Load-Sensor Litter Box Monitoring for Early Animal Health Detection
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Solution Overview
Problem
Existing animal health monitoring systems, such as those using cameras and RFID collars, provide one-dimensional information and require expert interpretation, often failing to detect subtle health issues and disrupting animal behavior, while relying on invasive methods like microchips and specific litter types.
Innovation Solution
A system utilizing load sensors under the litter box to monitor animal behavior, classifying interactions into events using machine learning, identifying individual animals without external identification, and providing early indicators of health conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If cameras and video recording devices are used to track litter box activity, then basic behavioral information can be captured, but the system provides only one-dimensional information and requires qualified behaviorist interpretation
Solution Approach 1:
The patent transitions from one-dimensional visual information (cameras) to multi-dimensional data collection by integrating load sensors that measure weight, pressure distribution, and temporal patterns. This adds quantitative dimensional data that complements visual observations, enabling more comprehensive behavioral analysis without requiring expert interpretation.
Solution Approach 2:
The system employs machine learning algorithms that automatically classify and interpret animal behavior patterns from sensor data, eliminating the need for qualified behaviorist interpretation. The algorithm self-trains on collected data to recognize elimination events, grooming behaviors, and health indicators autonomously.
2Reliability
If RFID collars or microchips are used for animal identification, then individual animals can be tracked, but invasive methods are required
Solution Approach 1:
The patent extracts the identification function from external devices (RFID collars, microchips) and embeds it within the litter box system itself. Load sensors detect unique weight patterns and temporal signatures of each animal during litter box interactions, enabling non-invasive identification based on natural behavioral mechanics rather than implanted or worn technology.
Solution Approach 2:
The system replaces electronic identification mechanisms (RFID, microchips) with mechanical sensing based on weight and pressure patterns. This substitution uses the animal's own physical interaction with the litter box as the identification medium, eliminating the need for separate electronic tracking devices.
3Loss of information
If specialized litter types are required for monitoring, then health data can be collected, but the system limits animal choice and requires specific materials
Solution Approach 1:
The load sensor-based monitoring system is designed to work with any litter type (clumping, non-clumping, various materials) rather than requiring specialized litter. The sensors detect mechanical interactions and weight changes universally applicable across different elimination behaviors and litter compositions, making the system adaptable to pet owner preferences and animal choices.
4Reliability
If visual indicators in litter boxes are used, then some health information can be observed, but symptoms only appear in mid- to late-stages of disease
Solution Approach 1:
The system performs preliminary detection of health issues by continuously monitoring behavioral patterns and temporal characteristics before visible symptoms manifest. Load sensor data reveals subtle changes in elimination frequency, duration, and mechanics that precede observable litter box symptoms, enabling early intervention.
Solution Approach 2:
The system establishes continuous feedback loops where collected sensor data is analyzed against baseline patterns to detect deviations indicating health issues. This ongoing monitoring and comparison enable real-time detection of behavioral changes that signal emerging health problems before they become apparent through traditional visual inspection.
Data Source
Figure 1A
Figure 1B~1C
Figure 2
AI summary
The present disclosure provides systems and methods for animal health monitoring. Load data can be obtained from a plurality of load sensors associated with a platform carrying contained litter thereabove, wherein individual load sensors of the plurality of load sensors are separated from one another and receive pressure input from the platform independent of one another. If the load data is determined or not to be from an animal interaction with the contained litter, an animal behavior property associated with an animal is recognized if a determination is made based on load data that the interaction with the contained litter was due to the animal interaction. The animal behavior property is classified into an animal classified event using a machine learning classifier. A change in the animal classified event is identified as compared to a previously recorded event associated with the animal.