Low-Power Neuromuscular Gesture Detection with High-Power Confirmation
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Solution Overview
Problem
Existing wearable devices face significant power consumption challenges when continuously operating circuitry for gesture detection and recognition, leading to reduced battery life, especially when using machine-learning models to process neuromuscular signals for in-air hand gestures.
Innovation Solution
Implementing a system with low-power and high-power detectors that cycle between active and inactive states, using a low-power detector for initial gesture detection and a high-power detector for confirmation, with buffered data allowing the high-power detector to evaluate gestures without user intervention, thereby reducing power consumption and extending battery life.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-power detectors are continuously operated for gesture detection and recognition, then gesture recognition accuracy is improved, but power consumption increases
Solution Approach 1:
The system divides gesture detection into two segments: initial detection by low-power detectors and confirmation by high-power detectors. This segmentation allows the high-power detectors to operate intermittently rather than continuously, reducing overall power consumption while maintaining recognition accuracy when needed.
Solution Approach 2:
Low-power detectors perform preliminary screening of gestures before activating high-power detectors. This preliminary action filters out non-gesture inputs and only triggers high-power processing when a potential gesture is detected, ensuring accurate recognition while minimizing energy usage.
2Use of energy by moving object
If low-power detectors are used for initial gesture detection, then power consumption is reduced, but gesture detection accuracy may be compromised
Solution Approach 1:
Low-power detectors serve as intermediaries between the physical gesture and the high-power confirmation system. They perform initial screening and trigger high-power detectors only when necessary, acting as a bridge that reduces power consumption while preserving detection accuracy through subsequent confirmation.
Solution Approach 2:
The system replaces continuous mechanical operation of high-power detectors with an event-driven approach. Low-power detectors substitute for continuous high-power operation by detecting potential gestures and triggering high-power detectors only when needed, effectively substituting a less energy-intensive mechanism for the more intensive one.
3Use of energy by moving object
If high-power detectors are activated only after low-power detector identification, then power consumption is reduced, but response time increases
Solution Approach 1:
The low-power detectors continuously monitor and perform preliminary identification of potential gestures in advance. When a potential gesture is detected, the system immediately activates high-power detectors for confirmation, eliminating delays and ensuring rapid response while maintaining power efficiency.
Solution Approach 2:
Low-power detectors maintain continuous monitoring capability to detect potential gestures at any moment. This continuous useful action ensures that when a gesture occurs, the system is already prepared to activate high-power detectors immediately, maintaining rapid response times while keeping power consumption low during idle periods.
Data Source
AI summary
The various implementations described herein include methods and systems for power-efficient processing of neuromuscular signals. In one aspect, a method includes: (i) obtaining a first set of neuromuscular signals; (ii) after determining, using a low-power detector, that the first set of neuromuscular signals require further processing to confirm that a predetermined in-air hand gesture has been performed: (a) processing the first set of neuromuscular signals using a high-power detector; and (b) in accordance with a determination that the processing indicates that the predetermined in-air hand gesture did occur, registering an occurrence of the predetermined in-air hand gesture; (iii) receiving a second set of neuromuscular signals; and (iv) after determining, using the low-power detector and not using the high-power detector, that a different predetermined in-air hand gesture was performed, performing an action in response to the different predetermined in-air hand gesture.