Home Sensor Monitoring for Early Condition Detection
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Solution Overview
Problem
Individuals with cognitive or health conditions may develop subtle signs that make it difficult for caregivers to determine their ability to live independently, as these conditions often develop slowly over time.
Innovation Solution
A system utilizing sensors in a home environment to capture data, analyze it for abnormalities or anomalies, and generate notifications to caregivers about potential conditions, potentially using machine learning to identify patterns and predict future conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple sensors are deployed to detect subtle signs of cognitive or health conditions, then the measurement precision and reliability of condition identification is improved, but the device complexity and cost increase
Solution Approach 1:
The system divides the monitoring function into multiple specialized sensors (motion sensors, temperature sensors, humidity sensors, etc.), each detecting specific environmental parameters. This segmentation allows the system to achieve high measurement precision through specialized detection while managing complexity by assigning specific detection tasks to individual sensor components rather than requiring a single complex sensor system.
Solution Approach 2:
The patent combines data from multiple sensors (motion, temperature, humidity) and integrates it with electronic health record data through a centralized processing system. This merging approach enables the system to achieve high condition identification accuracy by synthesizing information from diverse sources, while the modular architecture helps manage the overall system complexity.
2Productivity
If real-time data analysis is performed to identify conditions promptly, then the productivity and responsiveness of the system is improved, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-processing sensor data to identify patterns and establish baselines before actual condition detection is needed. Historical data is analyzed in advance to create reference profiles, enabling faster real-time condition identification without requiring extensive processing time during critical moments.
Solution Approach 2:
The system maintains continuous monitoring and analysis of sensor data, keeping the processing system actively engaged in detecting patterns and anomalies. This continuous action ensures that condition identification is always ready to occur immediately when changes are detected, improving productivity while the automated continuous processing eliminates manual intervention delays.
3Reliability
If comprehensive sensor data is collected to analyze for abnormalities, then the reliability of condition detection is improved, but the quantity of data and storage requirements increase
Solution Approach 1:
The system extracts and isolates specific parameters of interest from the comprehensive sensor data (such as motion patterns, temperature fluctuations, humidity changes) that are most indicative of cognitive or health conditions. By extracting only the relevant features rather than storing and analyzing all raw data, the system maintains high detection reliability while significantly reducing the quantity of data that needs to be stored and processed.
Solution Approach 2:
The patent introduces an intermediary processing layer that aggregates and pre-processes sensor data before it is stored or analyzed further. This intermediary layer filters and summarizes comprehensive sensor information into meaningful patterns and anomalies, reducing the effective data volume while preserving the reliability needed for accurate condition detection.
Data Source
AI summary
Systems and methods for identifying a condition associated with an individual in a home environment are provided. Sensors associated with the home environment detect data, which is captured and analyzed by a local or remote processor to identify the condition. In some instances, the sensors are configured to capture data indicative of electricity use by devices associated with the home environment, including, e.g., which devices are using electricity, what date/time electricity is used by each device, how long each device uses electricity, and/or the power source for the electricity used by each device. The processor analyzes the captured data to identify any abnormalities or anomalies, and, based upon any identified abnormalities or anomalies, the processor determines a condition (e.g., a medical condition) associated with an individual in the home environment. The processor generates and transmits a notification indicating the condition associated with the individual to a caregiver of the individual.