Intelligent Escalator Emergency Stop System Using AI Detection
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Solution Overview
Problem
Existing escalator emergency stop systems are ineffective due to reliance on human reaction time, awareness, and proximity, leading to frequent accidents resulting in injuries and fatalities, as they require a person to manually activate the stop button, which is often inactive or misused, and lacks immediate response capabilities.
Innovation Solution
The Intelligent Escalator Emergency Stop System (IE2S2) uses sensors and artificial intelligence to continuously monitor escalator users, detecting accidents such as imbalance or falling, and automatically or manually stopping the escalator to prevent injuries, with features like automatic restart options and differentiation between human and objects to prevent false alarms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual emergency stop button is installed on escalator, then safety mechanism is provided, but response time is delayed due to human reaction time and awareness requirements
Solution Approach 1:
The patent replaces the manual mechanical emergency stop button system with an automatic sensor-based detection system. Optical sensors, infrared sensors, or cameras continuously monitor the escalator for accidents, automatically triggering the stop mechanism without requiring human intervention. This substitution eliminates the delay caused by human reaction time and awareness limitations.
Solution Approach 2:
The system enables the escalator to monitor itself for accidents and automatically initiate emergency stopping. The detection system continuously analyzes sensor data to identify accidents such as falls, objects on steps, or unusual movements, and autonomously activates the brake mechanism when accidents are detected, making the system self-protecting without external human action.
2Speed
If emergency stop button is made active and responsive, then accident detection speed increases, but false alarms increase due to inability to distinguish accidents from normal usage
Solution Approach 1:
The system uses feedback from multiple sensors to continuously monitor escalator operation and distinguish between normal usage patterns and actual accidents. The detection algorithm analyzes sensor data in real-time, comparing current readings against established normal operation profiles to determine whether an emergency stop is warranted, thereby reducing false alarms while maintaining rapid response to genuine accidents.
Solution Approach 2:
The patent employs multi-functional detection capabilities using sensors that can detect various types of accidents and normal conditions. Optical sensors, infrared sensors, and cameras work together to monitor multiple parameters simultaneously, enabling the system to differentiate between diverse scenarios such as passengers boarding, objects left on steps, falls, and other emergencies, thereby reducing false alarms while maintaining high detection speed.
3Measurement precision
If multiple sensors and AI algorithms are added to the system, then detection accuracy and safety improve, but system complexity and cost increase
Solution Approach 1:
The patent divides the detection system into modular sensor units positioned at different locations along the escalator. Each sensor module independently monitors specific zones and can be processed separately by distributed computing units. This segmentation allows high detection accuracy through multiple monitoring points while managing complexity through modular architecture that can be configured and maintained independently.
Data Source
AI summary
The present invention constantly observes the balancing condition of users; at the first few signs of an occurrence of an accident such as falling, slipping, sitting on the escalator the system immediately initiates an emergency stop command.