Partiality Vector Deviation Detection in Home Monitoring
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Solution Overview
Problem
Current monitoring systems fail to effectively detect deviations in a person's routine activities, which can indicate changes or problems in their life, as they lack the capability to continuously and accurately track and analyze daily patterns and behaviors.
Innovation Solution
A system comprising sensors and a control circuit that monitor parameters associated with a person and their home, generating partiality vectors to detect deviations from routine behaviors, and sending alerts to appropriate recipients when deviations are detected.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If continuous monitoring of daily activities is implemented, then detection capability of routine deviations is improved, but device complexity increases
Solution Approach 1:
The system segments monitoring into discrete routine definitions (sequences of activities at locations) and deviation detection components. Each routine is broken down into individual activities and locations that can be independently tracked and analyzed, allowing complex monitoring to be managed through modular components rather than a monolithic system.
Solution Approach 2:
The control circuit acts as an intermediary that receives data from multiple sensors, processes it through routine comparison logic, and generates alerts. This intermediary layer simplifies the system architecture by centralizing the complex deviation detection logic in a dedicated component rather than distributing complexity across all sensing elements.
2Reliability
If multiple sensors monitor parameters continuously, then reliability of deviation detection is improved, but use of energy increases
Solution Approach 1:
The system employs periodic monitoring where sensors continuously collect data but the control circuit evaluates routines at defined intervals by comparing current activity sequences against stored routine patterns. This periodic evaluation approach maintains reliable detection capability while reducing continuous processing energy consumption compared to real-time analysis of every sensor input.
Solution Approach 2:
The system uses the person's own routine patterns as the reference standard for deviation detection. By learning and storing individual routine sequences from initial monitoring periods, the system enables self-powered deviation detection where the monitored subject's behavior itself provides the benchmark, eliminating need for external reference data or manual intervention.
Data Source
AI summary
In some embodiments, apparatuses, systems, and methods are provided herein useful to detecting a deviation in a person's activity. In some embodiments, an apparatus comprises one or more sensors, the one or more sensors configured to monitor parameters associated with a person and the person's home, and a control circuit, the control circuit communicatively coupled to the one or more sensors and configured to generate one or more partiality vectors for the person, receive, from the one or more sensors, values associated with the parameters, create, based on the values associated with the parameters, a spectral profile for the person, determine, based on the spectral profile and a routine base state for the person, that a combination of the values indicates a deviation, and update at least one of the one or more partiality vectors for the person.