Implantable Device Fall Risk Assessment via Multi-Sensor Fusion
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Solution Overview
Problem
Current medical devices lack an effective method to monitor and assess a patient's fall risk, which is crucial for preventing injuries and managing conditions that increase the likelihood of falls.
Innovation Solution
An implantable medical device (IMD) equipped with sensors, including electrodes for measuring electrograms, accelerometers for posture analysis, and optical sensors for oxygen saturation, processes data to determine a patient's fall risk by analyzing responses to body position changes and cardiac events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple sensors and processing circuitry are integrated into an implantable medical device to monitor patient parameters, then the ability to detect and analyze patient conditions improves, but the device complexity increases
Solution Approach 1:
The patent combines multiple sensors (accelerometer, electrogram electrodes, optical sensor) and processing circuitry into a single implantable medical device. The accelerometer monitors body position changes, electrodes detect cardiac electrical activity, and the optical sensor measures oxygen saturation. All these components are integrated within one device housing, allowing comprehensive patient monitoring without requiring multiple separate implants.
Solution Approach 2:
The implantable medical device performs multiple functions: it monitors body position via accelerometer, detects cardiac events through electrogram electrodes, measures oxygen saturation with an optical sensor, and processes this data to assess fall risk. This multi-functional approach allows a single device to provide comprehensive patient safety monitoring across different physiological parameters.
2Measurement precision
If the medical device continuously monitors multiple patient parameters over time, then the precision of fall risk assessment improves, but the energy consumption increases
Solution Approach 1:
The device monitors patient parameters continuously but processes data in periodic intervals to assess fall risk. The accelerometer continuously tracks body position, the electrodes continuously detect cardiac activity, and the optical sensor periodically measures oxygen saturation. The processing circuitry analyzes accumulated data at specific intervals rather than continuously computing, reducing energy consumption while maintaining assessment precision.
Solution Approach 2:
The device uses the patient's own physiological responses to body position changes as the basis for fall risk assessment. By monitoring how heart rate, blood pressure (via PTT), and other parameters naturally respond to sit-to-stand movements, the device derives fall risk information from the patient's own physiological data without requiring external energy input or additional active sensing mechanisms.
3Measurement precision
If the device analyzes patient responses to body position changes and cardiac events, then the accuracy of fall risk identification improves, but the difficulty of detecting and measuring increases
Solution Approach 1:
The fall risk assessment is divided into separate analytical components: body position detection via accelerometer, cardiac event detection via electrogram analysis, physiological parameter monitoring (heart rate, blood pressure through PTT, oxygen saturation), and integrated fall risk calculation. Each component processes specific data types independently, then the processing circuitry integrates these segmented results to produce the overall fall risk assessment.
Solution Approach 2:
The processing circuitry acts as an intermediary that receives raw data from multiple sensors, processes and correlates this information, and generates the fall risk assessment. The circuitry mediates between the complex sensor inputs (accelerometer signals, electrogram waves, optical absorption data) and the simplified fall risk output, translating complex multi-parameter data into an interpretable risk metric.
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 a more accurate and meaningful comparison of patient responses to different movements, enabling a precise identification of fall risk and potentially reducing the incidence of falls among patients.
Implementation Method 1
oxygen saturation using an optical sensor
Implementation Method 2
electrodes configured to measure an electrogram (EGM) of the patient. The EGM may, in some cases, indicate a ventricular depolarization (e.g., an R-wave) of the patient's heart
Implementation Method 3
tissue perfusion based on impedance sensed via the electrodes
Implementation Method 4
Processing circuitry may determine a pulse transit time (PTT) associated with the patient based on the EGM, the impedance, the measured oxygen saturation, or any combination thereof. PTT is correlated with blood pressure.
Implementation Method 5
the IMD may include a 3-axis accelerometer which generates an accelerometer signal indicative of a posture of the patient, an activity level of the patient, a gait of the patient, and a body angle of the patient
Data Source
AI summary
This disclosure is directed to devices, systems, and techniques for monitoring a patient condition. In some examples, a medical device system includes a medical device comprising a set of sensors. Additionally, the medical device system includes processing circuitry configured to identify, based on at least one signal of the set of signals, a time of an event corresponding to the patient and set a time window based on the time of the event. Additionally, the processing circuitry is configured to save, to a fall risk database in a memory, a set of data including one or more signals of the set of signals so that the fall risk database may be analyzed in order to determine a fall risk score corresponding to the patient, wherein the set of data corresponds to the time window.