Augmentative Communication Input Detection for Ventilated Patients
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Solution Overview
Problem
Current hospital accreditation standards require patients to have means to summon caregivers, but critical care patients, especially those on mechanical ventilation, are unable to activate standard nurse call systems due to weakness or paralysis, and existing communication methods fail to detect minimal intentional gestures.
Innovation Solution
The development of methods and systems that use signal processing to classify intentional inputs from various sensors, such as microphones, pressure sensors, and infrared reflectance sensors, to generate signals that control devices like nurse call switches, speech generating devices, and pain pumps, filtering out background noise and unintentional movements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If standard nurse call switches are used, then caregivers can be summoned by patients with sufficient physical ability, but critical care patients who are weak or paralyzed cannot activate them
Solution Approach 1:
The patent replaces mechanical switches with sensor-based detection systems including acoustic sensors to detect tongue clicks, pressure sensors to detect minimal pressure changes, and infrared sensors to detect eye movements. This substitution enables patients with minimal physical capability to communicate without requiring sufficient strength to activate traditional mechanical switches.
2Reliability
If mechanical ventilation is provided to support breathing, then patients can survive with respiratory failure, but they lose the ability to use voice to communicate or summon help
Solution Approach 1:
The patent introduces intermediary devices including speech-generating devices that translate detected intentional gestures into spoken communication, and alternative input methods such as eye movement detection and pressure sensor activation. These intermediaries bridge the gap between the patient's limited physical capabilities and the need for effective communication while on mechanical ventilation.
3Measurement precision
If existing communication methods are used, then simple gestures can be detected, but minimal intentional gestures from incapacitated patients cannot be distinguished from background noise and tremors
Solution Approach 1:
The patent implements feedback mechanisms where the system continuously monitors sensor inputs, compares detected signals against established patterns of intentional gestures, and adjusts detection thresholds dynamically. This feedback loop enables the system to distinguish minimal intentional gestures from background noise and tremors by learning and adapting to the patient's specific movement patterns.
Solution Approach 2:
The patent employs multiple sensor types (acoustic, pressure, infrared) that can detect various types of gestures including tongue clicks, eye movements, and minimal pressure changes. This multi-functional approach allows a single communication system to handle diverse input methods, increasing measurement precision while distributing the complexity across multiple specialized sensors rather than one complex device.
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.