Splinting Activity Detection Using Waveform Feature Extraction
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Solution Overview
Problem
Current methods for performing splinting activity detection are inefficient and ineffective due to the need for computationally expensive training operations, high storage requirements, and extensive data transmission, which hinders the accuracy and efficiency of identifying splinting activity in breathing patterns.
Innovation Solution
A splinting activity detection machine learning model is used to compare observed inspiration-expiration waveform patterns with expected patterns, reducing the need for extensive training operations and improving computational, storage, and network efficiency by generating a predicted interruption score.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional machine learning training operations are used for splinting activity detection, then detection accuracy can be improved, but computational cost and training time increase significantly
Solution Approach 1:
The patent pre-processes breathing waveform data during system initialization or offline periods to create training datasets and model parameters before actual detection is needed. This preliminary action separates data preparation from real-time detection, allowing accurate models to be built in advance without delaying clinical decision-making.
Solution Approach 2:
The patent extracts specific features from breathing waveforms (such as inspiration-expiration pattern characteristics) that are most relevant for detecting splinting activity. By extracting only the essential features rather than processing entire raw waveforms during detection, the system achieves accurate detection with reduced computational requirements and faster processing.
2Reliability
If extensive training data is stored for model development, then detection reliability improves, but storage requirements increase
Solution Approach 1:
The patent extracts and stores only the most discriminative features and patterns from breathing waveforms rather than storing complete raw datasets. This feature extraction approach maintains detection reliability by preserving essential diagnostic information while dramatically reducing the storage burden of training data.
Solution Approach 2:
The patent transforms raw breathing waveform data into standardized parameter representations (such as normalized inspiration-expiration ratios, waveform morphology parameters) that capture essential diagnostic information in a compact form. This parameter transformation enables reliable detection with minimal storage requirements.
3Measurement precision
If complex machine learning models are deployed for real-time detection, then detection accuracy improves, but computational resources and processing power increase
Solution Approach 1:
The patent extracts and utilizes only the most critical waveform features for splinting detection, avoiding the need to process entire raw waveforms or employ complex models. This selective feature extraction enables accurate detection using simple comparison operations that require minimal computational power and can run on resource-constrained devices.
Solution Approach 2:
The patent applies a simplified detection approach that focuses on the most salient characteristics of splinting activity in breathing patterns. Rather than implementing comprehensive complex models, the system uses targeted analysis of key waveform parameters, achieving sufficient detection accuracy with reduced computational overhead.
Data Source
AI summary
Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and/or the like for performing splinting activity detection. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform splinting activity detection using at least one of splinting activity detection machine learning models, observed inspiration-expiration waveform pattern, and expected inspiration-expiration waveform patterns.