Smart Home Sensor Fusion for Peril Detection in Assisted Living
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Solution Overview
Problem
Existing systems fail to effectively detect individuals in peril within independent or assisted living environments and timely notify appropriate individuals or authorities, relying on self-reporting or caregiver presence, which often results in unaddressed situations due to lack of constant monitoring.
Innovation Solution
A computer-implemented method and hardware controller system that analyzes sensor data from connected devices to detect perilous situations, generating real-time notifications to relevant individuals and facilitating mitigating actions, utilizing a central controller connected to various smart devices and sensors within the environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If self-reporting or caregiver presence is used to detect peril, then device complexity is reduced, but detection reliability and response time deteriorate
Solution Approach 1:
The system divides the monitoring function into multiple independent sensor components (motion sensors, vibration sensors, audio sensors, environmental sensors) distributed throughout the living environment. Each sensor independently monitors specific parameters, and their combined data provides comprehensive peril detection capability without requiring a single complex centralized system.
Solution Approach 2:
The sensor network is designed to perform multiple functions: detecting falls, monitoring environmental conditions, tracking movement patterns, and identifying various perilous situations. This multi-functional approach eliminates the need for separate specialized devices for each monitoring task, reducing overall system complexity while maintaining high detection reliability.
2Reliability
If constant monitoring with multiple sensors is implemented, then peril detection reliability improves, but device complexity and energy consumption increase
Solution Approach 1:
The system establishes baseline behavioral patterns and environmental conditions during normal operation phases. By pre-learning what constitutes normal activity for each resident, the system can quickly identify deviations indicating peril without requiring complex real-time analysis of every sensor input, thereby reducing processing complexity while maintaining detection reliability.
Solution Approach 2:
The system employs full sensor monitoring only when anomalies are detected or during critical periods, otherwise using reduced monitoring modes. This partial action approach maintains high detection reliability when needed while minimizing energy consumption and system complexity during normal operation.
3Loss of time
If real-time sensor data analysis is performed, then response time to peril situations improves, but energy consumption and processing requirements increase
Solution Approach 1:
The system implements periodic sampling of sensor data at optimized intervals rather than continuous monitoring. During stable conditions, sensors sample at lower frequencies, reducing energy consumption. When anomalies are detected or during high-risk periods, sampling frequency increases automatically to maintain rapid response capability without excessive energy use during normal operation.
Solution Approach 2:
The system automatically adjusts its monitoring intensity and energy consumption based on detected conditions without external intervention. It self-regulates by increasing power usage only when peril is detected or during critical monitoring phases, and reducing consumption during stable periods, thereby optimizing the balance between response time and energy efficiency.
Data Source
AI summary
The present embodiments relate to detecting instances of individuals being in peril within an independent or assisted living environment. According to certain aspects, with an individual's permission or affirmative consent, a hardware controller (such as a smart or interconnected home controller, or even a mobile device) may receive and analyze sensor data detected within the independent or assisted living environment to determine whether an individual may be in peril. In this circumstance, the hardware controller may generate a notification that indicates the situation and may communicate the notification to a proper individual, such as a family member or care giver, who may be in a position to mitigate or alleviate any risks posed by the situation. The foregoing functionality also may be used by an insurance provider to generate, update, or adjust insurance policies, premiums, rates, or discounts, and/or make recommendations to an insured individual.