Human De-Identification Data Collection with Adaptive Pet Sensor Sampling
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Solution Overview
Problem
Existing devices for companion animals fail to efficiently collect and process human de-identification information for interactive artificial intelligence learning, particularly considering the varying sensory capabilities and states of different companion animals, leading to inefficiencies and high power consumption.
Innovation Solution
A human de-identification information collection device that includes multiple sensors (microphones, inertial measurement, gas, biometric, and GPS) with adaptive sampling rates and operation modes based on animal breed, age, gender, and temperament, using interpolation models and noise filtering to manage data collection efficiently and reduce power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If adaptive sampling rates are used to collect data from multiple sensors, then data collection efficiency is improved, but device complexity increases
Solution Approach 1:
The patent implements adaptive sampling rates that dynamically adjust based on the companion animal's detected state (e.g., sleeping, awake, exercising). The processor modifies sampling frequencies of microphones, inertial measurement devices, and gas sensors in real-time, transitioning between high sampling rates during active states and low sampling rates during resting states, thereby optimizing the balance between data collection efficiency and power consumption without permanently increasing device complexity
Solution Approach 2:
The system changes operational parameters (sampling rates, interpolation model complexity) based on detected animal states and breed characteristics. Different sampling rates are applied to different sensor types depending on the animal's state, and interpolation models are adjusted to match the specific needs of different breeds, allowing efficient data collection while managing computational complexity through parameter adaptation rather than fixed high complexity
2Measurement precision
If multiple sensors with high sampling rates are used to collect comprehensive data, then measurement precision is improved, but use of energy increases
Solution Approach 1:
The system employs periodic sampling at variable frequencies rather than continuous high-rate sampling. The processor activates sensors at high sampling rates only during specific periods when the animal is awake or engaged in activities requiring detailed monitoring, and switches to low sampling rates or sleep mode during resting periods, thereby maintaining measurement precision when needed while significantly reducing overall power consumption through periodic rather than continuous operation
Solution Approach 2:
The system performs preliminary actions by detecting the animal's state using low-power sensors (such as inertial measurement devices) before activating high-power sensors (microphones, gas sensors) at high sampling rates. This preliminary detection determines whether comprehensive high-precision data collection is necessary, allowing the system to maintain measurement precision for critical moments while avoiding unnecessary high energy consumption during extended periods
3Measurement precision
If interpolation models with high complexity are used to process data, then data integration accuracy is improved, but device complexity increases
Solution Approach 1:
The patent implements parameter changes in interpolation model complexity based on the companion animal's breed, age, and detected state. The processor selects appropriate interpolation models (e.g., linear interpolation for simple cases, higher-order interpolation for complex patterns) dynamically, using simpler models for data from animals with straightforward behavioral patterns and more complex models only when necessary, thereby achieving accurate data integration without permanently increasing device complexity
Solution Approach 2:
The system applies partial action by using high-complexity interpolation models only for specific portions of the data processing pipeline rather than for all data uniformly. The processor applies different levels of processing complexity to different sensor data types and time periods, using simple interpolation for routine data and complex interpolation only when the animal's state or breed characteristics require it, thereby achieving necessary data integration accuracy while minimizing overall computational complexity
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
The device effectively collects and processes human de-identification information, adapting to the animal's state and sensory capabilities, enhancing data integration and reducing power usage, thus enabling efficient interactive AI learning.
Implementation Method 1
at least one microphone for capturing sounds generated around a companion animal and generating first audio data and second audio data
Implementation Method 2
an inertial measurement device for generating inertial data about a change in acceleration and angular velocity according to movement of the companion animal
Implementation Method 3
a gas sensor for generating olfactory data by detecting gas contained in the air around the companion animal
Data Source
AI summary
A human de-identification information collection device for artificial intelligence learning according to an embodiment may comprise: at least one microphone for capturing sounds generated around a companion animal and generating first audio data and second audio data, an inertial measurement device for generating inertial data about a change in acceleration and angular velocity according to movement of the companion animal, and a processor for determining each sampling rate for collecting the first audio data, the second audio data, and the inertial data on the basis of at least one among a breed, an age, a gender, whether or not neutered, and a temperament type of the companion animal.