Bed Load Sensor Matrix for Fall Detection
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Solution Overview
Problem
Existing present-in-bed state monitoring systems in medical and caregiving settings often result in mistaken notifications, leading to a need for improved precision in determining a subject's presence on a bed and detecting potential falls.
Innovation Solution
A system comprising load detectors, a center of gravity position calculating unit, a waveform output unit, a body motion determining unit, and a notification unit to accurately assess the subject's presence and motion, with distinct notifications for different conditions, such as body motion and edge area positioning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If load detectors are used to determine present-in-bed state, then basic monitoring function is provided, but mistaken notifications occur due to insufficient precision
Solution Approach 1:
The bed surface is divided into multiple detection zones with load detectors arranged in a matrix pattern, allowing segmentation of the monitoring area into edge areas and central areas. This enables precise localization of the subject's position and differentiation between actual falls and normal body movements within specific zones.
Solution Approach 2:
The system transitions from simple load magnitude detection to two-dimensional position detection by analyzing the distribution pattern of loads across multiple detectors. This dimensional enhancement allows the system to distinguish between subjects lying on the bed versus those who have fallen, based on the spatial configuration of detected loads.
2Device complexity
If simple load detection is used, then system complexity is reduced, but ability to differentiate body motion from respiration is insufficient
Solution Approach 1:
The system utilizes the mechanical vibration principle by detecting oscillatory movements of the subject's body. By analyzing the frequency and pattern of load variations on the load detectors, the system can distinguish between respiratory movements (higher frequency, smaller amplitude) and body motions (lower frequency, larger amplitude) without requiring complex additional sensors.
Solution Approach 2:
The system implements feedback by continuously monitoring load detector outputs and comparing temporal patterns against predefined criteria for respiration and body motion. This feedback mechanism enables real-time differentiation between normal respiratory activity and significant body movements that may indicate falling or other events requiring notification.
3Reliability
If multiple detection criteria are applied, then notification accuracy is improved, but false alarm reduction becomes more difficult
Solution Approach 1:
The system applies preliminary action by pre-defining detection criteria and decision thresholds for different scenarios (respiration pattern, body motion detection, edge area identification). These predetermined rules are established beforehand to systematically evaluate detection data, reducing the complexity of real-time decision-making while maintaining high notification accuracy and minimizing false alarms.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system provides high-precision monitoring and notification of a subject's in-bed state, reducing false alarms and enhancing safety by differentiating between body motions and respiratory activities, thereby improving fall detection accuracy.
Implementation Method 1
a plurality of load detectors configured to be provided on the bed or under legs of the bed to detect a load of the subject
Data Source
AI summary
A present-in-bed state monitoring system (100) configured to monitor a present-in-bed state of a subject on a bed (BD) includes: a plurality of load detectors (11, 12, 13, 14) configured to be provided on the bed or under legs of the bed to detect a load of the subject; a center of gravity position calculating unit (31) configured to calculate a center of gravity position of the subject based on the detected load of the subject; a waveform output unit (32) configured to output a waveform including a respiratory waveform of the subject based on a temporal variation of the calculated center of gravity position; a center of gravity position determining unit (332) configured to determine whether or not the center of gravity position is in an edge area of the bed; a body motion determining unit (331) configured to determine whether or not the subject has a body motion based on the waveform including the respiratory waveform of the subject, the body motion being different from a respiration of the subject; and a notifying unit (5) configured to perform a notification about the present-in-bed state of the subject based on a determination result of the body motion determining unit and a determination result of the center of gravity position determining unit.