Sensor Anomaly Detection With AI Risk Response for Safer Monitoring
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Solution Overview
Problem
Existing sensor-enabled environments lack effective systems for identifying and managing risks to the safety and well-being of individuals under monitoring, particularly in predicting and mitigating potential safety events through comprehensive risk assessment and response strategies.
Innovation Solution
A system that integrates risk evaluation and response systems, utilizing AI/ML models to analyze sensor data, generate risk metrics, and provide timely alerts and optimized responses to stakeholders, incorporating feedback mechanisms to minimize unintended consequences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If comprehensive sensor data collection and AI/ML analysis are implemented for risk assessment, then safety event prediction capability is improved, but system complexity increases
Solution Approach 1:
The system segments the complex monitoring task into distinct functional modules: sensor data acquisition module, AI/ML analysis module, risk metric calculation module, and alert generation module. Each module handles a specific aspect of the safety monitoring process, making the overall system more manageable and maintainable while achieving comprehensive safety event prediction
Solution Approach 2:
The patent introduces an intermediary processing layer that sits between raw sensor data and final safety alerts. This layer includes buffer memory for data storage, preprocessing circuits for data cleaning, and AI/ML models for pattern recognition. The intermediary layer transforms complex raw data into meaningful risk metrics, reducing the complexity burden on the alert generation system
2Loss of time
If continuous monitoring and real-time alert generation are implemented, then response time to safety events is improved, but energy consumption increases
Solution Approach 1:
The system implements periodic sampling of sensor data at optimized intervals rather than continuous monitoring. The AI/ML analysis is performed periodically on accumulated data batches, allowing the system to maintain real-time safety monitoring capability while reducing peak energy consumption during data processing periods
Solution Approach 2:
The system employs edge computing capabilities where the wearable device performs preliminary data filtering and processing locally using onboard microprocessors. Only relevant anomaly detections are transmitted to remote servers for further analysis, reducing the energy burden of continuous cloud communication while maintaining rapid local response capability
3Measurement precision
If multiple risk metrics and safety parameters are calculated, then accuracy of safety assessment is improved, but computational load increases
Solution Approach 1:
The system calculates different risk metrics with varying degrees of precision based on local conditions. For example, fall risk assessment uses high-precision accelerometer data with complex algorithms, while general activity monitoring uses lower-precision motion detection. This localized precision approach maintains overall assessment accuracy while reducing total computational load
Solution Approach 2:
The AI/ML models dynamically adjust calculation parameters based on the current operational context. When the person being monitored is in a low-risk state, the system reduces the frequency and complexity of metric calculations. When anomaly patterns are detected, the system increases computational intensity for specific risk parameters, optimizing the balance between accuracy and computational load
Data Source
AI summary
Automated anomaly detection and response generation in a sensor-enabled environment (SEE) may be provided via measuring, via a plurality of sensors disposed in a SEE, state information of the SEE and a person under monitoring (PUM) within the SEE; identifying, using the state information, an in-progress behavior affecting the PUM in the SEE; identifying an intended outcome of the in-progress behavior; predicting, using the state information and the in-progress behavior, a predicted outcome of the in-progress behavior; and in response to identifying that a difference between the intended outcome and the predicted outcome represents an actionable risk level for health, wellness or safety of the PUM: identifying a responsive action to the in-progress behavior to reduce the actionable risk level to the PUM; and performing the responsive action.