Multidimensional Multivariate Multiple Sensor System
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Solution Overview
Problem
Current biometric sensing technologies face challenges in accurately distinguishing external vibrations and multiple subjects due to limitations in distinguishing between static signals and air pressure variations, making it difficult to determine biometric parameters, presence, weight, location, and position on a bed.
Innovation Solution
The implementation of a system using multiple sensors to generate multiple sensor multiple dimensions array (MSMDA) data, which processes macro and micro signals to determine biometric parameters and person-specific information by analyzing relationships between sensors, including location, angular orientation, and body position, using proprietary algorithms to separate individual source measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple sensors are used to detect biometric parameters, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The system divides the sensing task across multiple sensors positioned at different locations on the substrate. Each sensor captures specific signal components, and the processor segments and integrates these signals to achieve comprehensive biometric measurement with improved precision while managing complexity through functional distribution.
Solution Approach 2:
The patent transitions from single-sensor point detection to multi-sensor spatial array detection. By adding spatial dimensionality through multiple sensors arranged in specific patterns, the system achieves superior measurement precision for location, orientation, and body position while the processor manages the increased data complexity through algorithmic integration.
2Measurement precision
If sensors detect static signals, then biometric parameters can be determined, but the ability to distinguish external vibrations deteriorates
Solution Approach 1:
The system merges signals from multiple sensors to achieve signal integration that enhances biometric parameter detection while suppressing external vibration interference. The processor combines macro and micro signals from different sensor locations, using signal processing techniques to distinguish physiological signals from environmental noise.
Solution Approach 2:
The system employs feedback mechanisms where the processor continuously analyzes sensor data, identifies patterns characteristic of biometric parameters, and adjusts signal processing accordingly. This feedback loop enables differentiation between static biometric signals and dynamic external vibrations through pattern recognition and adaptive filtering.
3Adaptability or versatility
If sensors are used to detect multiple subjects, then person-specific information can be obtained, but the ability to distinguish between multiple subjects deteriorates
Solution Approach 1:
The system segments the detection space into multiple zones using the array of sensors. Each subject interacts with different sensor subsets, and the processor assigns signals to specific subjects based on spatial relationships and signal characteristics. This segmentation enables simultaneous multi-subject detection while maintaining individual subject differentiation accuracy.
Solution Approach 2:
The patent applies local quality by having different sensors or sensor combinations optimized for detecting specific subjects in different locations. The processor analyzes local signal patterns at each sensor position and integrates them to achieve both multi-subject versatility and precise subject differentiation through location-specific signal characteristics.
Data Source
AI summary
Devices and methods for determining item-specific information for single or multiple items on one or multiple substrates are described. The method includes generating multiple sensor multiple dimensions array (MSMDA) data from multiple sensors, where each of the multiple sensors capture sensor data for one or more items in relation to a substrate. For each item, the method includes determining relationships between the multiple sensors based on characteristics of the MSMDA data, determining a location of the item on the substrate based on at least the determined relationships between the multiple sensors, determining an angular orientation of the item on the substrate based on at least the determined relationships between the multiple sensors, and determining a body position of the subject on the substrate based at least the determined relationships between the multiple sensors, the location of the subject, and the angular orientation of the item.