Hand-Tracking Pipeline Dimming for Low-Power Gesture Detection
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Solution Overview
Problem
AR systems face challenges in conserving power and extending usage time due to always being in full operation when not interacting with the user, and they lack effective input modalities beyond hardware buttons or touchpads.
Innovation Solution
A hand-tracking input pipeline that can be dimmed to conserve power by deactivating most components and entering a limited operational mode, activating only upon gesture initiation detection using a binary gesture classifier.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the hand-tracking input pipeline operates in full mode continuously, then gesture recognition accuracy is maintained, but power consumption increases
Solution Approach 1:
The hand-tracking input pipeline dynamically adjusts its operational mode based on detected gesture initiation. The system transitions from a limited operational mode (lower power consumption) to a fully operational mode (higher accuracy) when gesture initiation is detected, and returns to limited mode when no gesture is detected. This dynamic adaptation resolves the contradiction by matching system performance to actual usage needs.
Solution Approach 2:
The system implements periodic monitoring using a binary gesture classifier that continuously analyzes camera data at a reduced level. This periodic detection mechanism allows the system to maintain awareness of gesture initiation events while operating in a low-power state, triggering full operational mode only when necessary. The periodic action enables the system to balance power consumption with gesture recognition capability.
2Use of energy by moving object
If the hand-tracking input pipeline is deactivated to conserve power, then power consumption decreases, but gesture input functionality is lost
Solution Approach 1:
The system performs preliminary action by continuously monitoring for gesture initiation using a lightweight binary classifier even when the full hand-tracking pipeline is deactivated. This preliminary detection mechanism ensures that when a gesture is initiated, the system can quickly activate the full pipeline without significant delay, thereby maintaining gesture input functionality while conserving power during non-interactive periods.
Solution Approach 2:
The binary gesture classifier acts as an intermediary between the low-power state and the full hand-tracking pipeline. This intermediary component maintains minimal power consumption while detecting gesture initiation events, serving as a bridge that allows the system to transition smoothly between power-saving mode and full operational mode without completely losing gesture input capability.
3Ease of operation
If the system enters always-on mode for interactive responsiveness, then user interaction responsiveness is improved, but usage time decreases
Solution Approach 1:
The system applies partial action by maintaining only the essential gesture detection functionality (binary classifier) in an always-on state, while keeping the more resource-intensive full hand-tracking pipeline deactivated until needed. This partial operation enables the system to remain responsive to gesture initiation events without consuming the full power that would be required for continuous full-mode operation, thereby extending usage time while preserving interactive responsiveness.
Data Source
AI summary
A hand-tracking input pipeline dimming system for an AR system is provided. The AR system deactivates the hand-tracking input pipeline and places a camera component of the hand-tracking input pipeline in a limited operational mode. The AR system uses the camera component to detect initiation of a gesture by a user of the AR system and in response to detecting the initiation of the gesture, the AR system activates the hand-tracking input pipeline and places the camera component in a fully operational mode.