Systems and methods for computationally efficient non-invasive blood quality measurement
In some embodiments, a system comprises a wearable member configured to receive first energy, generate first signals, select a first subset of the time series data, determine a standard deviation indicating a quality at a low percentile of values associated with first windows of the time series data and a high percentile of values associated with the first windows of the time series data, the signal assessment threshold being including at least the values of the lower percentile, receive second energy, generate second signals containing second time series data from the second energy, select a second subset of the time series data to assess, compare all or part of the second signals to the signal assessment threshold, and if all or part of the second signals include the second time series data that is within the values of the lower percentile, then remove the second signals from further processing.
1. A method comprising:
receiving, from a wearable sensor device, a first energy from at least one light emitting diode (LED) of the wearable sensor device, the first energy being at a wavelength to take a physiological measurement;
generating, by the wearable sensor device, first signals containing first time series data from the first energy;
selecting, by the wearable sensor device, a first subset of the first time series data to generate a signal assessment threshold;
determining a standard deviation indicating a quality of the signal assessment threshold at a low percentile of values associated with a first window of the first time series data and at a high percentile of values associated with the first window of the first time series data, the signal assessment threshold being including at least the values of the lower percentile;
receiving, from the wearable sensor device, a second energy from the at least one LED of the wearable sensor device, the second energy being at the wavelength to take the physiological measurement of a first wearer of the wearable sensor device;
generating, by the wearable sensor device, second signals containing second time series data from the second energy;
selecting a second subset of the second time series data to assess prior to processing the second time series data of the second subset and other second time series data taken near or at a same time as the generation of the second signals;
comparing all or part of the second signals to the signal assessment threshold;
if all or part of the second signals include the second time series data that is within the values of the lower percentile based on the signal assessment threshold, then removing the second signals from further processing; and
determining a photoplethysmogram signal of a user based on the signals.
2. The method of claim 1 , wherein if all or part of the second signals include second time series data that is within the values of the upper percentile based on the signal assessment threshold, then removing the second signals from further processing.
3. The method of claim 1 , wherein if all or part of the second signals include second time series data that is not within the values of the upper percentile and not within the values of the lower percentile based on the signal assessment threshold, then processing the second signals to take the physiological measurement.
4. The method of claim 1 , wherein the physiological measurement is a respiratory rate.
5. The method of claim 1 , wherein the physiological measurement is a Sp02 measurement.
6. The method of claim 1 , wherein the first energy is taken of a second wearer of the wearable sensor device.
7. The method of claim 1 , wherein if all or part of the second signals include the second time series data that is above the values of the lower percentile based on the signal assessment threshold, then processing the second signals to make measurements.
8. The method of claim 1 , wherein if all or part of the second signals include the second time series data that is above the values of the lower percentile based on the signal assessment threshold, then processing third signals to make measurements, the third signals being generated by a location of the first wearer associated with a low signal-to-noise ratio (SNR).
9. The method of claim 1 , wherein determining the standard deviation indicating the quality of the signal assessment threshold at the low percentile of values associated with the first window of the first time series data and the high percentile of values associated with the first window of the first time series data comprises computing a normalized standard deviation of the first window:
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10. A system comprising:
a wearable member including a processor and a memory to store executable instructions, the instructions being executable to:
receive a first energy from at least one light emitting diode (LED) of a wearable sensor device, the first energy being at a wavelength to take a physiological measurement;
generate first signals containing first time series data from the first energy;
select a first subset of the first time series data to generate a signal assessment threshold;
determine a standard deviation indicating a quality of the signal assessment threshold at a low percentile of values associated with a first window of the first time series data and at a high percentile of values associated with the first window of the first time series data, the signal assessment threshold being including at least the values of the lower percentile;
receive a second energy from the at least one LED of the wearable sensor device, the second energy being at the wavelength to take the physiological measurement of a first wearer of the wearable sensor device;
generate second signals containing second time series data from the second energy;
select a second subset of the second time series data to assess prior to processing the second time series data of the second subset and other second time series data taken near or at a same time as the generation of the second signals;
compare all or part of the second signals to the signal assessment threshold;
if all or part of the second signals include the second time series data that is within the values of the lower percentile based on the signal assessment threshold, then remove the second signals from further processing; and
determine a photoplethysmogram signal of a user based on the signals.
11. The system of claim 10 , wherein if all or part of the second signals include second time series data that is within the values of the upper percentile based on the signal assessment threshold, then remove the second signals from further processing.
12. The system of claim 10 , wherein if all or part of the second signals include second time series data that is not within the values of the upper percentile and not within the values of the lower percentile based on the signal assessment threshold, then process the second signals to take the physiological measurement.
13. The system of claim 10 , wherein the physiological measurement is a respiratory rate.
14. The system of claim 10 , wherein the physiological measurement is a Sp02 measurement.
15. The system of claim 10 , wherein the first energy is taken of a second wearer of the wearable sensor device.
16. The system of claim 10 , wherein if all or part of the second signals include the second time series data that is above the values of the lower percentile based on the signal assessment threshold, then process the second signals to make measurements.
17. The system of claim 10 , wherein if all or part of the second signals include the second time series data that is above the values of the lower percentile based on the signal assessment threshold, then process the third signals to make measurements, the third signals being generated by a location of the first wearer associated with a low signal-to-noise ratio (SNR).
18. The system of claim 10 , wherein determining the standard deviation indicating the quality of the signal assessment threshold at the low percentile of values associated with the first window of the first time series data and the high percentile of values associated with the first window of the first time series data comprises computing a normalized standard deviation of the first window:
σ
s
=
1
N
Σ
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(
x
i
-
x
)
2
x
Where:
x
=
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∑
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.
19. A system comprising:
a processor; and
a memory storing instructions that, when executed by the processor, cause the processor to:
receive a first energy from at least one light emitting diode (LED) of a wearable sensor device, the first energy being at a wavelength to take a physiological measurement;
generate first signals containing first time series data from the first energy;
select a first subset of the first time series data to generate a signal assessment threshold;
determine a standard deviation indicating a quality of the signal assessment threshold at a low percentile of values associated with a first window of the first time series data and at a high percentile of values associated with the first window of the first time series data, the signal assessment threshold being including at least the values of the lower percentile;
receive a second energy from the at least one LED of the wearable sensor device, the second energy being at the wavelength to take the physiological measurement of a first wearer of the wearable sensor device;
generate second signals containing second time series data from the second energy;
select a second subset of the second time series data to assess prior to processing the second time series data of the second subset and other second time series data taken near or at a same time as the generation of the second signals;
compare all or part of the second signals to the signal assessment threshold;
if all or part of the second signals include the second time series data that is within the values of the lower percentile based on the signal assessment threshold, then remove the second signals from further processing; and
determine a photoplethysmogram signal of a user based on the signals.