Patient Communication Input Filtering for Intentional Gesture Detection
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Solution Overview
Problem
Current hospital communication systems fail to effectively interpret and respond to the minimal intentional gestures of critically ill or incapacitated patients, particularly those on mechanical ventilation, due to background noise and unintentional movements, limiting their ability to summon caregivers or communicate.
Innovation Solution
The development of methods and systems that utilize signal processing to differentiate between intentional and unintentional inputs from various sensors, such as touch, pressure, and audio, by applying filters based on frequency and time domain analysis, allowing patients to control devices like nurse call systems or speech generators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If standard nurse call switches are provided, then patients can summon caregivers, but critically ill patients who are too weak or on mechanical ventilation cannot activate them
Solution Approach 1:
The patent replaces mechanical switches with sensor-based detection systems that can sense minimal intentional gestures (such as eye movements, head movements, or other subtle body movements) and convert them into communication signals, enabling patients who cannot physically activate mechanical switches to still communicate effectively
Solution Approach 2:
The system introduces an intermediary processing layer that detects candidate inputs from sensors, classifies them as intentional or unintentional, and generates appropriate signals. This intermediary layer bridges the gap between the patient's limited physical capabilities and the communication system's requirements
2Ease of operation
If sensors are used to detect minimal gestures, then communication capability is improved, but false detection of unintentional movements increases
Solution Approach 1:
The system employs a classification process that analyzes sensor inputs and provides feedback to distinguish intentional gestures from unintentional movements. By evaluating patterns and characteristics of detected movements, the system can differentiate between deliberate communication attempts and random physiological movements
Solution Approach 2:
The patent changes the parameters of detection by using multiple sensors and analyzing multiple characteristics of movements (such as duration, amplitude, frequency, and pattern) rather than relying on a single threshold, thereby improving the accuracy of intentional gesture detection
3Difficulty of detecting and measuring
If multiple sensors are deployed to detect various inputs, then detection capability is improved, but differentiation between intentional and unintentional inputs becomes more difficult
Solution Approach 1:
The patent segments the signal processing task into distinct stages: candidate input detection, classification as intentional or unintentional, and signal generation. This segmentation allows each stage to focus on a specific aspect of the problem, managing complexity while maintaining comprehensive detection capabilities
Data Source
AI summary
Methods and systems for augmentative and alternative communication are disclosed. An example method can comprise receiving a candidate input, classifying the candidate input as an intentional input, and generating a signal in response to the intentional input.