Multi-Sensor Fall Prediction System for Worker Safety
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Solution Overview
Problem
Existing fall protection systems for workers at heights often allow a short distance of fall before intervention and fail to notify other workers of the incident, lacking real-time monitoring and proactive safety measures.
Innovation Solution
A system utilizing multiple movement sensors associated with a worker's body to detect and predict slips and falls by comparing sensor readings with stored patterns, generating warnings, and activating safety measures such as automatic restraint systems, and providing real-time monitoring and alerts to supervisors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If passive safety systems such as restraint systems based on fall velocity are used, then worker injury can be prevented, but the worker still falls a short distance prior to stopping
Solution Approach 1:
The system performs preliminary action by detecting movement patterns that precede a fall and triggering the restraint system before the fall occurs. Multiple sensors detect anomalies in worker movement patterns, and the system activates the restraint mechanism in advance, preventing the fall entirely rather than stopping it after initiation.
Solution Approach 2:
The system implements feedback by continuously monitoring worker movement patterns through multiple sensors and adjusting the restraint system activation based on detected anomalies. The system provides real-time feedback on worker status and automatically activates protection when fall patterns are detected, creating a closed-loop safety system.
2Reliability
If passive safety systems are used, then worker protection is provided, but other workers are not notified of the fall situation
Solution Approach 1:
The system implements feedback by continuously monitoring worker movement patterns through multiple sensors and automatically notifying supervisors and other workers when fall patterns are detected. The notification system provides real-time information about fall incidents to relevant personnel, ensuring immediate awareness and response.
Solution Approach 2:
The system uses an intermediary notification mechanism that automatically communicates fall incident information between the detection system and supervisory personnel. This intermediary layer ensures that information about fall situations is reliably transmitted to appropriate workers without requiring direct human observation.
3Measurement precision
If multiple sensors are used to detect movement patterns, then fall prediction accuracy is improved, but system complexity increases
Solution Approach 1:
The system applies segmentation by dividing the monitoring function across multiple sensors placed at different locations on the worker's body. Each sensor independently monitors specific movement parameters, and the system integrates these segmented measurements to achieve comprehensive fall detection with high accuracy while maintaining manageable system complexity through modular architecture.
Data Source
AI summary
A method of determining the likelihood of a slip or fall comprises receiving, by a detection application stored in a non-transitory memory and executed on a processor, a plurality of sensor readings from a plurality of movement sensors, comparing, by the detection application, the plurality of sensor readings with a plurality of movement patterns stored in a database, determining, by the detection application, that at least one of the movement patterns of the plurality of movement patterns matches the plurality of sensor readings, and generating, by the detection application, an indication that a slip or fall is likely based on the plurality of sensor readings. The plurality of movement sensors is associated with different areas of a worker's body.