Physiological monitoring devices and methods for noise reduction in physiological signals based on subject activity type
Methods and apparatus for monitoring a subject are described. A monitoring device configured to be attached to a body of a subject includes a sensor that is configured to detect and/or measure physiological information from the subject and at least one motion sensor configured to detect and/or measure subject motion information. The physiological sensor and motion sensor are in communication with a processor that is configured to receive and analyze signals produced by the physiological sensor and motion sensor. The processor is configured to process motion sensor signals to identify an activity characteristic of the subject. Once an activity characteristic is identified, the processor is configured to select a noise reference in response to identification of the activity characteristic of the subject, and then process physiological sensor signals using the noise reference to generate an output signal having reduced noise relative to the physiological sensor signal, to produce physiological information about the subject.
1. A method of monitoring a subject via a monitoring device, wherein the monitoring device includes a physiological sensor, a first type of motion sensor and a second type of motion sensor, each in communication with at least one processor, the method comprising:
processing motion sensor signals received from at least one of the first type of motion sensor or the second type of motion sensor using the at least one processor to identify an activity characteristic of the subject based on a pattern associated with a known subject activity mode;
in response to the identified activity characteristic being classified within a first category:
operating the first type of motion sensor to detect first motion sensor data;
selecting by the at least one processor and from a set of preconfigured biometric signal extraction algorithms, a first biometric signal algorithm for processing sensors signal received from the physiological sensor, the first biometric signal algorithm using the first motion sensor data to remove noise from received physiological sensor data; and
processing physiological sensor signals received from the physiological sensor via the at least one processor using the first biometric signal algorithm to produce physiological information about the subject;
in response to the identified activity characteristic being classified within a second category:
operating the second type of motion sensor to detect second motion sensor data;
selecting by the at least one processor and from the set of preconfigured biometric signal extraction algorithms, a second biometric signal algorithm for processing sensors signal received from the physiological sensor, the second biometric signal algorithm using the second motion sensor data to remove noise from received physiological sensor data; and
processing physiological sensor signals received from the physiological sensor via the at least one processor using the second biometric signal algorithm to produce physiological information about the subject.
2. The method of claim 1 , wherein the pattern associated with the known subject activity mode comprises a first pattern associated with periodic activity and a second pattern associated with lifestyle activity that is different from the first pattern.
3. The method of claim 2 , wherein the first or second biometric signal extraction algorithm is configured based on noise associated with the activity characteristic that was identified from processing the motion sensor signals.
4. The method of claim 3 , wherein selecting the first or second biometric signal extraction algorithm based on the activity characteristic comprises:
selecting frequency domain processing and/or filtering in response to identification of the activity characteristic based on one of the first pattern and the second pattern; and
selecting time domain processing and/or filtering in response to identification of the activity characteristic based on another of the first pattern and the second pattern.
5. The method of claim 2 , wherein selecting the first or second biometric signal extraction algorithm comprises modifying the first or second biometric signal extraction algorithm or circuit based on the activity characteristic that was identified from processing the motion sensor signals.
6. The method of claim 2 , wherein, responsive to identification of the activity characteristic based on the second pattern and processing the physiological signal using the second biometric signal extraction algorithm, an accuracy of the physiological information is increased relative to processing the physiological signal using the first biometric signal extraction algorithm.
7. The method of claim 2 , wherein one of the first type of motion sensor or the second type of motion sensor comprises a multi-axis accelerometer, and wherein processing motion sensor signals via the at least one processor to identify an activity characteristic of the subject comprises:
filtering a signal from one or more axes of the accelerometer over a given time period;
producing an accelerometer spectrum using the signal that was filtered; and
determining if one or more frequencies in the accelerometer spectrum match the pattern associated with the known subject activity mode.
8. The method of claim 7 , wherein the determining comprises determining if a ratio of peak frequencies fit the first pattern associated with the periodic activity.
9. The method of claim 1 , wherein the physiological sensor is a photoplethysmography (PPG) sensor, and wherein the biometric signal extraction algorithm is configured to produce subject biometric information from the physiological sensor signals produced by the PPG sensor.
10. The method of claim 9 , wherein the physiological information includes one or more of the following: subject heart rate, subject respiration rate, subject respiratory rate interval (RRi), subject heart rate variability (HRV), subject blood pressure, subject blood analyte levels, subject cardiovascular properties.
11. The method of claim 1 , wherein the monitoring device is configured to be positioned at or within an ear of the subject, secured to an appendage of the subject, or worn on a bodily location of the subject.
12. The method of claim 1 , further comprising:
monitoring the physiological sensor signals and/or the motion sensor signals; and
detecting a change in activity of the subject responsive to the monitoring,
wherein the processing the motion sensor signals to identify the activity characteristic of the subject is responsive to detecting the change in activity.
