Hand-Worn Gesture Device Power Management
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing gesture input technologies for portable computing devices face challenges such as high power consumption and the need for fixed camera setups, making them unsuitable for ultra-portable form factors like wearable devices, and existing solutions like touch screens are expensive and occluded during use.
Innovation Solution
A hand-worn device equipped with a microphone and accelerometer that switches between low-power sleep mode and user interaction mode based on detected gestures, using audio and motion signals to decode inputs efficiently, allowing for energy-efficient 3D gesture recognition without the need for continuous power or fixed camera setups.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If depth cameras are used for 3-D gesture tracking, then gesture recognition capability is improved, but power consumption increases
Solution Approach 1:
The patent combines multiple low-power sensors (accelerometer, gyroscope, microphone) into an integrated gesture recognition system. By merging data from these sensors and processing it through machine learning algorithms, the system achieves 3-D gesture tracking capability without requiring high-power depth cameras, thus resolving the contradiction between gesture recognition capability and power consumption.
Solution Approach 2:
The patent replaces the mechanical/optical depth camera system with an electro-acoustic sensor system (accelerometers, gyroscopes, microphones). This substitution uses electrical and acoustic fields instead of optical depth mapping, dramatically reducing power consumption while maintaining gesture recognition functionality.
2Adaptability or versatility
If depth cameras are used for 3-D gesture tracking, then gesture recognition capability is improved, but device portability deteriorates
Solution Approach 1:
The patent integrates multiple sensors into a compact wearable form factor. By combining accelerometers, gyroscopes, and microphones in a single device unit, the system enables portable 3-D gesture tracking without requiring fixed camera setups, thus improving both gesture recognition capability and device portability.
Solution Approach 2:
The patent transitions from fixed camera-based gesture tracking to dynamic wearable sensor-based tracking. The sensors are designed to move with the user's body, capturing gesture data in real-time as the device moves, thereby enabling portability while maintaining gesture recognition accuracy.
3Ease of operation
If touch screens are used for input, then input capability is improved, but device cost increases
Solution Approach 1:
The patent uses inexpensive sensors (accelerometers, gyroscopes, microphones) that are already widely available in consumer electronics, replacing expensive touch screen components. These sensors provide alternative input capabilities through gesture recognition, reducing device manufacturing cost while maintaining input functionality.
Solution Approach 2:
The patent replaces the mechanical touch screen interface with sensor-based gesture detection. By using accelerometers and gyroscopes to detect hand movements and microphones to capture tapping sounds, the system provides input capability without requiring expensive touch screen hardware.
4Speed
If sensors remain active continuously for gesture detection, then gesture recognition responsiveness is improved, but power consumption increases
Solution Approach 1:
The patent implements periodic sampling of sensor data at optimized intervals. Instead of continuous monitoring, the system samples accelerometer, gyroscope, and microphone data at specific time intervals, reducing power consumption while maintaining responsive gesture recognition through efficient data processing and pattern recognition algorithms.
Solution Approach 2:
The patent uses preliminary motion detection through accelerometers to trigger more detailed gesture analysis. The system first detects basic motion patterns, and only when motion exceeds certain thresholds does it activate full gesture recognition processing, thereby reducing overall power consumption while maintaining responsiveness to actual gestures.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables energy-efficient gesture recognition with low power consumption, allowing the device to be constantly available and versatile for various surfaces and environments, while reducing the risk of accidental activation and improving usability in portable devices.
Implementation Method 1
an accelerometer configured to capture a motion input and generate an accelerometer signal
Implementation Method 2
a microphone configured to capture an audio input and generate an audio signal
Data Source
Figure 1
Figure 2
Figure 3
AI summary
Embodiments that relate to energy efficient gesture input on a surface are disclosed. One disclosed embodiment provides a hand-worn device that may include a microphone configured to capture an audio input and generate an audio signal, an accelerometer configured to capture a motion input and generate an accelerometer signal, and a controller comprising a processor and memory. The controller may be configured to detect a wake-up motion input based on the accelerometer signal. The controller may wake from a low-power sleep mode in which the accelerometer is turned on and the microphone is turned off and enter a user interaction interpretation mode in which the microphone is turned on. Then, the controller may contemporaneously receive the audio signal and the accelerometer signal and decode strokes. Finally, the controller may detect a period of inactivity based on the audio signal and return to the low-power sleep mode.