Behavioral Exception Detection Using Atomic Pattern Segmentation
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Solution Overview
Problem
Individuals with communication and cognitive disabilities, such as autism, face challenges in expressing feelings and emotions, leading to increased vulnerability to mistreatment and abuse, necessitating tools for enhanced personal safety and well-being monitoring.
Innovation Solution
A system utilizing personal communication devices with sensors to track location and motion data, analyzing behavior patterns through clustering algorithms, and identifying exceptions such as changes in activity or emotional arousal, with visualization and alert mechanisms for caregivers, incorporating environmental and external data sources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sensors and data collection systems are deployed to monitor individuals with disabilities, then personal safety and well-being monitoring is improved, but device complexity and system resource requirements increase
Solution Approach 1:
The system segments behavior monitoring into atomic patterns (basic movements, location changes, activity types) that can be independently detected and analyzed. This segmentation allows the complex monitoring task to be divided into manageable components, reducing overall system complexity while maintaining comprehensive safety monitoring.
Solution Approach 2:
The system performs preliminary actions by pre-defining atomic behavior patterns and activity types before actual monitoring begins. By establishing these baseline patterns in advance, the system can quickly compare real-time sensor data against known behaviors without requiring complex real-time analysis, thus improving reliability while managing complexity.
2Measurement precision
If detailed behavior analysis and exception identification are implemented, then detection precision of safety issues is improved, but loss of time for data processing increases
Solution Approach 1:
The system applies partial action by focusing analysis only on exceptional behaviors that deviate from normal atomic patterns, rather than analyzing every single behavior in detail. This selective approach maintains high detection precision for safety issues while minimizing unnecessary data processing time for routine activities.
Solution Approach 2:
The system uses feedback mechanisms where detected atomic patterns are continuously compared against expected behavior models, and only exceptions trigger detailed analysis. This feedback-driven approach ensures high measurement precision for abnormal behaviors while avoiding excessive processing time for normal activities.
3Loss of information
If comprehensive sensor data collection is performed, then information completeness for safety monitoring is improved, but use of energy by the monitoring system increases
Solution Approach 1:
The system extracts only the essential atomic behavior patterns needed for safety monitoring from the comprehensive sensor data, rather than processing all available information. This extraction approach maintains information completeness for safety-critical behaviors while reducing energy consumption by ignoring redundant data.
Solution Approach 2:
The system applies local quality by adjusting sensor sampling rates and data collection frequency based on the specific behavior being monitored. Critical safety-related behaviors trigger higher-frequency data collection, while routine activities use lower-frequency sampling, optimizing the balance between information completeness and energy usage.
Data Source
AI summary
A method and system for identifying exceptions, of a person/child behavior through scheduled behavior, using a personal communication device having at least one sensor attached to person/child body which provide location data and motion data. The method includes sampling measurements data for each sensor of the personal communication device, identifying atomic pattern of sampled measurements, the atomic pattern representing basic behavior of the person/child, identify location in which person has spent time using a clustering algorithm, create activity segmentation characterized by location, schedule, caregiver, or content derived from sensors measurements and atomic patterns and analyzing characteristics changes or combination thereof of activities in sequenced/complex activities in comparison to baseline, to identify exception which indicate of at least one of the following: changes at activity level, changes at emotional arousal level, unknown locations or unexpected locations based on schedule.