Canine Mobility Detection via Baseline Comparison
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Solution Overview
Problem
Current solutions fail to provide real-time analysis and comparison of canine mobility data, making it difficult for pet owners to determine if their pets have mobility issues, especially distinguishing between normal aging and breed-specific issues.
Innovation Solution
A system and method for canine mobility detection that processes mobility data from attached devices, determining metrics like velocity, acceleration, and entropy, compares them to baseline metrics from similar canines, and displays alerts on user interfaces when scores exceed thresholds, indicating potential mobility issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If mobility data is collected and analyzed in real-time with comparison to baseline data, then measurement precision and reliability are improved, but device complexity and processing requirements increase
Solution Approach 1:
The system segments the mobility analysis into multiple independent metrics (velocity, acceleration, cadence, entropy) that can be processed separately and then aggregated. This segmentation allows complex mobility patterns to be broken down into manageable components, improving detection precision without overwhelming the system with monolithic complexity
Solution Approach 2:
The system performs preliminary actions by establishing baseline mobility metrics for individual pets before actual monitoring begins. These baselines are stored and used for future comparisons, allowing the system to detect deviations without requiring complex real-time analysis of absolute values, thus improving precision while managing computational complexity
2Loss of time
If real-time mobility monitoring is implemented, then loss of time in detecting mobility issues is reduced, but use of energy and device complexity increase
Solution Approach 1:
The monitoring device operates autonomously, automatically collecting mobility data, comparing it against stored baselines, and generating alerts without requiring owner intervention. The system self-manages the entire monitoring process from data collection to interpretation, reducing time loss while the energy consumption is offset by the efficiency gains from automated processing
Solution Approach 2:
The system implements continuous feedback loops where mobility data is constantly monitored and compared to baselines, with immediate feedback provided through alerts when thresholds are exceeded. This real-time feedback mechanism minimizes detection time by instantly identifying issues rather than requiring periodic manual checks, while energy consumption is optimized through event-triggered processing rather than continuous full-scale analysis
3Measurement precision
If baseline comparisons with similar canines are performed, then measurement precision is improved, but loss of time in data processing increases
Solution Approach 1:
Baseline mobility data for different breeds and sizes is pre-computed and stored in the system before actual monitoring begins. When a pet's mobility is assessed, the system quickly matches the pet to appropriate pre-established baselines rather than performing complex comparisons from scratch, thereby improving breed-specific assessment precision while minimizing real-time processing time
Solution Approach 2:
The system applies different baseline standards tailored to each pet's specific breed, size, and age characteristics rather than using a single universal standard. This local quality approach allows the system to use specialized baseline data relevant to each individual pet's expected mobility patterns, improving measurement precision for breed-specific assessment while the pre-categorized nature of these baselines keeps processing efficient
Data Source
AI summary
A computer-implemented method for canine mobility detection is disclosed. The method includes processing mobility data captured by a device attached to a canine, analyzing canine data corresponding to the canine to determine at least one baseline canine, wherein the at least one baseline canine is similar to the canine, for each of the one or more metrics, comparing the one or more metrics of the canine to one or more baseline metrics of the at least one baseline canine, determining one or more scores for each of the one or more metrics, the one or more scores based on a normal range of the one or more metrics from the one or more baseline metrics of the at least one baseline canine, and displaying at least one alert on one or more user interfaces of a user device.