Elevator Cab Sensor Fusion for Trapped Passenger Detection
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Solution Overview
Problem
Existing elevator systems lack accurate methods to detect trapped passengers and often result in false alarms, and conventional methods fail to distinguish between living beings and inanimate objects, leading to inadequate assistance and potential risks for trapped individuals.
Innovation Solution
A system incorporating jerk detection sensors, microphones, and breath detection sensors, coupled with a central server and machine learning algorithms, to prognostically identify elevator malfunctions and trapped passengers, providing quick assistance and accurate detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If weight sensors are used to determine passenger presence, then the system can detect if someone is in the elevator, but it cannot distinguish between a living breathing human being and an inanimate object such as a trolley or luggage
Solution Approach 1:
The detection system is segmented into multiple independent sensor components: weight sensors for mass detection, microphones for sound detection, and breath detection sensors for respiratory analysis. Each sensor segment performs a specific function, and their combined results enable accurate distinction between living passengers and inanimate objects.
Solution Approach 2:
Multiple sensing modalities (weight, sound, breath detection) are merged into a unified detection system. The controller integrates data from all sensors to comprehensively determine passenger presence and status, overcoming the limitations of any single sensor type.
2Ease of operation
If panic buttons are used for alarm signaling, then passengers can request help, but the system produces many false alarms due to inadvertent pushing or unknowing children
Solution Approach 1:
The system continuously monitors multiple parameters (weight, sound patterns, breath detection) and uses this feedback to dynamically assess whether an alarm condition is genuine. The controller analyzes the combination of sensor inputs over time to distinguish true emergencies from false alarm scenarios, reducing unreliable panic button activations.
Solution Approach 2:
The system performs preliminary detection and analysis using multiple sensors before triggering an alarm response. By预先 analyzing sensor data patterns and confirming genuine distress conditions before activation, the system prevents premature or false alarm responses while maintaining ease of legitimate emergency activation.
3Measurement precision
If multiple sensors and machine learning algorithms are integrated for accurate trapped passenger detection, then false alarms are reduced, but the device complexity increases
Solution Approach 1:
The controller serves multiple functions: it processes data from weight sensors, microphones, and breath detection sensors; runs machine learning algorithms for pattern recognition; and coordinates alarm signaling. This multi-functional design consolidates complexity into a single central processing unit rather than requiring separate dedicated systems for each function.
Solution Approach 2:
The machine learning algorithms automatically learn and adapt to distinguish genuine trapped passenger scenarios from false alarm conditions without requiring manual programming of every possible scenario. The system self-adjusts its detection parameters based on learned patterns, reducing the need for complex manual configuration and maintenance.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system effectively detects trapped passengers and elevator malfunctions, ensuring rapid response and rescue, reducing false alarms, and enhancing passenger safety by integrating multiple sensors and centralized monitoring.
Implementation Method 1
The elevator cab (102) includes a jerk detection sensor (106) that detects a jerk in the elevator cab
Implementation Method 2
a microphone (107) that detects a sound generated by a trapped passenger
Implementation Method 3
a breath detection sensor (109) that detects a breath of the trapped passenger
Data Source
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AI summary
A system and method for detection of a malfunction in an elevator. The system includes an elevator cab, a server, at least one sensor, and a controller. The controller receives a signal captured by the at least sensor and transmit to the server. The server processes the transmitted signal and prognostically detects the malfunction in the elevator cab.