Implanted Biosensor Monitoring for Hemophilia Joint Bleed Detection
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Solution Overview
Problem
Existing technologies struggle to accurately and rapidly identify internal bleeding in the target joints of hemophiliac patients, leading to premature joint damage and the need for surgical interventions, as conventional data analysis methods are hindered by noise from patient movement and environmental factors.
Innovation Solution
Implantable biosensors combined with machine learning techniques to cleanse and process data, employing thresholds for each biosensor type to remove noise and accurately detect internal bleeds, using a trained classification model to provide timely treatment recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional data analysis methods are used to monitor joint health, then the system is simple to implement, but the detection accuracy of internal bleeding is low due to noise from patient movement and environmental factors
Solution Approach 1:
The patent segments the data analysis process into multiple stages: raw data collection from multiple biosensors, noise filtering using machine learning models, feature extraction, and classification. This segmentation allows each stage to be optimized independently, improving overall detection accuracy while managing system complexity through modular architecture
Solution Approach 2:
The patent introduces machine learning models as intermediary components between the biosensors and the decision-making system. These models act as mediators that process raw sensor data, remove noise, and extract meaningful features, thereby improving detection accuracy without requiring direct complex processing of raw signals
2Difficulty of detecting and measuring
If implanted biosensors are used to collect joint health data, then the detection capability is improved, but the system complexity and data processing requirements increase
Solution Approach 1:
The patent implements self-service mechanisms where the system automatically performs noise filtering, feature extraction, and anomaly detection using machine learning models trained on historical data. This automation reduces the need for manual intervention and simplifies operation despite the complex sensor array, allowing the system to serve itself in processing and interpreting data
Solution Approach 2:
The patent applies preliminary action by pre-training machine learning models with historical joint health data before deployment. This preliminary training enables the system to recognize patterns and filter noise effectively from the start, improving detection capability while reducing the complexity of real-time decision-making
3Measurement precision
If noise filtering techniques are applied to biosensor data, then the measurement precision is improved, but the processing time and computational requirements increase
Solution Approach 1:
The patent applies partial action by implementing selective noise filtering that focuses computational resources on the most critical frequency ranges and sensor inputs. Rather than processing all data uniformly, the system identifies and filters only the noise components that most impact detection accuracy, reducing overall processing time while maintaining data accuracy
Data Source
AI summary
A computer system provides patient monitoring and treatment using implanted biosensors. Data associated with a target joint of a patient is collected via one or more implanted biosensors. A plurality of feature values are extracted from the data. The plurality of feature values are processed using a trained classification model to select a recommendation. The recommendation is provided to mitigate hemophilia-related injury to the target joint. Embodiments of the present invention further include a method and program product for providing patient monitoring and treatment using implanted biosensors in substantially the same manner described above.