Sensor-Based Process Auditing for Real-Time Task Compliance
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Solution Overview
Problem
Human error in performing tasks according to established procedures leads to incomplete or improper execution, resulting in potential real-world consequences, such as exposure to harmful conditions in hospital rooms or vehicle malfunctions, due to the lack of effective auditing methods.
Innovation Solution
Utilizing machine learning models trained with sensor data to monitor and provide real-time feedback on task performance, ensuring adherence to established procedures through supervised, unsupervised, or reinforcement learning, and incorporating multiple sensor types for accurate monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If human staff perform cleaning tasks manually, then operational flexibility and adaptability are maintained, but task accuracy and compliance with procedures deteriorate due to human error
Solution Approach 1:
The patent introduces an auditing system comprising sensors, processors, and machine learning models as an intermediary between the cleaning staff and the cleaning tasks. This intermediary automatically monitors and verifies procedure compliance, eliminating the need for manual self-auditing while preserving operational flexibility. The system captures sensor data from the environment and cleaning tools, processes it through ML models trained on correct procedure patterns, and provides real-time feedback without restricting staff autonomy.
Solution Approach 2:
The system implements continuous feedback loops where sensor data from cleaning activities is immediately processed by machine learning models that compare actual performance against trained procedures. Real-time feedback is provided to cleaning staff through the system interface, enabling immediate correction of deviations. Historical feedback data is used to continuously retrain and improve the ML models, creating a self-enhancing system that maintains high task accuracy while allowing operational flexibility.
2Reliability
If auditing systems are implemented to monitor task performance, then task accuracy and procedure compliance are improved, but device complexity and implementation costs increase
Solution Approach 1:
The auditing system is designed with multi-functionality to reduce overall complexity. A single integrated platform performs multiple functions: sensor data acquisition, real-time processing, machine learning model execution, feedback generation, and historical data storage. The machine learning models serve multiple purposes including procedure verification, anomaly detection, and continuous system improvement through retraining. This universal approach consolidates what could be multiple separate complex systems into one cohesive solution.
Solution Approach 2:
The system incorporates self-service capabilities where the machine learning models automatically retrain using historical sensor data and verified procedure outcomes without requiring manual intervention. The system self-calibrates and improves over time, reducing the need for complex manual configuration and maintenance. This self-enhancing mechanism simplifies long-term system management while maintaining high procedure compliance monitoring capabilities.
3Measurement precision
If comprehensive sensor monitoring is deployed to track all cleaning activities, then measurement precision and detection accuracy are improved, but loss of information and data processing burden increase
Solution Approach 1:
The system extracts and focuses only on the most relevant sensor data features needed for procedure verification. Rather than processing all raw sensor data equally, the machine learning models are trained to identify and extract specific features that indicate compliance with cleaning procedures. This selective extraction reduces the data processing burden while maintaining high measurement precision for critical compliance indicators.
Solution Approach 2:
The auditing system segments the cleaning process into distinct procedural steps, with dedicated machine learning models or model components for each step. This segmentation allows the system to process and analyze data for each specific task element separately, reducing overall computational complexity. Each segmented model focuses on specific compliance criteria, making data processing more efficient while maintaining comprehensive monitoring accuracy across all cleaning activities.
Data Source
AI summary
Data associated with one or more workers performing a task is obtained by one or more sensors. One or more machine learning models trained to determine whether the one or more workers correctly performed the task based on the data associated with the one or more workers performing the task are utilized. A notification indicating whether the one or more workers correctly performed the task is outputted.