Proximity Sensor Filtering for Medical Imaging Collision Avoidance
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Solution Overview
Problem
Current anti-collision systems in medical imaging equipment, such as C-arm X-ray imagers, often produce unacceptable numbers of false positive or false negative results, leading to inefficient use or safety hazards due to their inability to accurately detect object proximity and differentiate between non-contact and contact events.
Innovation Solution
An object approach detection apparatus that utilizes a filter module to suppress frequencies caused by relative motion, allowing for the differentiation between proximity events and their persistence, and adapts base-values dynamically to improve detection accuracy, thereby reducing false positives and enabling efficient collision avoidance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If proximity sensors are used to detect object proximity in anti-collision systems, then collision avoidance capability is improved, but false positive and false negative results increase due to inability to differentiate between non-contact proximity and contact events
Solution Approach 1:
The system dynamically adapts the base-value over time based on detected proximity events. The base-value is continuously updated to reflect current environmental conditions, allowing the system to distinguish between transient proximity (false positives) and persistent contact (true positives). This dynamic adaptation resolves the contradiction by making the detection system both sensitive to real contacts and robust against false alarms.
Solution Approach 2:
The system performs preliminary detection of proximity events before confirming contact. By detecting proximity events first and then monitoring their persistence over time, the system can differentiate between non-contact proximity and actual contact. This preliminary action approach reduces false positives while maintaining detection of true contact events.
2Reliability
If the system is overly cautious to avoid false negatives, then safety is improved, but productivity decreases due to frequent unnecessary collision avoidance actions
Solution Approach 1:
The system performs preliminary detection of proximity events before triggering collision avoidance actions. By monitoring the persistence and characteristics of detected events, the system can distinguish between genuine contact threats requiring action and transient proximity events that do not require intervention. This reduces unnecessary productivity losses while maintaining safety.
Solution Approach 2:
The system uses feedback from detected proximity events to continuously refine its base-value and detection thresholds. This feedback mechanism allows the system to learn from past events and improve its discrimination between false positives and true contacts, thereby reducing unnecessary collision avoidance actions that hinder productivity while maintaining safety.
3Device complexity
If the system uses fixed base-value for proximity detection, then device complexity is reduced, but adaptability to environmental conditions and false positive reduction is worsened
Solution Approach 1:
The base-value is implemented as a dynamic parameter that automatically adapts to changing environmental conditions. The system continuously updates the base-value based on detected proximity events, eliminating the need for manual calibration or complex environmental sensing while maintaining high adaptability. This dynamic approach resolves the contradiction by providing both simplicity and adaptability.
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 reduces collision risks by enhancing the accuracy of object detection, differentiating between non-contact proximity and physical touch, and improving robustness against environmental conditions, thus ensuring safer and more efficient operation of medical imaging equipment.
Implementation Method 1
the sensor being a capacitance sensor arranged to provide a capacitance signal in response to an object approach
Data Source
AI summary
An object approach detection apparatus (ACU) includes an input interface (IN) for receiving a response signal from a proximity sensor (PSj) measured relative to a first base-value of said at least one sensor. A filter module (FM) is configured to filter said response signal to produce a filtered response signal. A proximity event declarator (PED) is configured to declare a proximity event has occurred if the filtered response signal fulfils a first condition, in particular crosses a first threshold. A base value adaptor (BVA) is configured to choose a new base value in response of the declaring that the proximity event has occurred.