
The control system dynamically adjusts its operation mode between automatic and manual based on detected context. When user presence or specific gestures are detected, the system can switch from fully automatic mode to a mode that requires user confirmation or interaction, providing dynamic adaptability to different usage scenarios
Data Source
AI summary
Computerized appliances may be operated by users remotely. 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.