Non-contact Sleep Monitoring for RBD Detection
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Solution Overview
Problem
Current methods for diagnosing Rapid Eye Movement Sleep Behavior Disorder (RBD) are expensive, uncomfortable, and require patients to wear contact sensors in a hospital setting, making them inconvenient for widespread screening.
Innovation Solution
A sleep management system comprising multiple sensors, a hub device, and a user device that collects and processes data to determine if RBD occurs during REM sleep, using machine learning models to analyze body movements and sleep stages, and performs operations such as alerting the user or notifying a medical institution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If polysomnography (PSG) is performed in a hospital with contact sensors, then diagnostic accuracy for RBD is improved, but patient comfort and convenience deteriorate
Solution Approach 1:
The patent replaces contact-based mechanical sensors (electrodes, belts) with non-contact sensors including radar sensors, optical sensors, and acoustic sensors. These sensors detect body movements, breathing patterns, and other physiological signals without physical contact, thereby maintaining diagnostic accuracy while significantly improving patient comfort and convenience during sleep monitoring
Solution Approach 2:
The patent introduces a processor that acts as an intermediary between non-contact sensors and diagnostic interpretation. The processor analyzes signals from multiple non-contact sensors (radar, optical, acoustic) to reconstruct physiological parameters, enabling accurate RBD diagnosis without direct sensor-to-skin contact
2Measurement precision
If multiple contact sensors are worn on the body, then measurement precision for RBD detection is improved, but device complexity and cost increase
Solution Approach 1:
The patent employs non-contact sensors that serve multiple functions simultaneously. For example, radar sensors can detect both body position and movement patterns, while optical sensors can monitor both respiratory rate and heart rate. This multi-functionality reduces the need for multiple specialized contact sensors, simplifying the overall system while maintaining comprehensive RBD detection capability
Solution Approach 2:
The patent combines multiple types of non-contact sensors (radar, optical, acoustic) into a unified monitoring system. The processor integrates data from these different sensor modalities to achieve accurate RBD detection, replacing the need for multiple separate contact sensor systems and reducing overall device complexity
3Reliability
If hospital-based PSG is used for screening, then diagnostic reliability is improved, but accessibility and convenience deteriorate
Solution Approach 1:
The patent enables patients to perform self-monitoring at home using non-contact sensors placed in the bedroom environment. The system automatically detects sleep parameters and RBD episodes without requiring hospital staff intervention or specialized medical equipment, making reliable screening accessible to patients in their own homes
Solution Approach 2:
The patent uses a processor and communication system as intermediaries to transmit sleep data from the home environment to healthcare providers. This intermediary layer maintains diagnostic reliability by ensuring accurate data collection and transmission while eliminating the need for patients to travel to hospitals for screening
Data Source
AI summary
A sleep management system including: a plurality of sensors including a first sensor and a second sensor, the first sensor and the second sensor being configured to collect data of a user; a hub device configured to: receive first data from the first sensor and second data from the second sensor, obtain first processed data based on processing of the first data, and obtain second processed data based on processing of the second data; and a user device configured to: receive the first processed data and the second processed data from the hub device, obtain sleep state information about a sleep stage and a body movement of the user based on the first processed data and the second processed data, determine whether a Rapid Eye Movement Sleep Behavior Disorder (RBD) of the user occurs based on the body movement of the user.