3D Keepout Zone Detection for Power Tool Appendage Safety
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Solution Overview
Problem
Conventional safety systems fail to effectively monitor and prevent human contact with hazardous areas, such as power tools and chemical baths, leading to accidental injuries, as they do not discriminate between human appendages and other occlusions and require permanent installation.
Innovation Solution
The use of sensors and a pre-trained machine learning model to dynamically or statically define a 3D keepout zone around hazardous areas, processing imagery to detect human appendages and triggering safety events, such as power interruption or warnings, to prevent accidental contact.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional optical breakscreens are used to guard hazardous areas, then safety monitoring is provided, but the system cannot discriminate between human appendages and other occlusions, leading to false alarms and restricted workflow
Solution Approach 1:
The patent replaces conventional optical breakscreens with a machine learning-based image processing system that uses sensors (cameras, depth sensors, thermal sensors) to capture and analyze visual data. The system substitutes mechanical/optical barrier detection with intelligent image recognition that can distinguish human appendages from other objects, eliminating false alarms while maintaining safety monitoring.
Solution Approach 2:
The system changes the detection parameters from simple optical beam interruption to multi-parameter image analysis including depth information, thermal signatures, and visual features. By processing multiple parameters simultaneously through machine learning models, the system achieves accurate discrimination between human appendages and other occlusions, improving reliability without restricting workflow.
2Reliability
If permanent optical breakscreens are installed to monitor keepout zones, then safety coverage is provided, but installation complexity and calibration requirements increase
Solution Approach 1:
The system employs self-calibration capabilities where the machine learning model automatically adapts to the specific environment and hazardous area geometry during initial operation. The sensors automatically map the keepout zone boundaries and the system learns to recognize relevant safety features without requiring manual calibration, significantly reducing installation complexity while maintaining comprehensive safety coverage.
Solution Approach 2:
The system transitions from static, permanently installed optical breakscreens to dynamic sensor arrays that can be repositioned and reconfigured as needed. The machine learning model dynamically adjusts detection parameters and keepout zone boundaries based on real-time conditions, providing flexible safety coverage without permanent installation requirements.
3Reliability
If keepout zones are monitored to prevent human contact with hazardous areas, then safety is improved, but false detection of non-human objects as human appendages may occur
Solution Approach 1:
The system segments the detection task into multiple specialized analysis streams: visual feature extraction, depth analysis, thermal pattern recognition, and motion detection. Each sensor type processes specific aspects of the target object, and the machine learning model integrates these segmented analyses to make comprehensive determination. This segmentation allows the system to cross-validate detections and eliminate false alarms caused by non-human objects.
Solution Approach 2:
The machine learning model serves as an intermediary layer between raw sensor data and safety determination. Instead of directly interpreting sensor signals, the model processes intermediate features extracted from multiple sensor sources, comparing them against learned patterns of human appendages. This intermediary processing stage filters out false detections while maintaining sensitivity to genuine safety concerns.
Data Source
AI summary
A keepout zone detection system and method for a hazardous area, such as the workspace around a power tool, that includes a safety device that controls a safety event, such as disabling the power tool, and a keepout zone detection device that includes at least one imager for imaging a workspace associated with the power tool. A processor dynamically determines the keepout zone specific to the power tool based on information received from the imager(s) or by referring to a predetermined keepout zone stored in a memory, where the processor includes a pre-trained neural network that processes the received images to further identify the presence of human appendage(s) within the keepout zone; and a communication device that communicates with the safety device to control the safety event, such as disabling the power tool, based on the determination of the presence of the human appendage within the keepout zone.