AI Video Monitoring for Multi-Subject Distress Detection
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Solution Overview
Problem
Healthcare practitioners face challenges in monitoring multiple subjects simultaneously, leading to missed signs of distress or adverse events due to divided attention and communication lapses, as current systems rely on human insight and video feeds that can overwhelm practitioners.
Innovation Solution
A subject monitoring system using artificial intelligence processes video and contextual data to identify subject actions, emotions, and identities, prioritizing feeds and sending alerts to caregivers through a rules engine and mitigation module.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a practitioner monitors multiple subjects using video feeds, then the ability to remotely monitor many subjects is improved, but the risk of missing distress signs increases due to divided attention
Solution Approach 1:
The patent replaces the mechanical system of human visual monitoring with an automated computer vision system using machine learning models. The system processes video feeds from multiple subjects simultaneously, extracting skeletal features and detecting adverse events without human intervention, thereby maintaining high detection accuracy while monitoring numerous subjects.
Solution Approach 2:
The patent introduces an intermediary automated analysis system between the video feeds and the practitioner. This intermediary system processes video data, identifies distress signs, and alerts practitioners only when adverse events are detected, filtering out normal variations and reducing the cognitive load on practitioners while maintaining reliable detection.
2Ease of operation
If a practitioner divides attention equally among multiple video feeds, then all subjects are monitored, but details indicative of distress are missed due to prioritization requirements
Solution Approach 1:
The patent replaces human attention allocation with an automated machine learning system that continuously analyzes all video feeds with equal precision. The system detects subtle distress signs such as grimacing or attempted movements without requiring practitioner prioritization, maintaining high measurement precision across all subjects simultaneously.
3Productivity
If video feeds from multiple subjects are displayed simultaneously, then remote monitoring capability is improved, but communication lapses occur when practitioners are not notified in time
Solution Approach 1:
The patent implements preliminary detection and automatic alerting mechanisms that notify practitioners immediately when adverse events are detected. The system continuously analyzes video feeds, identifies distress signs before they escalate, and sends notifications without delay, eliminating communication lapses and ensuring timely practitioner response.
Solution Approach 2:
The patent establishes a feedback loop where the automated system continuously monitors video feeds, detects adverse events, and provides immediate feedback to practitioners through alerts. This real-time feedback mechanism ensures practitioners are notified without delay and can respond promptly to subjects requiring assistance.
4Measurement precision
If human insight is required to identify signs of distress, then accurate detection is possible, but the system becomes overwhelmed when practitioners monitor more than 20 subjects
Solution Approach 1:
The patent replaces human insight with automated machine learning models that analyze video feeds and detect distress signs. The system extracts skeletal features, identifies adverse events, and maintains high detection accuracy without being limited by practitioner capacity, enabling scalable monitoring of any number of subjects.
Solution Approach 2:
The patent creates a universal automated monitoring system that can handle any number of subjects with consistent detection accuracy. The machine learning models are designed to process video feeds from multiple subjects simultaneously, providing scalable functionality that does not degrade with increased subject count, unlike human monitoring capacity.
Data Source
AI summary
Various embodiments of a system and associated method for detection of subject activity by processing video and other signals using artificial intelligence are shown herein. In particular, a subject monitoring system is shown that monitors subjects using a video-capable camera or other suitable video capture device to identify a subject's status of an individual by monitoring subject actions in real-time. The system further monitors other persons in the room with a subject to identify their actions and identities to ensure safety of the subject and facility while preventing confusion of the system as multiple individuals step in and out of frame over the course of the collected video feed.