Wearable EMG Gesture Control With Priming Confirmation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing wearable devices suffer from inefficient man-machine interfaces due to false positives from inadvertent neuromuscular gestures and require physical interaction, wasting computing and power resources.
Innovation Solution
Implementing wearable devices with EMG, IMU, and time-of-flight sensors to detect multi-stage in-air gestures, comprising a priming gesture for triggering and a control gesture for confirmation, reducing false positives to less than 4%.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If single-stage gestures are used for controlling wearable devices, then ease of operation is improved, but false positive rate increases
Solution Approach 1:
The gesture control system is divided into multiple stages: a priming stage that detects initial gesture intent and a control stage that requires additional confirmation gestures. This segmentation allows the system to differentiate between intentional and inadvertent gestures, reducing false positives while maintaining ease of operation through intuitive multi-step interactions.
2Reliability
If physical interaction with device is required, then reliability of activation is improved, but ease of operation deteriorates
Solution Approach 1:
The system replaces mechanical contact-based interaction with neuromuscular signal detection. EMG sensors detect electrical signals from muscle contractions in the user's arm and hand, enabling hands-free gesture control while maintaining reliable activation through deliberate muscular actions that distinguish intentional gestures from inadvertent movements.
3Speed
If continuous gesture monitoring is implemented, then responsiveness is improved, but energy consumption increases
Solution Approach 1:
The system implements periodic sampling of neuromuscular signals rather than continuous monitoring. The EMG sensors take periodic measurements of muscle activity, allowing the system to detect gestures with good responsiveness while significantly reducing power consumption compared to continuous high-frequency sampling.
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 efficient, intuitive, and low-resource interaction with electronic devices through multi-stage gestures, allowing hands-free operations without physical contact and minimizing accidental activations.
Implementation Method 1
receiving, via one or more sensors of a wrist-wearable device worn by a user, data generated from performance of a multi-stage in-air hand gesture by the user
Implementation Method 2
Implementing wearable devices with EMG, IMU, and time-of-flight sensors to detect multi-stage in-air gestures
Implementation Method 3
Implementing wearable devices with EMG, IMU, and time-of-flight sensors to detect multi-stage in-air gestures
Data Source
AI summary
The various implementations described herein include methods and systems for using a multi-stage in-air hand gesture to activate user-interface interactions in a way that ensures low-false positive rates. In one aspect, a method includes, while a gating gesture is maintained, receiving a first indication of performance of an adjustment gesture of a first magnitude directed to a user interface object associated with a plurality of values. The method further includes, in response to receiving the first indication, adjusting the user interface object to have a first state after moving through some of the plurality of values based on the first magnitude. The method also includes, after receiving an indication of a release of the gating gesture, in response to receiving a second indication of performance of the adjustment gesture, forgoing adjusting the user interface object such that the user interface object continues to have the first state.