Motion Sensor Analysis for Intentional Sensor Removal Detection
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Solution Overview
Problem
Current monitoring systems generate irrelevant alarms due to intentional sensor removal, leading to alarm fatigue among medical professionals, as they cannot distinguish between intentional and unintentional sensor disconnection, especially in ICU environments where SpO2 and ECG measurements are common.
Innovation Solution
A system comprising a physiological sensor and a motion sensor that analyzes movement data prior to sensor disconnection to differentiate between intentional and unintentional removals, modifying the alarm protocol by delaying, changing the sound, or altering the priority based on detected patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sensor removal alarms are generated for all disconnections, then patient safety monitoring is maintained, but alarm fatigue occurs due to irrelevant intentional removal alarms
Solution Approach 1:
The system performs preliminary action by analyzing motion sensor data before generating an alarm to determine whether sensor removal is intentional or accidental. Motion patterns are evaluated in advance to classify the removal type, allowing the system to suppress alarms for intentional removals while maintaining alarms for accidental ones, thus preventing alarm fatigue while preserving patient safety monitoring.
2Object-generated harmful factors
If motion sensor analysis is performed to classify sensor removal type, then irrelevant alarms are reduced, but device complexity increases
Solution Approach 1:
The motion sensor serves as an intermediary that provides additional data to help classify sensor removal events. By incorporating motion sensor analysis as a mediator between sensor disconnection detection and alarm generation, the system can distinguish between intentional and accidental removals without requiring complex AI or machine learning algorithms, thus reducing irrelevant alarms while keeping device complexity manageable.
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 irrelevant alarms by accurately classifying the reason for sensor disconnection, allowing for more appropriate responses to unintentional removals and minimizing alarm fatigue among medical professionals.
Implementation Method 1
a motion sensor, such as an accelerometer, connected to a patient
Data Source
AI summary
The system and method of the present application includes a physiological sensor and a motion sensor connected to a patient. The physiological sensor detects patient connection to the sensor and collects a physiological signal while connected. When the physiological sensor is disconnected, the motion sensor data is analyzed. Patterns of sensor connection and patient movement typical for nurse initiated removal compared to accidental or patient initiated removals are created. The alarm protocol may be modified if the disconnection is due to patient movement. The detected movement patterns may include movement measurements that are close to the sensor that detects how the actual disconnection happens, or general movement information for the patient such as whether the patient has been still or has moved before the sensor gets disconnected. By using this information to classify the reason of the sensor removal, a more relevant alarm may be generated.