Asset Tracker Microphone Sound Source Direction
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Solution Overview
Problem
Existing asset tracking systems face challenges in balancing location accuracy, reporting frequency, and battery life, especially when monitoring environmental conditions using integrated microphones.
Innovation Solution
The integration of one or more microphones into asset trackers to monitor environmental conditions, correlating sound data with data from other sensors, using an IMU to determine orientation, and employing a pre-learned machine learning sound identification model to identify sound sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If microphones are integrated into asset trackers to monitor environmental conditions and identify sound sources, then measurement precision and contextual information quality are improved, but device complexity and energy consumption increase
Solution Approach 1:
The patent combines multiple sensors (microphones, IMU, other environmental sensors) into an integrated asset tracking device. The microphones are merged with existing tracking hardware and software, allowing environmental monitoring to be performed alongside location tracking. This consolidation improves measurement precision while managing device complexity through unified system architecture.
Solution Approach 2:
The asset tracker is designed with multi-functionality, serving both location tracking and environmental condition monitoring purposes. The microphone system is integrated into the existing tracker framework, enabling the device to perform multiple functions (location tracking, sound detection, environmental monitoring) without requiring separate dedicated devices, thus improving measurement capabilities while controlling overall system complexity.
2Measurement precision
If microphones and machine learning models are used to identify sound sources and monitor environmental conditions, then measurement precision is improved, but use of energy increases
Solution Approach 1:
The system employs partial action by selectively activating the microphone and machine learning sound identification model only when needed for specific monitoring tasks. Rather than continuously processing all audio data, the system triggers sound source identification based on detected acoustic events or predefined conditions, reducing overall energy consumption while maintaining high measurement precision when the feature is actively used.
Solution Approach 2:
The machine learning model operates autonomously to identify sound sources from raw audio data captured by the microphones. The system self-processes the acoustic information using pre-trained models, eliminating the need for continuous external processing or manual intervention. This self-service approach optimizes energy usage by performing computations efficiently on-device and only when acoustic events warrant analysis.
3Measurement precision
If sound data is collected and processed continuously to monitor environmental conditions, then measurement precision is improved, but loss of energy increases
Solution Approach 1:
The system implements periodic action by collecting and processing sound data at intervals rather than continuously. The microphones monitor environmental conditions using periodic sampling triggered by acoustic events or at scheduled intervals, allowing the system to maintain measurement precision for environmental monitoring while significantly reducing energy loss compared to continuous processing. This approach balances data quality with battery conservation.
Data Source
AI summary
A computerized method of an integrated microphone to monitor environmental conditions of battery-operated asset tracker comprising: integrating one or more microphones into each asset tracker of a cluster of asset trackers; using the one or more microphones to monitor an environmental condition of a battery-operated asset tracker; correlate a set of sound data received form the one or more microphones with another set of data from other sensors of each asset tracker of the cluster of asset trackers; using an IMU (Inertial Management Unit) to determine an orientation in space of each asset tracker; with the data of the one or more microphones and the orientation in space of each asset tracker, determining a direction of a sound in a proximity of the cluster of asset trackers; and providing a pre-learned machine learning sound identification model to identify a sound source of the sound.