Biological-Movement Signal Fusion for Non-Invasive Impairment Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for detecting impairment indicators are invasive and lack accuracy, particularly in identifying cognitive, physical, mental, sensory, or developmental impairments, which is crucial for safety and compliance in various contexts.
Innovation Solution
A method and device using both movement and biological sensors to measure and compare signals during different time windows, calibrating impairment detection based on movement parameters with biological factors, reducing false positives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional impairment detection methods are used, then detection capability is provided, but the methods are invasive and lack accuracy
Solution Approach 1:
The patent combines movement sensors (accelerometers, gyroscopes) with biological sensors (heart rate monitors, skin conductance sensors) to create a comprehensive impairment detection system. This merging of sensor types allows non-invasive multi-parameter monitoring that improves detection accuracy by analyzing both movement patterns and physiological responses simultaneously
Solution Approach 2:
The patent replaces traditional invasive mechanical detection methods with electronic sensor-based monitoring. Instead of physical examinations or intrusive testing, the system uses accelerometers, gyroscopes, and biological sensors to non-invasively collect data about movement and physiological state, thereby eliminating the harmful factor of invasiveness while maintaining detection capability
2Measurement precision
If multiple sensors are used for impairment detection, then detection accuracy is improved, but device complexity increases
Solution Approach 1:
The patent divides the impairment detection system into separate functional modules: movement sensing (accelerometers, gyroscopes), biological sensing (heart rate, skin conductance), data processing units, and alarm systems. This segmentation allows each sensor type to be optimized independently while working together through a coordinated control architecture, managing overall system complexity
Solution Approach 2:
The patent designs a universal processing system that handles multiple sensor types through a common architecture. The same processing unit analyzes data from accelerometers, gyroscopes, and biological sensors using unified algorithms, reducing the need for separate dedicated processing circuits for each sensor type and thereby managing complexity
3Reliability
If continuous monitoring is implemented, then safety is improved, but energy consumption increases
Solution Approach 1:
The patent implements periodic sampling of sensor data at optimized intervals rather than continuous high-frequency monitoring. The system adjusts sampling rates based on activity level and risk assessment, maintaining safety monitoring while reducing energy consumption during low-risk periods
Solution Approach 2:
The patent uses dynamic adjustment of monitoring intensity based on real-time conditions. The system transitions between different monitoring modes (e.g., continuous, intermittent, or standby) based on detected patterns, allowing intensive monitoring when impairment is detected and reduced monitoring during normal states, thereby balancing safety with energy efficiency
Data Source
AI summary
A method and system for monitoring impairment indicators. The method includes, during a first time window, measuring a first movement signal related to movement of the person with a movement sensor associated with the person, and measuring a first biological signal of the person with a biological sensor attached to the person. The method further includes electronically storing at least one numerical descriptor derived from the first movement signal and at least one numerical descriptor derived from the first biological signal as reference data for the person. The method includes during a second time window, measuring a second signal related to movement of the person with the movement sensor, and measuring a second biological signal of the person with the biological sensor. The method further includes comparing at least one numerical descriptor derived from the second signal and at least one numerical descriptor derived from the second biological signal to the reference data to identify an impairment indicator.