ML Anomaly Detection for Human Presence Verification
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Solution Overview
Problem
Existing human presence detection systems in monitored locations suffer from high false alarms and missed detections due to inherent noise in outdated hardware and intentional manipulation, and are typically unreliable when legitimate occupants are present, as they cannot distinguish between authorized and unauthorized users.
Innovation Solution
A machine learning-based anomaly detection method that processes data from multiple sources to generate continuous time-series data, applies this data to a baseline behavioral model to identify deviations and generate a probability distribution, allowing for real-time detection of anomalies without requiring exact person identification, and can be executed on edge devices for reduced latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional sensor-based presence detection is used, then the system can detect human presence, but it produces high false alarms and missed detections due to noise and manipulation
Solution Approach 1:
The patent combines data from multiple diverse data sources (motion sensors, temperature sensors, audio sensors, camera data) into a unified analysis framework. By merging these different sensor types that measure different physical phenomena, the system creates a more robust detection approach where anomalies in one sensor can be contextualized by data from other sensors, reducing false alarms and missed detections caused by individual sensor noise or manipulation.
Solution Approach 2:
The system performs preliminary learning of normal behavior patterns during an initial period when legitimate occupants are present. This baseline behavioral model is established before the system begins anomaly detection, allowing it to distinguish between normal variations in sensor readings and actual anomalies. This preliminary action enables the system to adapt to the specific occupancy patterns of each location, improving reliability without requiring exact person identification.
2Productivity
If presence detection systems are activated continuously, then real-time monitoring is achieved, but legitimate occupants cannot distinguish between authorized and unauthorized users
Solution Approach 1:
The system performs preliminary learning of normal behavior patterns during an initial period when legitimate occupants are present. This baseline behavioral model captures the typical movement patterns, temperature changes, and audio characteristics of authorized users. During continuous monitoring, the system compares current sensor data against this learned baseline, enabling it to distinguish between authorized occupants (who match the baseline) and unauthorized users (who deviate from it), thereby achieving both continuous monitoring and accurate verification.
Solution Approach 2:
The system dynamically adapts the baseline behavioral model based on learned occupancy patterns. Rather than using fixed thresholds, the system continuously refines its understanding of normal behavior for each location, adjusting its detection criteria to match the specific patterns of legitimate occupants. This dynamic approach allows the system to maintain high precision in presence verification while operating continuously, as it can differentiate between authorized users following established patterns and intruders exhibiting anomalous behavior.
Data Source
AI summary
Techniques are provided for machine learning-based anomaly detection in a monitored location. One method comprises obtaining data from multiple data sources associated with a monitored location for storage into a data repository; processing the data to generate substantially continuous time-series data for multiple distinct features within the data; applying the substantially continuous time-series data for the distinct features to a machine learning baseline behavioral model to obtain a probability distribution representing a behavior of the monitored location over time; and evaluating a probability score generated by the machine learning baseline behavioral model to identify an anomaly at the monitored location. The machine learning baseline behavioral model is trained, for example, to identify anomalies in correlations between the plurality of distinct features at each timestamp. A presence verification is optionally provided based on a deviation from the machine learning baseline behavioral model at the monitored location.