Real-Time Animal Behavior Detection With Loadable Sensor Models
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Solution Overview
Problem
Existing animal behavior monitoring devices require extensive programming knowledge and are limited in flexibility and energy efficiency, with data transmission and processing burdens hindering real-time behavioral recognition and adaptation to different species.
Innovation Solution
A device with a modular sensor-specific behavior model stored externally, allowing adaptation without firmware changes, using machine-learned models and a user-friendly interface for model creation, enabling flexible and energy-efficient behavioral recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor data is downloaded and processed at the receiver, then behavior can be determined from recorded sensor data, but a considerable amount of data must be logged and transmitted and behavior can only be determined with delay
Solution Approach 1:
The patent implements preliminary action by performing behavior recognition processing directly on the data logger before data transmission. The processor evaluates sensor data locally using stored behavior models to generate behavior results in real-time, eliminating the delay inherent in post-processing approaches while maintaining accurate behavior detection.
2Measurement precision
If sensor data is processed at the receiver, then behavior analysis can be performed, but a considerable amount of data must be transmitted consuming limited energy
Solution Approach 1:
The patent extracts the essential behavior recognition functionality from the receiver and embeds it directly in the data logger. By taking out the processing requirement from the receiver side and implementing it locally with compact behavior models, the system transmits only essential behavior results rather than raw sensor data, dramatically reducing energy consumption while preserving behavior analysis capability.
Solution Approach 2:
The behavior recognition processing is performed preliminarily at the data logger before data leaves the animal carrier. This preliminary evaluation extracts only the necessary behavior information for transmission, eliminating the need to transmit and process large volumes of raw sensor data at the receiver, thus reducing energy consumption.
3Extent of automation
If hardware-level programming is used in the device, then behavioral models can be integrated, but users require extensive programming skills limiting widespread application
Solution Approach 1:
The patent uses copying by storing behavior models as separate data structures in memory rather than hardcoding them in firmware. Users can copy and load different behavior model files into the device without modifying the underlying hardware or firmware, dramatically simplifying operation while maintaining full automation of behavior recognition.
4Adaptability or versatility
If multiple specific behavioral models for different species are combined, then general applicability increases, but computing and storage effort increases negatively impacting energy efficiency
Solution Approach 1:
The patent segments the behavior model system into modular, independently loadable model files for different species and sensor configurations. Instead of loading all possible models simultaneously, the system only loads the specific model needed for the current application, reducing computing and storage energy requirements while maintaining versatility across multiple species.
Data Source
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Figure 3
AI summary
The present invention relates to a device for automatically recognizing the behavior of encoded animals, comprising a sensor (10) for detecting behavior-specific sensor data of an animal (1) connected to the device (100); a processor (20) for evaluating the behavior-specific sensor data detected by the sensor (10) within the device (100) to derive a behavior of the animal (1) corresponding to the sensor data on the basis of an associated sensor-specific behavior model;and a memory (30) in which the associated sensor-specific behavior model is stored, wherein different sensor-specific behavior models can be selectively stored in the memory (30) from outside the device (100), wherein a suitably selected initial model can be learned using training data in an external software program and then transferred to the device (100), wherein the behavior models that can be selectively stored in the memory (30) from outside the device (100) allow the sensor-specific behavior model to be adapted independently of any operating software or firmware of the device (100) before each use on a different animal (1) or with different sensors (10);