Generative Model-Driven Sensor Sampling Under XR Battery Constraints
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Solution Overview
Problem
The utility and availability of sensor information in XR devices like AR glasses are limited by battery life, as the power required to operate various sensors for always-on assistance is significant, while battery space is constrained.
Innovation Solution
A framework for selecting sensor sampling modes based on a level of certainty determined using a generative model, allowing for adaptive sensing of user states and environments, switching between high- and low-power modes to balance accuracy and power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensors operate continuously at high sampling rates to maintain accurate user state measurement, then measurement precision is improved, but power consumption increases
Solution Approach 1:
The system dynamically adjusts sensor sampling rates based on user activity states. During active periods, sensors operate at high sampling rates to capture detailed user state information. During inactive periods, sampling rates are reduced to minimize power consumption. This dynamic adaptation resolves the contradiction by making measurement precision variable rather than constant.
Solution Approach 2:
The system changes the operational parameters of sensors based on detected user states. When the generative model predicts the user is engaged or transitioning between states, sampling parameters are increased. When the user is in a stable inactive state, sampling parameters are decreased. This parameter adjustment strategy maintains measurement accuracy when needed while reducing power consumption during stable periods.
2Adaptability or versatility
If multiple sensors operate simultaneously to provide comprehensive environmental awareness, then adaptability is improved, but power consumption increases
Solution Approach 1:
The system segments the sensor array into functionally groups (e.g., audio sensors, visual sensors, inertial sensors) and activates only the segments necessary for the current user state. The generative model determines which sensor modality is most relevant for predicting the current user intent, and only those sensors are actively sampled. This segmentation allows comprehensive environmental awareness capability while reducing simultaneous sensor operation.
Solution Approach 2:
The system uses a unified generative model that can process inputs from multiple sensor types but only activates the specific sensor subset needed for the current prediction task. The same model framework handles different sensor modalities, allowing the system to maintain adaptability across different sensing configurations while minimizing active sensor count based on current needs.
3Measurement precision
If sensor sampling frequency is increased to capture rapid user state transitions, then measurement precision is improved, but loss of energy increases
Solution Approach 1:
The system implements periodic sampling with variable intervals based on detected user state stability. During periods of user inactivity or stable state, sampling intervals are extended to reduce power loss. When the generative model detects indicators of potential state transition (such as increased motion or audio activity), the sampling frequency is increased to capture the transition accurately. This periodic adaptation resolves the contradiction between transition detection accuracy and energy loss.
Data Source
AI summary
The disclosed computer-implemented method may include (1) predicting a user state, wherein the user state is measurable via a plurality of different sensor sampling modes, (2) determining a level of uncertainty associated with the predicted user state, and (3) selecting, from the plurality of different sensor sampling modes, a sampling mode to measure the user state. Selecting the sampling mode may include selecting a first sampling mode in response to determining that the level of uncertainty is above a threshold or selecting a second sampling mode in response to determining that the level of uncertainty is below the threshold. Various other methods, systems, and computer-readable media are also disclosed.