Adaptive control of monitoring devices
Disclosed herein is a framework for facilitating adaptive control of monitoring devices. In accordance with one aspect, a position detector detects a chest elevation level and provides chest elevation level data. A processor uses the chest elevation level data to determine a heart elevation level with respect to a reference level. A comparator compares the determined heart elevation level with an elevation level of a monitoring device with respect to the reference level. In response to the comparison, a movement system adjusts the elevation level of the monitoring device.
1. A monitoring device control system, comprising:
a position detector that detects a chest elevation level of a subject and provides chest elevation level data;
a pressure sensor system installed on a bed that provides pressure sensor data associated with the subject;
a processor in a control unit that uses the chest elevation level data to continuously monitor a heart elevation level with respect to a reference level, wherein the control unit recalibrates the heart elevation level in accordance to whether a left movement or a right movement of the subject is detected, wherein recalibration in accordance with left movement differs from recalibration in accordance with right movement for a given subject chest thickness, wherein the control unit further comprises a closed-loop self-learning structure that nonlinearly combines the heart elevation data, the pressure sensor data and a position of a trolley or rail where a monitoring device is installed to generate one or more control parameters; and
a movement system that adjusts an elevation level of the monitoring device in response to the one or more control parameters.
2. The system according to claim 1 , wherein the closed-loop self-learning structure comprises an artificial neural network.
3. The system according to claim 1 , wherein
the position detector detects chest thickness based on the chest elevation level data and provides chest thickness data, and
the processor uses the chest thickness data to determine the heart elevation level.
4. The system according to claim 3 , wherein the processor uses the chest thickness data to determine the heart elevation level by multiplying the chest thickness by a predetermined factor.
5. The system according to claim 4 , wherein the processor selects the predetermined factor from a plurality of predetermined factors associated with subjects having different demographic characteristics.
6. The system according to claim 5 , wherein the different demographic characteristics comprise age, height, weight, pregnancy status or gender.
7. The system according to claim 4 , wherein the processor adjusts the predetermined factor by a predetermined coefficient based at least in part on body size of the subject to compensate for movement of the subject.
8. The system according to claim 1 , wherein the movement system adjusts the elevation level of the monitoring device to match the determined heart elevation level.
9. The system according to claim 1 , wherein the monitoring device comprises a blood pressure monitor.
10. A system, comprising:
a position detector that includes a light emitter and sensor pair that detects a chest elevation level of a subject and provides chest elevation level data;
a pressure sensor system installed on a bed that provides pressure sensor data associated with the subject;
a processor in a control unit that uses the chest elevation level data to continuously monitor a heart elevation level with respect to a reference level, wherein the control unit recalibrates the heart elevation level in accordance to whether a left movement or a right movement of the subject is detected, wherein recalibration in accordance with left movement differs from recalibration in accordance with right movement for a given subject chest thickness, wherein the control unit further comprises a closed-loop self-learning structure that nonlinearly combines the heart elevation data, the pressure sensor data and a position of a trolley or rail where a monitoring device is installed to generate one or more control parameters; and
a movement system that adjusts an elevation level of the monitoring device in response to the one or more control parameters.
11. The system according to claim 10 , wherein the monitoring device comprises a blood pressure monitor.
12. The system according to claim 10 , wherein
the position detector detects chest thickness based on the chest elevation level data and provides chest thickness data, and
the processor uses the chest thickness data to determine the heart elevation level.
13. The system according to claim 12 , wherein the processor uses the chest thickness data to determine the heart elevation level by multiplying the chest thickness by a predetermined factor.
14. The system according to claim 13 , wherein the processor selects the predetermined factor from a plurality of predetermined factors associated with subjects having different demographic characteristics.
15. The system according to claim 10 , wherein the closed-loop self-learning structure comprises an artificial neural network.
16. The system according to claim 10 , wherein the closed-loop self-learning structure further nonlinearly combines the heart elevation data, the pressure sensor data and the position of the trolley or rail with demographic data.
17. The system according to claim 10 , wherein the one or more control parameters comprise altitude change, altitude speed of change, calibration and compensation parameters for controlling or positioning the monitoring device.
18. The system according to claim 10 , wherein the light emitter and sensor pair detects upper and lower chest elevation levels.
19. A method of controlling a monitoring device, comprising:
determining a heart elevation level of a subject with respect to a reference level by multiplying a chest thickness of the subject by a predetermined factor, wherein the predetermined factor is adjusted by using a predetermined coefficient based at least in part on body size of the subject and whether a left movement or a right movement of the subject is detected, wherein recalibration in accordance with left movement differs from recalibration in accordance with right movement for a given subject's chest thickness;
determining one or more control parameters by nonlinearly combining, via a closed-loop artificial neural network, the heart elevation level with demographic data of the subject; and
adjusting, by a movement system, an elevation level of a monitoring device in response to the one or more control parameters.
20. A method comprising:
determining a heart elevation level of a subject with respect to a reference level by multiplying a chest thickness of the subject by a predetermined factor, wherein the predetermined factor is adjusted by using a predetermined coefficient based at least in part on body size of the subject and whether a left movement or a right movement of the subject is detected, wherein recalibration in accordance with left movement differs from recalibration in accordance with right movement for a given subject's chest thickness;
determining one or more control parameters by nonlinearly combining, via a closed-loop self-learning structure, the heart elevation level with demographic data of the subject and a position of a trolley or rail where a monitoring device is installed; and
adjusting an elevation level of the monitoring device in response to the one or more control parameters.