Presence Signal Interpretation Using Historical Occupancy Patterns
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Solution Overview
Problem
Monitoring systems face challenges in accurately determining user presence at home based on presence signals, as they struggle to differentiate between users leaving or staying at home, leading to potential false alerts and inefficient resource management.
Innovation Solution
The implementation of a monitoring system that interprets presence signals using historical data patterns, analyzing sensor data from various sources to determine user presence and absence, and performing actions accordingly, such as sending alerts or controlling HVAC systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the monitoring system uses basic presence signals to determine user presence, then the system operation is simple, but the accuracy of user presence detection is low
Solution Approach 1:
The system performs preliminary actions by collecting and storing historical presence signal data before making presence determination decisions. The monitoring server accumulates presence signals over time periods and analyzes patterns beforehand, so when a presence determination is needed, it can reference pre-analyzed historical data rather than making immediate judgments based on single signals.
Solution Approach 2:
The system implements feedback by continuously monitoring presence signals and comparing current signals against historical patterns. The monitoring server analyzes whether current presence signals match established patterns from historical data, and uses this feedback loop to improve the accuracy of presence determinations over time while maintaining systematic operation.
2Reliability
If the monitoring system sends alerts for every presence signal change, then the system responds quickly to all events, but the number of false alerts increases
Solution Approach 1:
The system applies partial action by sending alerts only for certain presence signal changes rather than all changes. The monitoring server evaluates each presence signal against historical patterns and determines whether alerting is necessary, thereby reducing the number of false alerts while still maintaining timely response to genuine presence changes that warrant notification.
Solution Approach 2:
The system uses feedback by analyzing current presence signals against historical patterns before generating alerts. The monitoring server continuously compares signal patterns and only triggers alerts when the analysis indicates a genuine presence change, thereby improving alert reliability while maintaining efficient system response to meaningful events.
3Loss of energy
If the monitoring system controls HVAC systems based on every presence signal, then the system optimizes resource usage continuously, but the system complexity and energy consumption increase
Solution Approach 1:
The system performs preliminary analysis by establishing historical presence patterns before implementing HVAC control. The monitoring server analyzes historical presence signal data to understand typical occupancy patterns, then uses this pre-analyzed information to guide HVAC control decisions, avoiding the need for complex real-time analysis while optimizing energy usage based on learned patterns.
Solution Approach 2:
The system implements periodic action by controlling HVAC systems based on periodic analysis of presence patterns rather than continuous control. The monitoring server evaluates presence signals at intervals and adjusts HVAC operations periodically based on whether presence patterns indicate occupancy, thereby reducing energy waste while maintaining manageable system complexity through scheduled rather than continuous control.
Data Source
AI summary
A method includes obtaining historical event data for events detected over a past period of time by sensors within a property, receiving a set of current event data for one or more events detected by one or more of the sensors within the property, determining that the set of current event data matches a pattern of events indicated by the historical event data, generating, based on the pattern of events, a confidence score for the set of current event data, wherein the confidence scores reflects a confidence that a person is not within the property, determining that the confidence score satisfies a confidence threshold associated with an action to be performed when a person is not within the property, and triggering execution of the action.