Bed Weight Tracking Using Low-Entropy Sleep Data Windows

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Solution Overview

Problem

Existing bed systems fail to efficiently track inter-sleep-session changes in body weight and other physiological phenomena, producing high-entropy sensor data that is difficult to process and analyze in a timely manner.

Innovation Solution

A bed system equipped with weight sensors and computational nodes that utilize a distributed algorithm to reduce entropy in sensor data, enabling rapid analysis of weight and physiological metrics through low-entropy epochs, allowing for real-time monitoring and generation of clinically-relevant data.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If weight sensors continuously monitor body weight throughout sleep sessions, then measurement precision and data completeness are improved, but data processing complexity and entropy increase

Engineering Contradiction:

Improvebody weight measurement precisionVSAvoiddata processing complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The continuous weight monitoring data is segmented into discrete epochs (time windows), allowing the system to process data in manageable chunks rather than handling the entire continuous stream at once. This segmentation reduces processing complexity while maintaining measurement precision through systematic analysis of each epoch.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system extracts and isolates high-entropy portions of the sensor data (noisy or irrelevant segments) from the overall dataset. By identifying and separating these high-entropy regions, the system can focus computational resources on processing only the low-entropy, meaningful data portions, thereby reducing overall processing complexity while preserving measurement accuracy.

Inventive Principle:

Principle #2Taking out (Extraction)

2Productivity

If distributed algorithms process weight data across multiple computational nodes, then processing speed and productivity are improved, but system complexity increases

Engineering Contradiction:

Improvedata processing speedVSAvoidsystem architecture complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The distributed algorithm divides the weight data processing task into segments distributed across multiple computational nodes. Each node processes a specific portion of the data independently, and results are aggregated to produce the final analysis. This segmentation enables parallel processing that increases productivity while managing system complexity through modular architecture.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The computational nodes are designed with universal functionality to handle various processing operations (epoch identification, entropy calculation, data aggregation). This multi-functionality allows the same hardware infrastructure to support multiple processing tasks, increasing system productivity without proportionally increasing architectural complexity.

Inventive Principle:

Principle #6Universality (Multi-functionality)

3Productivity

If the system identifies and processes only low-entropy epochs, then analysis time is reduced and productivity increases, but information loss may occur

Engineering Contradiction:

Improveanalysis speedVSAvoidpotential information loss

Core Design Contradiction:

ProductivityVSLoss of information

Solution Approach 1:

The system extracts and processes only the low-entropy epochs (high-quality, informative data segments) while discarding or separately handling high-entropy portions (noisy or redundant data). This extraction approach increases analysis speed by focusing on meaningful data while minimizing information loss by preserving the extracted low-entropy epochs for further analysis.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The system incorporates feedback mechanisms that continuously monitor data quality and adjust processing parameters. By analyzing the entropy characteristics of processed epochs, the system can adaptively refine which data segments to process, ensuring that no critical information is lost while maintaining high processing productivity.

Inventive Principle:

Principle #23Feedback

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 expedites data processing from high-entropy to low-entropy, facilitating rapid analysis of weight and physiological changes, generating actionable insights within seconds instead of minutes, and supporting wellness coaching and home automation.

Implementation Method 1

one or more weight sensors configured to sense weight applied to the bed

Methodology Applied

Scientific EffectPiezoresistive Effect: Piezoresistive Effect

Data Source

PatentUS20250375332A1Bed system with features to track changes in body weight within a sleep session using rolling windows finding high quality sensor data including low-entropy sensor data

Publication Date: 2025.12.11 SLEEP NUMBER CORP

AI summary

A bed senses inter-sleep-session changes in bodyweight of a subject. A computer system can be configured to: receive weight readings; determine, using the weight readings, user presence in a bed; identify, using the user presence, sleep sessions for the user; and generate, using the weight readings and for a given sleep session, weight change data that records a change of weight by the user through the sleep session.