Powered Saw Hand Detection Using Camera-Defined Keep-Out Zones
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Solution Overview
Problem
Powered saw tools pose a risk of injury when the saw blade comes into contact with a user's hand, as existing technologies lack effective methods to detect and prevent such incidents.
Innovation Solution
A powered saw equipped with a camera, inertial measurement unit (IMU) sensor, and an electronic controller using machine learning (ML) models to analyze images and detect hands within defined keep-out areas, triggering safety actions when a hand is detected.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a camera and ML model are added to detect hands, then safety is improved, but device complexity increases
Solution Approach 1:
A camera system serves as an intermediary between the user's hand and the control system. The camera captures images of the work area, and an ML model processes these images to detect hands in the keep-out area. This intermediary system enables safety monitoring without requiring direct physical contact or complex mechanical safety devices, thus improving safety while managing device complexity through software-based detection.
2Measurement precision
If real-time image analysis is performed to detect hands, then detection precision is improved, but processing time increases
Solution Approach 1:
The ML model is pre-trained on a dataset of images containing hands in various positions, lighting conditions, and angles. This preliminary training enables the model to rapidly classify new images during operation. By performing the computationally intensive learning phase beforehand, the system achieves high detection precision during real-time operation with minimal processing delay, as the model only needs to infer from pre-learned patterns rather than learn during operation.
Data Source
AI summary
A powered saw includes at least one camera, a sensor, a saw blade, a motor configured to drive the saw blade, and an electronic controller including an electronic processor and a memory. The electronic controller receives an indication of an orientation of the saw blade from the sensor and determines a keep-out area based on the orientation of the saw blade, where the keep-out area corresponds to, or is defined relative to, the saw blade. Images captured from the at least one camera are received by the electronic controller, where the captured images include at least a portion of the keep-out area. The electronic controller analyzes, using a machine learning (ML) model, the captured images to determine whether a portion of a hand is present in the keep-out area and, in response to detecting the portion of the hand in the keep-out area, executes a safety action.