Care System Using Segmented Sensor Activation for Physiological Monitoring
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Solution Overview
Problem
Conventional care systems rely heavily on manpower and limited sensor technologies, leading to burdens on caretakers and inefficiencies in monitoring and warning systems, especially for determining sleep quality and overall physiological status of care recipients.
Innovation Solution
A care system and automatic care method utilizing multiple sensors and machine learning technology to continuously monitor physiological and environmental data, activating additional sensors as needed to generate comprehensive detection data and build a personalized care-taking warning prediction model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple sensors are activated continuously to monitor care recipients, then measurement precision and reliability improve, but energy consumption and device complexity increase
Solution Approach 1:
The system performs preliminary monitoring using a first group of sensors to detect initial physiological status. When abnormal conditions are detected, the system then activates the second group of sensors to gather additional data for comprehensive analysis, avoiding continuous operation of all sensors
Solution Approach 2:
The sensor system is divided into two groups: a first group of sensors for continuous basic monitoring and a second group of sensors for activated detailed monitoring. This segmentation allows the system to balance between energy consumption and measurement precision by operating different sensor subsets based on needs
2Measurement precision
If comprehensive physiological data collection is implemented, then physiological status determination accuracy improves, but system complexity and data processing requirements increase
Solution Approach 1:
The system segments sensor operation into two modes: basic continuous monitoring with the first group of sensors, and detailed activated monitoring with the second group of sensors. This reduces overall system complexity by not requiring all sensors to operate simultaneously while maintaining comprehensive data collection capability when needed
Solution Approach 2:
The system dynamically adjusts sensor activation based on detected conditions. The data processing unit activates the second group of sensors only when the first group detects abnormal physiological status, making the system adaptable and reducing complexity through conditional operation rather than fixed comprehensive monitoring
3Measurement precision
If professional doctors manually analyze all physiological data, then care accuracy improves, but labor requirements and response time increase
Solution Approach 1:
The system performs self-service through automated data processing and analysis. The data processing unit automatically processes physiological data from sensors, determines care needs, and generates warnings or reminders without requiring continuous manual intervention by professional doctors, thereby improving efficiency while maintaining accuracy
Solution Approach 2:
The system implements automated feedback loops where sensor data is continuously processed, analyzed against predetermined criteria, and triggers appropriate warnings or reminders. This automated feedback mechanism reduces reliance on manual doctor analysis while maintaining high accuracy in care determination
4Ease of operation
If limited physiological sensors are used for home care, then ease of operation improves, but measurement precision and reliability deteriorate
Solution Approach 1:
The system uses the first group of sensors for preliminary continuous monitoring of basic physiological parameters during home care. This maintains ease of operation with simple continuous monitoring while enabling accurate sleep quality assessment. When needed, the second group of sensors is activated to provide additional measurement precision for comprehensive analysis
Data Source
AI summary
A care system and an automatic care method are provided. The care system is suitable for a care recipient. A physiological condition of the care recipient can be determined by sensors arranged around the care recipient, and a health condition of the care recipient can be determined through monitoring sleep disorders thereof. The care system receives a sound of the care recipient through a sound receiver, after removing a background sound, a sound of the care recipient can be obtained, and an image sensor is used to obtain an image of the care recipient to perform a motion detection, so as to obtain a posture image of the care recipient. Artificial intelligence technology can be used to build a care-taking warning prediction model to determine whether or not the status of the care recipient has reached a warning threshold.