Smartphone Gesture Control for Real-Time 3D Device Selection
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Solution Overview
Problem
Existing home automation systems rely on cumbersome markers or sensors, are expensive to construct, and struggle with real-time motion capture due to data analysis complexities, limiting their deployment and user experience.
Innovation Solution
A smartphone equipped with a motion sensory control device detects gestures in a 3D space, allowing selection and control of devices without markers or sensors, and automatically adjusts power modes to conserve energy while maintaining real-time motion tracking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If conventional motion capture approaches using markers or sensors are implemented, then motion detection capability is improved, but device complexity and cost increase
Solution Approach 1:
The patent extracts the motion detection functionality from complex dedicated motion capture systems and relocates it to mobile computing devices. The mobile device's existing camera and processor are utilized to perform gesture recognition, eliminating the need for specialized markers, sensors, or bulky capture equipment. This extraction principle resolves the contradiction by maintaining motion detection capability while dramatically reducing system complexity.
Solution Approach 2:
The patent applies universality by using a mobile device that serves multiple functions: it acts as both the computational platform for gesture recognition and the imaging device for capturing motion. This multi-functional approach eliminates the need for separate dedicated motion capture equipment, thereby improving motion detection capability without increasing device complexity.
2Measurement precision
If multiple cameras are deployed for motion capture, then measurement precision is improved, but productivity decreases due to data analysis complexity
Solution Approach 1:
The patent extracts the image processing and gesture recognition algorithms from complex multi-camera systems and implements them on mobile device processors. This extraction enables real-time processing of video data from a single camera, achieving both measurement precision and real-time productivity that would be difficult to attain with traditional multi-camera systems.
Solution Approach 2:
The system processes video frames at regular intervals (e.g., 30 or 60 frames per second), using periodic action to maintain real-time processing capability. By analyzing frames at consistent intervals and using efficient algorithms, the system achieves both precision in motion detection and high productivity in real-time processing.
3Measurement precision
If markers or sensors are worn by occupants, then motion detection accuracy is improved, but ease of operation deteriorates due to cumbersome equipment
Solution Approach 1:
The patent extracts the detection function from wearable markers and sensors and relocates it to a mobile device carried by the user. This eliminates the need to attach cumbersome equipment to the body, significantly improving ease of operation and user comfort while maintaining motion detection accuracy through software-based gesture recognition.
Solution Approach 2:
Instead of physically attaching markers to the body, the system creates a digital copy or model of the user's gestures by analyzing video imagery. This virtual representation allows for accurate motion detection without the physical burden of wearable sensors, resolving the contradiction between accuracy and ease of operation.
4Difficulty of detecting and measuring
If specialized smart home environments are constructed, then motion capture capability is improved, but cost increases
Solution Approach 1:
The patent applies universality by using a mobile device that already exists in most households for other purposes (communication, photography, etc.). This device is repurposed to provide motion capture capability, eliminating the need for expensive specialized smart home infrastructure while maintaining full motion detection functionality.
Solution Approach 2:
The mobile device serves itself by using its own built-in camera and processor to perform gesture recognition. This self-service approach eliminates the need for external specialized equipment or infrastructure, dramatically reducing deployment costs while maintaining motion capture capability.
Data Source
AI summary
The technology disclosed relates to selecting among devices to interact with. It also relates operating a smart phone with reduced power consumption. It further relates to gesturally interacting with devices that lack gestural responsiveness. The technology disclosed also relates to distinguishing control gestures from proximate non-control gestures in a pervasive three dimensional (3D) sensory space. The technology disclosed further relates to selecting among virtual interaction modalities to interact with.