Equine Leg Wearable Sensor Array for Early Injury Prediction
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Solution Overview
Problem
Existing systems fail to effectively monitor and predict equine health conditions, particularly leg injuries in racehorses, leading to potential lameness and worse due to undetected early warning signs.
Innovation Solution
A wearable device with spatially distributed sensors on a horse's leg captures biometric data, which is analyzed using machine learning models to generate health predictions and recommendations, integrated with a client device interface and backend server for real-time monitoring and intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional monitoring methods are used, then device complexity is low, but measurement precision and reliability of health detection are insufficient
Solution Approach 1:
The monitoring system is segmented into multiple independent sensor units (accelerometer, gyroscope, magnetometer, barometer, temperature sensor, humidity sensor) distributed across the wearable device. Each sensor independently measures specific physical parameters, and their data is integrated through machine learning algorithms to achieve comprehensive health monitoring with high precision while maintaining modular device architecture
Solution Approach 2:
Machine learning models serve as intermediaries between raw sensor data and health condition predictions. The models process and interpret complex sensor signals, transforming multi-source data into actionable health insights, thereby enabling high measurement precision without requiring direct complex hardware configurations
2Reliability
If continuous monitoring is implemented, then reliability of health detection improves, but loss of energy increases
Solution Approach 1:
The system implements periodic sampling of sensor data rather than continuous monitoring. Sensors collect data at optimized intervals, and the machine learning model processes batches of data periodically. This approach maintains reliable health detection by capturing sufficient temporal information while significantly reducing energy consumption compared to continuous operation
Solution Approach 2:
The machine learning model performs edge computing on the wearable device itself, processing sensor data locally without requiring constant cloud communication. This self-service processing reduces energy consumption by eliminating frequent wireless transmissions while maintaining continuous monitoring capability through local analysis
3Measurement precision
If multiple sensors are deployed, then measurement precision increases, but device complexity increases
Solution Approach 1:
The machine learning model serves multiple functions simultaneously: it fuses data from different sensor types, detects various health conditions (lameness, injuries, temperature abnormalities), and provides predictive analytics. This multi-functionality allows the system to achieve high measurement precision across multiple health parameters without proportionally increasing device complexity, as the same computational infrastructure supports diverse monitoring capabilities
Data Source
AI summary
An equine health monitoring system generates health predictions based on monitored sensor data from at least one wearable device worn on a leg of an equine. The wearable device includes a pad and one or more securing straps for securing the pad to the leg of the equine. The pad includes an array of spatially distributed sensors for obtaining sensor data and a controller for transmitting the sensor data to a prediction application of a client device. The predication application applies a machine learning model to the sensor data and equine profile to generate likelihood values associated with one or more equine health conditions. The prediction application generates from the likelihood values, a prediction associated with the one or more equine health conditions, and outputs a visual representation of the prediction to a user interface of the client device.