Touchless Lavatory Sensor Fusion for Inadvertent Activation Control
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Solution Overview
Problem
Inadvertent triggering of sensors in touchless lavatories leads to resource wastage and user discomfort, particularly in compact spaces like airplanes.
Innovation Solution
A system utilizing ambient light and motion sensors, combined with machine learning models, to accurately determine user activities and control motor operations in touchless lavatories.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If sensors are used to control lavatory operations, then automation and convenience are improved, but inadvertent triggering increases causing resource waste and user discomfort
Solution Approach 1:
The system segments the sensor control function into multiple independent components: ambient light sensor, motion sensor, and machine learning classification models. Each sensor operates independently and feeds data to the ML model, which then makes the final activation decision. This segmentation allows the system to process multiple data sources and reduce false activations while maintaining automation.
Solution Approach 2:
The system implements feedback by continuously monitoring sensor data and using machine learning models to predict user intent based on aggregated sensor patterns. The ML model learns from sensor feedback and adjusts its predictions to distinguish between intentional user actions and inadvertent triggers, thereby improving reliability while maintaining automation.
2Adaptability or versatility
If multiple sensors are deployed in compact spaces, then functionality is improved, but the risk of inadvertent triggering increases
Solution Approach 1:
The system merges data from multiple sensors (ambient light sensor and motion sensor) into a unified analysis framework using machine learning. Instead of having each sensor operate independently with separate activation thresholds, the ML model combines sensor inputs to make a single integrated decision about user intent. This merging reduces inadvertent triggering while preserving the functionality of multiple sensors in compact spaces.
Solution Approach 2:
The machine learning classification model serves as a universal decision-making component that handles multiple sensor types and multiple classification tasks (occupancy status, sitting status, gesture status). This universal approach allows the system to maintain versatile functionality with multiple sensors while using a single intelligent framework to prevent inadvertent triggering across all sensor inputs.
3Speed
If sensor data is processed in real-time with short time windows, then responsiveness is improved, but measurement accuracy decreases
Solution Approach 1:
The system uses periodic action by processing sensor data in discrete time windows (e.g., 0.05-0.5 seconds) rather than continuous processing. The ML model evaluates sensor data at regular intervals, which provides sufficient responsiveness for touchless lavatory operations while allowing enough time aggregation to improve measurement precision and reduce noise from individual sensor readings.
Data Source
AI summary
A method for controlling a touchless lavatory is disclosed herein. The method includes collecting, by a processor, sensor data from a sensor, the sensor data including an ambient light measurement and an indication of motion, aggregating, by the processor, the collected sensor data for a time window, extracting, by the processor, features from the aggregated sensor data, and determining, by the processor, a probability of an activity occurring based on the extracted features. The method further includes activating, by the processor, a motor based on the determining that the activity has a high probability of occurring.