Motion-Sensing Pet Collar for Real-Time Droppings Localization
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Solution Overview
Problem
Pet owners face the challenge of locating and cleaning up pet droppings and urine spots on their property without real-time tracking and notification systems, leading to unsightly and potentially harmful lawn damage.
Innovation Solution
A system utilizing a motion-sensing collar with onboard machine learning and location tracking to detect pet defecation or urination events, combined with various localization methods (AoA, phase-based ranging, camera-assisted, and GPS-based tracking) to notify owners and optionally trigger robotic clean-up.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If geo-boundary techniques and buried wire techniques are used to keep pets on property, then pet containment is improved, but real-time droppings location capability is lost
Solution Approach 1:
The patent combines geo-boundary containment functionality with droppings location tracking into a single integrated system. The collar device simultaneously performs pet containment through geo-fencing and tracks droppings locations using motion sensors and location services, eliminating the need for separate systems and ensuring both containment reliability and location information are maintained together.
2Device complexity
If manual droppings location method is used, then device complexity is reduced, but clean-up efficiency and timeliness deteriorate
Solution Approach 1:
The system enables automatic detection and reporting of droppings events without requiring manual monitoring by the pet owner. The motion sensors on the collar automatically detect when the pet defecates or urinates, the location is captured via GPS or other location services, and the information is transmitted to the owner's device, allowing timely clean-up while minimizing owner effort.
3Measurement precision
If motion sensing and machine learning are added to detect droppings events, then droppings detection accuracy is improved, but device complexity and power consumption increase
Solution Approach 1:
The patent replaces complex mechanical or electronic detection mechanisms with motion sensors combined with machine learning algorithms. Instead of using sophisticated hardware to directly detect droppings, the system uses accelerometers and gyroscopes to capture motion patterns, then applies machine learning to recognize droppings events from these patterns, reducing hardware complexity while maintaining high detection accuracy.
Data Source
AI summary
A system and method for detecting and responding to pet urination and defecation events is disclosed. A pet-worn motion capture device equipped with an inertial measurement unit (IMU) and wireless transceiver transmits data to a base station or mobile application. A machine learning model running on the device classifies the pet's behavior in real time and signals when a urination or defecation event is detected. Upon classification, the system records the pet's location using one or more localization methods, including angle-of-arrival triangulation, phase-based ranging, GPS, or camera-based visual positioning. A notification is sent to the pet owner with the event type and location, enabling clean-up. In some embodiments, a robotic unit may be deployed to perform automated retrieval or rinsing based on the location data. The system operates with minimal pet-mounted hardware and can be configured for standalone operation or integrated with additional sensors, mapping data, or user-defined parameters.