Presence Simulation Control Using Behavioral Profiles for Home Security
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Solution Overview
Problem
Security systems struggle to effectively detect and prevent unwanted intrusions when residents are present, as existing sensors often fail to mimic normal activity patterns.
Innovation Solution
A monitoring and security system that analyzes audio patterns to create a user profile, simulating presence by controlling devices to replicate typical user habits, such as turning on TVs or stereos, when residents are away.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional sensors are used to detect intrusions, then the security system can identify break-ins, but the system fails to detect when no intrusion is occurring (false negatives) and cannot distinguish between normal activity and suspicious behavior
Solution Approach 1:
The system creates a behavioral profile (copy) of the resident's normal activities by monitoring and analyzing their patterns. This profile is then used to compare against current sensor data, allowing the system to distinguish between normal behavior and actual intrusions without requiring complex sensor arrays.
Solution Approach 2:
The system performs preliminary monitoring and analysis of resident behavior patterns before an intrusion detection scenario occurs. By establishing the baseline behavioral profile in advance, the system is prepared to quickly and accurately compare current sensor data against known normal patterns when security events occur.
2Difficulty of detecting and measuring
If multiple sensors are deployed to improve detection capabilities, then the system can monitor more parameters, but the false alarm rate increases and the system becomes more complex
Solution Approach 1:
Instead of relying on multiple sensors to detect every possible anomaly, the system creates a copy of the resident's unique behavioral fingerprint and uses this profile to interpret sensor data. This allows accurate detection with fewer sensors, as the system looks for deviations from the established behavioral copy rather than trying to detect all possible intrusion indicators.
Solution Approach 2:
The system continuously monitors activities and uses the results to refine and update the behavioral profile over time. This feedback mechanism allows the system to learn from past observations and improve its detection accuracy, reducing false alarms as the profile becomes more precise with each monitoring cycle.
3Measurement precision
If the system monitors all user activities to create accurate profiles, then detection accuracy improves, but user privacy concerns increase and system complexity increases
Solution Approach 1:
The system extracts only the essential behavioral patterns needed for security detection rather than monitoring and storing all possible user activities. By focusing on key indicators such as routine schedules, frequently visited rooms, and typical activity sequences, the system achieves accurate profiling with minimal monitoring overhead and reduced privacy intrusion.
Solution Approach 2:
The system allows users to voluntarily provide activity information through mobile device check-ins or optional sensors in specific locations. This self-service approach enables the system to build accurate behavioral profiles using user-contributed data rather than requiring comprehensive mandatory monitoring, thereby reducing complexity and privacy concerns.
Data Source
AI summary
Methods, systems and computing devices are described herein for monitoring a premises when residents or other users are present in order to detect patterns of activity at the premises. Such patterns may comprise, for example, a typical schedule indicating usage of one or more devices by one or more users of the premises. When a user is away or otherwise inactive, commands may be sent to various user devices to make it appear (e.g., to those outside the premises) as if the user is present by simulating and/or recreating the patterns that were previously detected.