Physiological Sensor Data Quality Control via Adaptive Querying
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Solution Overview
Problem
Current systems for monitoring physiological parameters in residential settings lack efficient methods to ensure high-quality data acquisition and timely detection of changes in patient conditions, particularly for chronic conditions like heart failure, due to limitations in sensor placement, data quality assessment, and adaptive measurement strategies.
Innovation Solution
A system comprising a network of remote non-contact physiological sensors, including microphones and image capture devices, connected to a computing device that queries sensors based on need-measurement criteria, assigns quality values to sensor data, and updates flags to ensure data quality thresholds are met, allowing for adaptive and timely data acquisition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If continuous monitoring of physiological parameters is implemented, then detection timeliness of patient condition changes is improved, but energy consumption and system resource usage increase
Solution Approach 1:
The system implements periodic sampling of physiological parameters at predetermined time intervals rather than continuous monitoring. The processor queries sensors at specific intervals to collect sensor information, which reduces energy consumption while still enabling timely detection of patient condition changes. This periodic action allows the system to balance monitoring responsiveness with resource conservation.
Solution Approach 2:
The system dynamically adjusts monitoring frequency based on patient condition. When quality indicators indicate deteriorating conditions, the processor increases the frequency of sensor queries to provide more frequent updates. Conversely, when conditions are stable, monitoring frequency is reduced. This dynamic adaptation optimizes both detection timeliness and energy consumption based on actual patient needs.
2Measurement precision
If multiple sensors are deployed for comprehensive monitoring, then measurement precision and reliability are improved, but device complexity increases
Solution Approach 1:
The system employs feedback mechanisms where the processor evaluates quality indicators of sensor data and uses this information to adjust monitoring strategies. When sensor data quality is sufficient, the system maintains current monitoring levels. When quality deteriorates, the processor triggers alerts and adjusts sensor query frequencies. This feedback loop ensures measurement precision while managing system complexity through intelligent data evaluation rather than simply adding more sensors.
Solution Approach 2:
The system uses multiple types of sensors (acoustic, optical, electromagnetic) that can serve multiple monitoring functions. For example, acoustic sensors can detect both respiratory sounds and heart sounds, while optical sensors can measure both oxygen saturation and heart rate. This multi-functionality allows comprehensive monitoring with fewer sensor types, reducing overall system complexity while maintaining measurement precision.
3Reliability
If frequent sensor queries are performed, then data quality and reliability are improved, but loss of time for processing and transmitting data increases
Solution Approach 1:
The system performs preliminary evaluation of sensor data quality before full processing and transmission. The processor quickly assesses quality indicators of incoming sensor information and only triggers comprehensive data processing and transmission when quality thresholds are met. This preliminary action filters out low-quality data early in the process, reducing unnecessary processing time while ensuring that only reliable data undergoes full analysis and transmission.
Solution Approach 2:
The system applies partial processing to sensor data based on quality assessments. When data quality is sufficient, full processing and transmission are performed. When quality is marginal, the system performs only essential processing or requests additional sensor readings before proceeding with full analysis. This partial action approach maintains data reliability by ensuring adequate quality checks while reducing overall processing time by avoiding unnecessary full processing of low-quality data.
Data Source
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AI summary
Systems and methods are described for controlling acquisition of sensor information, including: one or more physiological sensors and a computing device including a processor programmed to query the physiological sensors to measure one or more physiological parameters of an individual in response to at least one flag indicating a need to measure the one or more physiological parameters; receive a set of sensor values from the physiological sensors; assign a quality value to the set of sensor values received from the physiological sensors; retain the set of sensor values if the assigned quality value of the set of sensor values meets or exceeds a minimum quality value threshold; and update the at least one flag if the assigned quality value of the set of sensor values meets or exceeds the minimum quality value threshold.