13. A monitoring device configured to be attached to a bodily location of a subject, the monitoring device comprising:
a physiological sensor configured to detect and/or measure physiological data from the subject and generate physiological signals representative thereof;
a first type of motion sensor configured to detect and/or measure subject motion data and generate first motion sensor signals representative thereof;
a second type of motion sensor configured to detect and/or measure subject motion data and generate second motion sensor signals representative thereof; and
at least one processor coupled to the physiological sensor and the motion sensor, wherein the at least one processor is configured to perform operations comprising:
processing at least one of the first motion sensor signals and the second motion sensor signals to identify an activity characteristic of the subject based on a pattern associated with a known subject activity mode;
in response to the identified activity characteristic being classified within a first category:
operating the first type of motion sensor to detect first motion data;
selecting by the at least one processor and from a set of preconfigured biometric signal extraction algorithms, a first biometric signal algorithm for processing sensors signal received from the physiological sensor, the first biometric signal algorithm using the first motion data to remove noise from received physiological sensor data; and
processing the physiological sensor signals using the first biometric signal algorithm to produce physiological information about the subject
in response to the identified activity characteristic being classified within a second category:
operating the second type of motion sensor to detect second motion data;
selecting by the at least one processor and from the set of preconfigured biometric signal extraction algorithms, a second biometric signal algorithm for processing sensors signal received from the physiological sensor, the second biometric signal algorithm using the second motion data to remove noise from received physiological sensor data; and
processing physiological sensor signals received from the physiological sensor via the at least one processor using the second biometric signal algorithm to produce physiological information about the subject.
14. The monitoring device of claim 13 , wherein the pattern associated with the known subject activity mode comprises a first pattern associated with periodic activity and a second pattern associated with lifestyle activity that is different from the first pattern.
15. The monitoring device of claim 14 , wherein the first or second biometric signal extraction algorithm is configured based on noise associated with the activity characteristic that was identified from processing the motion sensor signals.
16. The monitoring device of claim 14 , wherein, responsive to identification of the activity characteristic based on the second pattern and processing the physiological signal using the second biometric signal extraction algorithm, an accuracy of the physiological information is increased relative to processing the physiological signal using the first biometric signal extraction algorithm.
17. The monitoring device of claim 14 , wherein one of the first type of motion sensor or the second type of motion sensor comprises a multi-axis accelerometer, and wherein the at least one processor is configured to process motion sensor signals to identify an activity characteristic of the subject by performing operations comprising:
filtering a signal from at least one axis of the accelerometer over a given time period;
producing an accelerometer spectrum using the signal that was filtered; and
determining if one or more frequencies in the accelerometer spectrum match the pattern associated with the known subject activity mode.
18. The monitoring device of claim 13 , wherein the physiological sensor is a photoplethysmography (PPG) sensor, and wherein the first biometric signal extraction algorithm and the second biometric signal extraction algorithm are configured to produce subject biometric information from signals produced by the PPG sensor, wherein the biometric information includes one or more of the following: subject heart rate, subject respiration rate, subject respiratory rate interval (RRi), subject heart rate variability (HRV), subject blood pressure, subject blood analyte levels, subject cardiovascular properties.
19. The monitoring device of claim 13 , wherein the monitoring device is configured to be positioned at or within an ear of the subject, secured to an appendage of the subject, or worn on a bodily location of the subject.
20. The monitoring device of claim 13 , wherein the operations further comprise:
monitoring the physiological sensor signals and/or the motion sensor signals; and
detecting a change in activity of the subject responsive to the monitoring,
wherein the processing of the motion sensor signals to identify the activity characteristic of the subject is responsive to detecting the change in activity.
21. A method of monitoring a subject via a monitoring device, wherein the monitoring device includes a physiological sensor and a plurality of motion sensors comprising a first type of motion sensor and a second type of motion sensor, each in communication with at least one processor, the method comprising:
receiving, via the at least one processor, a physiological sensor signal from the physiological sensor and/or a motion sensor signal from at least one of the plurality of motion sensors;
detecting, via the at least one processor, a change in activity of the subject responsive to the receiving;
in response to an identified activity characteristic being classified within a first category:
operating the first type of motion sensor to determine first motion sensor data;
selecting by the at least one processor, the first type of motion sensor as a noise reference; and
processing, by the at least one processor, the physiological sensor signal using the selected first type of sensor to remove noise from the physiological sensor signal; and
in response to the identified activity characteristic being classified within a second category:
operating the second type of motion sensor to determine second motion sensor data;
selecting by the at least one processor, the second type of motion sensor, different from the first, as the noise reference; and
processing, by the at least one processor, the physiological sensor signal using the selected second type of sensor to remove noise from the physiological sensor signal.
22. The method of claim 21 , wherein the plurality of motion sensors comprises first and second motion sensors, wherein the first and second motion sensors are different types of sensors.
23. The method of claim 22 , wherein the physiological sensor comprises an optical sensor, and wherein the second motion sensor is configured to provide a signal response that more closely corresponds to motion-related optical noise present in the physiological sensor signal than the first motion sensor.
24. The method of claim 22 , wherein the first motion sensor comprises an inertial sensor, and wherein the second motion sensor comprises an optomechanical sensor.
25. The method of claim 21 , wherein body motion information contained in the noise reference is greater than blood flow information contained therein.
26. The method of claim 21 , wherein the noise reference comprises information associated with periodic or non-periodic motion of the subject and is substantially free of information associated with blood flow of the subject.
27. The method of claim 21 , wherein the physiological information includes one or more of the following: subject heart rate, subject respiration rate, subject RR-interval (RRi), subject heart rate variability (HRV), subject blood pressure, subject blood analyte levels, subject cardiovascular properties.