Patient-Proximate Unknown Object Detection With Caregiver Voice Commands
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Solution Overview
Problem
Existing healthcare monitoring systems struggle to detect and identify unknown objects in real-time, particularly in diverse healthcare environments, and lack intuitive user interfaces for adjusting detection protocols.
Innovation Solution
A system incorporating an image sensor, microphone, and controller that allows for real-time visual training and natural language processing of vocal instructions to detect and identify unknown objects, utilizing a central database for object identification and implementing monitoring protocols based on vocal commands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing healthcare monitoring systems are used, then basic patient monitoring is provided, but unknown objects cannot be detected and identified in real-time
Solution Approach 1:
The system enables self-service object identification by allowing caregivers to train the AI model directly at the point of need. When an unknown object is detected, the system prompts the caregiver to provide identifying information or select from suggestions, and the model automatically updates its knowledge base without requiring manual retraining or system reconfiguration. This resolves the contradiction by enabling real-time adaptation to new objects while maintaining reliable detection through continuous learning.
Solution Approach 2:
The system dynamically changes its detection and identification parameters based on real-time input from caregivers. When a new object type is encountered, the system updates its object database and detection protocols to include this new object class, thereby expanding its adaptability while maintaining reliable detection performance through continuous parameter updates rather than system redesign.
2Measurement precision
If comprehensive object detection protocols are implemented, then detection accuracy improves, but system complexity increases
Solution Approach 1:
The system segments the object detection task into distinct functional modules: image capture, object detection, identification, and protocol selection. Each module operates independently and can be optimized separately. The controller manages these segmented functions through a hierarchical structure where low-level image processing is automated but high-level identification relies on caregiver input, reducing overall system complexity while maintaining high detection accuracy through specialized processing at each stage.
Solution Approach 2:
The system introduces an intermediary layer between automated detection and final identification. When the AI detects an unknown object, it presents this intermediate finding to the caregiver through a simplified interface, and the caregiver's input then triggers the appropriate identification and protocol selection. This intermediary step reduces system complexity by offloading the difficult identification task to human expertise while maintaining automated detection accuracy through the AI's specialized image analysis.
3Speed
If real-time object detection is performed, then responsiveness improves, but computational resources are consumed
Solution Approach 1:
The system applies partial action by performing comprehensive object detection only when necessary. The AI continuously monitors images but triggers full analysis and caregiver involvement only when an unknown object is detected. This selective approach maintains rapid response to critical events while reducing overall computational resource consumption by avoiding continuous intensive processing of all image data, instead using lighter-weight monitoring with targeted deep analysis only when needed.
Data Source
AI summary
A system for detecting an unknown object in a region of interest, the system including: an image sensor; a microphone; and a controller coupled to the image sensor and the microphone for receiving a vocal instruction from a caregiver and for analyzing images from the image sensor in response to the vocal instruction. The controller may be configured to detect and identify the unknown object within the analyzed images. The controller may also monitor the object according to a monitoring protocol. The instruction may identify the region of interest in which the object may be located within the analyzed images. The controller may monitor the region of interest according to a monitoring protocol.