Sensor Fusion Circuitry for Context-Aware Wake-Up Detection
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Solution Overview
Problem
Conventional voice-activated systems lack sufficient context for accurate wake-up detection, leading to potential false activations and inefficient power consumption in battery-powered devices, as they primarily rely on single input modalities like voice recognition or motion sensing without comprehensive context evaluation.
Innovation Solution
A multi-dimensional wake-up system utilizing sensor fusion circuitry that combines data from various sensors, including microphones, cameras, and motion sensors, to generate a wake signal through feature extraction and inference decisions, minimizing power consumption while providing a more comprehensive context for device activation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If single input modality (voice recognition or motion sensing) is used for wake-up detection, then power consumption is reduced, but wake-up accuracy and context awareness deteriorate
Solution Approach 1:
The patent combines multiple sensor inputs (microphone, camera, motion sensors, environmental sensors) into a unified wake-up detection system. The sensor fusion circuitry aggregates data from these diverse sources to make comprehensive wake-up decisions, resolving the contradiction by achieving both power efficiency (through selective sensor activation) and high accuracy (through multi-modal context analysis).
Solution Approach 2:
The system segments wake-up detection into multiple independent sensor modules, each processing specific types of input (audio, visual, motion, environmental). This segmentation allows the system to activate only necessary sensors based on context, reducing overall power consumption while maintaining comprehensive detection capability through the coordinated work of segmented sensor units.
2Reliability
If multiple sensor inputs are combined for comprehensive context evaluation, then wake-up accuracy improves, but power consumption increases
Solution Approach 1:
The system dynamically adjusts sensor activation based on contextual needs. The sensor fusion circuitry evaluates incoming data streams and selectively activates additional sensors only when necessary for comprehensive context evaluation. This dynamic approach allows the system to maintain high wake-up accuracy while minimizing power consumption by keeping sensors in low-power states when full multi-modal detection is not required.
Solution Approach 2:
The system changes operational parameters of sensors based on detection needs. Sensors can switch between active monitoring mode and low-power standby mode, with their sampling rates and activation thresholds adjusted dynamically. This parameter changing strategy enables the system to achieve comprehensive context awareness during critical moments while consuming minimal power during normal operation.
3Loss of information
If comprehensive sensor fusion is implemented, then context awareness improves, but device complexity increases
Solution Approach 1:
The patent introduces a sensor fusion circuitry as an intermediary layer between individual sensors and the main processor. This intermediary component aggregates, filters, and pre-processes data from multiple sensors before presenting consolidated information to the main system. This approach reduces device complexity by providing a unified interface for multi-modal input while preserving comprehensive context information through intelligent data fusion.
Data Source
AI summary
A device wake-up system has one or more sensors each receptive to an external input. The respective external inputs are translatable to corresponding signals. One or more feature extractors connected to a respective one of the one or more sensors are receptive to the signals outputted from the sensors, and the feature data is associated with the signals being generated by the corresponding one of the one or more feature extractors. One or more inference circuits are connected to a respective one of the one or more feature extractors, and inference decisions are generated from patterns of the feature data generated by a corresponding one of the one or more feature extractors. A decision combiner is connected to each of the one or more inference circuits, and a wake signal is be generated based upon an aggregate of the inference decisions provided by the one or more inference circuits.