Smart Scale Weight Normalization via Load Cell Array and Machine Learning
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Solution Overview
Problem
Current smart scale systems lack the ability to accurately determine the weight of non-static items and provide comprehensive health-related information, particularly for animals and pets, and do not effectively utilize pressure data to assess weight distribution and sleep quality.
Innovation Solution
A smart scale system that incorporates a plurality of load cells and pressure sensors to generate weight and pressure data, which is then processed using machine learning algorithms to determine normalized weights and sleep status, including a pressure heat map for weight distribution analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional load cell systems are used to measure weight, then weight data can be obtained, but the measurement precision is insufficient for non-static items and animals
Solution Approach 1:
The patent divides the measurement system into multiple load cells arranged in an array, each measuring local pressure points. By segmenting the measurement into multiple discrete points and combining them, the system achieves both precision for static weight and reliability for dynamic items like animals that move during measurement
Solution Approach 2:
The patent transitions from traditional single-point or few-point load measurement to a two-dimensional array of pressure sensors. This dimensional expansion allows the system to capture weight distribution across the surface, providing accurate measurements for non-static items by measuring pressure at multiple locations simultaneously
2Loss of information
If pressure sensors are added to the smart scale system, then pressure data and weight distribution information can be obtained, but the device complexity increases
Solution Approach 1:
The pressure sensor array serves multiple functions: measuring total weight, determining weight distribution, analyzing body posture, and detecting presence. By making the sensing system multi-functional, the patent reduces overall system complexity as a single component performs what would otherwise require multiple separate systems
Solution Approach 2:
The patent combines the pressure sensing function with the weight measurement function into a unified sensor array system. The same array of sensors that measures weight also captures pressure distribution data, eliminating the need for separate sensing systems and reducing overall device complexity
3Measurement precision
If machine learning algorithms are used to process weight data, then normalized weight can be determined for non-static items, but the loss of time for data processing increases
Solution Approach 1:
The patent collects and stores historical weight data and pressure distribution patterns before they are needed for normalization. By pre-processing and storing reference data during periods when the system is not in active use, the machine learning algorithm can quickly compare current measurements against pre-established patterns, reducing real-time processing time while maintaining high precision
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 accurate and normalized weight measurements for various items, including animals, and assesses sleep quality by analyzing pressure data, offering comprehensive health-related information.
Implementation Method 1
Weight data associated with the non-static item is received from a plurality of load cells
Implementation Method 2
pressure data associated with the non-static item is received from an array of pressure sensors
Data Source
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AI summary
A method for determining a normalized weight of a non-static item is disclosed. Weight data associated with the non-static item is received from a plurality of load cells. A load cell weight for the non-static item is determined based at least in part on the weight data. The load cell weight for the non-static item is received as an input for a machine learning algorithm. The normalized weight for the non-static item is generated as an output for the machine learning algorithm.