Building Wellness Index Using Sensors and ML Risk Assessment
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Solution Overview
Problem
Current systems lack an efficient and automated method for managing building wellness, particularly in the context of COVID-19, as they rely on manual data collection and analysis, which is inadequate for quickly identifying and addressing health risks such as pathogen exposure in office environments.
Innovation Solution
A computer-implemented system that collects and processes wellness-related data from various sources, including sensors and occupant reports, to calculate a building wellness index, providing an indication of pathogen risk and facilitating corrective actions through automated data aggregation and machine learning models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual data collection and analysis methods are used, then system complexity is reduced, but data processing accuracy and speed deteriorate
Solution Approach 1:
The patent replaces manual mechanical data collection and analysis with automated electronic sensors, processors, and machine learning algorithms. Sensors automatically collect wellness data from the building environment, and computer processors analyze this data to generate wellness indices, eliminating the need for manual monitoring while significantly improving accuracy and speed.
Solution Approach 2:
The system implements self-service through automated data collection sensors that continuously monitor building conditions without human intervention. The machine learning models automatically process and interpret the collected data, generating wellness assessments and alerts autonomously, allowing the system to serve itself in data collection and analysis tasks.
2Productivity
If automated data collection and machine learning models are implemented, then productivity is improved, but device complexity increases
Solution Approach 1:
The patent segments the automated system into distinct functional modules: sensor arrays for data collection, processors for data analysis, machine learning models for pattern recognition, and communication systems for alert generation. This modular segmentation allows each component to perform its specific function efficiently while maintaining overall system productivity, and makes the complex system more manageable through clear functional divisions.
3Reliability
If comprehensive wellness parameters are monitored, then reliability of wellness assessment is improved, but loss of information increases
Solution Approach 1:
The patent extracts only the most critical wellness parameters from the comprehensive data set for detailed analysis and reporting. The machine learning models identify and prioritize key indicators such as temperature anomalies, humidity levels, and occupancy patterns that most directly impact wellness, extracting these specific data points for focused monitoring while reducing the overall data management burden.
Solution Approach 2:
The system transforms comprehensive raw wellness data into simplified wellness index scores and categorical assessments. By changing the parameter representation from multiple detailed measurements to aggregated index values, the system maintains reliable wellness assessment while reducing information complexity and making the data more actionable for building managers and occupants.
Data Source
AI summary
A system and a computer-implemented method of managing building wellness. The method may include the steps of: obtaining wellness parameters for a building (e.g., an office building) having an occupant(s); processing the wellness parameters to determine a current wellness index for the building; and, based on the current wellness index, sending a message regarding the current wellness index to a recipient(s) (e.g., a building occupant), displaying the current wellness index for a user(s), and/or identifying a remediation action(s) to improve the current wellness index.