Smart Device Behavior Model for Elderly Safety Monitoring
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Solution Overview
Problem
Elderly individuals often require caretakers as they age, but desire independence; existing technologies lack effective, non-obtrusive methods to monitor and ensure their well-being, particularly in detecting abnormal behaviors or conditions.
Innovation Solution
A system utilizing smart devices to create a behavior model based on user interactions, comparing current device statuses to a base model, and determining abnormal conditions through an artificial neural network, with notifications sent to external devices if thresholds are exceeded.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If caretakers are provided to assist elderly individuals with day-to-day needs, then their safety and well-being are improved, but their independence and self-reliance deteriorate
Solution Approach 1:
The system enables elderly individuals to independently interact with smart devices in their home environment, allowing them to perform daily tasks and monitor their own well-being without constant caretaker assistance. The behavior model automatically analyzes their actions and detects anomalies, providing safety monitoring while preserving their autonomy and self-reliance.
2Reliability
If traditional monitoring methods are used to ensure elderly safety, then their well-being is improved, but their sense of independence and normalcy deteriorates
Solution Approach 1:
The system replaces traditional mechanical monitoring approaches (such as cameras, sensors, or direct human observation) with an intelligent behavior analysis model that processes data from existing smart devices. This substitution enables passive, non-intrusive monitoring that detects abnormal behaviors through pattern recognition rather than direct surveillance, preserving the elderly individual's dignity and sense of normalcy.
Solution Approach 2:
The behavior model acts as an intermediary layer between the elderly individual and the monitoring system. Instead of directly observing or interfering with the individual's actions, the model analyzes data from smart devices indirectly, detecting anomalies through behavioral patterns. This intermediary approach provides safety monitoring while minimizing direct interaction and obtrusiveness.
3Measurement precision
If smart devices are deployed to monitor behavior, then detection capability is improved, but system complexity increases
Solution Approach 1:
The system leverages existing smart devices (light bulbs, outlets, thermostats, doorbells) that elderly individuals already have in their homes for other purposes. These multi-functional devices simultaneously provide their primary functions and generate behavioral data for monitoring. This approach improves detection capability without requiring a separate complex monitoring infrastructure, as the same devices serve multiple purposes.
Solution Approach 2:
The system combines data from multiple existing smart devices into a unified behavior model. Instead of deploying separate monitoring equipment throughout the home, it merges the functionality of already-present smart devices to create a comprehensive monitoring system. This integration approach improves detection precision while avoiding the complexity of a fully new system architecture.
Data Source
AI summary
A system and method for determining abnormal conditions based on signals received from smart devices. The method includes receiving, via a controller and transmitted via one or more devices, one or more first signals indicative of one or more first statuses of the one or more devices. The method includes determining, via the controller and based on the one or more first statuses, a base model. The method includes receiving, via the controller and transmitted via the one or more devices, one or more second signals indicative of one or more second statuses of the one or more devices. The method includes comparing, via the controller, the one or more statuses to the base model and determining, via the controller and based on the comparison, an occurrence of an abnormal condition.