Patient Gesture Communication Using Noise-Filtered Intent Detection
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Solution Overview
Problem
Current hospital communication systems fail to effectively allow critically ill or incapacitated patients to summon caregivers, as they are unable to activate standard nurse call switches or communicate through voice due to mechanical ventilation and limited physical abilities.
Innovation Solution
The development of methods and systems that process complex signals embedded in noise to enable patients to control devices using intentional gestures, such as tongue clicks or eye blinks, by filtering out ambient noise and distinguishing intentional from unintentional movements through signal processing techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If standard nurse call switches are provided, then caregivers can be summoned, but critically ill patients who are too weak cannot activate them
Solution Approach 1:
The patent replaces mechanical switches with acoustic and optical sensing systems. Acoustic sensors detect tongue clicks and other minimal gestures, while optical sensors detect eye movements. This substitution allows patients with minimal physical capability to communicate without requiring manual activation of mechanical switches.
Solution Approach 2:
The system changes the activation parameter from mechanical force (pressing a switch) to acoustic signals (tongue clicks) and optical signals (eye movements). This parameter change enables patients who cannot generate mechanical force to still activate communication devices through alternative physiological parameters.
2Reliability
If mechanical ventilation is used to support patients, then breathing is maintained, but patients lose voice communication capability
Solution Approach 1:
The patent introduces intermediary devices including acoustic sensors that detect tongue clicks and optical sensors that detect eye movements. These intermediaries translate minimal patient gestures into communication signals, bypassing the need for voice production that is blocked by mechanical ventilation.
Solution Approach 2:
The system replaces voice-based communication with sensor-based detection of alternative gestures. Acoustic sensors capture tongue clicks and optical sensors capture eye movements, substituting the vocal communication pathway that is blocked by mechanical ventilation.
3Ease of operation
If sensors are used to detect patient gestures, then communication is enabled, but ambient noise and unintentional movements create false signals
Solution Approach 1:
The system employs feedback mechanisms where the processor analyzes sensor signals in real-time, comparing them against learned patterns of intentional gestures. The system provides feedback by only responding to signals that match the trained patterns, filtering out noise and unintentional movements that do not conform to the learned gesture characteristics.
Solution Approach 2:
The system performs preliminary training to establish the patient's specific gesture patterns before actual communication begins. This preliminary action creates a reference model of intentional gestures, allowing the system to distinguish them from noise and unintentional movements during subsequent use.
4Measurement precision
If signal processing filters are applied to distinguish intentional gestures, then communication accuracy improves, but system complexity increases
Solution Approach 1:
The system performs self-training by automatically learning the patient's gesture patterns during an initial training period without requiring manual configuration. The processor autonomously analyzes training data, identifies patterns, and creates classification models, eliminating the need for complex manual setup and reducing overall system complexity.
Solution Approach 2:
The system uses a universal processor-based architecture that can handle multiple sensor types (acoustic, optical, tactile) and multiple gesture types through a single unified signal processing framework. This multi-functional approach reduces complexity compared to having separate dedicated systems for each sensor type.
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.