Audio Analytics Machine Learning for Patient Behavior Classification
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Solution Overview
Problem
Current video analytics systems struggle to automatically classify and leverage patient behaviors, such as physical movements, non-verbal communications, and verbal statements, which are indicative of a patient's health condition, due to the difficulty in obtaining objective metrics from video streams.
Innovation Solution
A computer-implemented machine-learning engine analyzes audio and semantic text data from video streams to identify feature sets indicative of alerting behaviors, determining patient behavior and automatically invoking alerts when specific conditions are met, using a system comprising a video camera, processor, and memory encoded with instructions to perform these analyses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If video analytics systems are used to monitor patient behaviors, then patient care can be improved through automated detection, but the difficulty in obtaining objective metrics from video streams prevents accurate classification of patient behaviors
Solution Approach 1:
The patent replaces manual video analysis with an automated machine learning system that processes video streams. The system uses trained models to automatically extract features and classify patient behaviors, substituting human mechanical analysis with computational algorithms that can objectively measure and interpret video data without subjective bias.
Solution Approach 2:
The machine learning system performs self-training and self-improvement through automated feature extraction and model training processes. The system uses labeled training data to teach itself how to recognize patient behaviors, then autonomously applies this knowledge to classify new video streams without requiring continuous human intervention or manual metric definition.
2Extent of automation
If machine learning models are trained using labeled training data, then the system can automatically classify patient behaviors, but the complexity of the system increases due to multiple processing components
Solution Approach 1:
The patent combines multiple processing functions into a unified machine learning pipeline. The feature extraction module, model training component, and behavior classification system are integrated into a single automated workflow that processes video streams end-to-end. This merging reduces operational complexity despite the sophisticated underlying algorithms, as the system functions as a cohesive unit rather than separate manual processes.
Solution Approach 2:
The patent introduces an intermediary machine learning model that acts as a bridge between raw video data and clinical decision-making. This model translates complex video stream information into standardized behavior classifications that healthcare providers can easily interpret, mediating between the complexity of automated analysis and the simplicity needed for clinical use.
3Speed
If real-time analysis of audio and semantic text data is performed, then alerting behaviors can be detected promptly, but the processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary processing of video and audio streams by pre-extracting relevant features and pre-training classification models before actual patient monitoring begins. This advance preparation allows the system to quickly classify behaviors in real-time without performing complex analysis during critical monitoring periods, reducing processing delays when alerts are needed.
Solution Approach 2:
The patent segments the analysis process into distinct modular components: video feature extraction, audio feature extraction, semantic text processing, and behavior classification. Each component processes specific data types independently and in parallel, reducing overall processing time compared to sequential analysis. This segmentation allows the system to handle multiple data streams simultaneously without overwhelming computational resources.
Data Source
AI summary
Apparatus and associated methods relate to invoking an alert based upon a behavior of a patient as determined by a machine learning model operating on an audio stream of the patient. Audio data, and semantic text data are extracted from an audio stream of the patient. The audio data are analyzed to identify a first feature set. The semantic text data are analyzed to identify a second feature set. Using a computer-implemented machine-learning model, a patient behavior of the patient is determined based on the first and/or second features sets. The patient behavior is compared with a set of alerting behaviors corresponding to a patient classification of the patient. The alert is automatically invoked when the patient behavior is determined to be included in the set of alerting behaviors corresponding to the patient classification of the patient.