US20220039661A1 - Personal Warning Temperature (PWT) - Google Patents
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- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
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- A—HUMAN NECESSITIES
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- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/01—Measuring temperature of body parts ; Diagnostic temperature sensing, e.g. for malignant or inflamed tissue
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- A—HUMAN NECESSITIES
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- A—HUMAN NECESSITIES
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- G16H40/00—ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
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Definitions
- the invention relates to the field of medical screening and health monitoring; and particularly to reduce the spread of COVID-19 and other infectious diseases.
- the invention reduces or prevents the spread of infectious diseases by enabling the early detection of body temperature abnormalities based on personal warning temperatures that are otherwise not noticed by widely accepted minimum temperature thresholds for identification of fever.
- Fever is defined as having a body temperature above the normal range.
- body temperature naturally varies for a variety of factors such as sex, time of day, current metabolic activity, ambient temperature, and biological events.
- body temperature may become elevated as fever is a common, natural response to infection. It is helpful to know the expected body temperature of an individual so that a person can determine whether or not they are healthy.
- CDC US Centers for Disease Control
- COVID-19 people often have very mild symptoms, or symptoms may be delayed. Even though fever is a significant symptom for COVID-19, people with elevated temperatures are often considered asymptomatic if their fever fails to rise to 100.4 degrees Fahrenheit. However, for COVID-19, defining a fever as 100.4 degrees Fahrenheit is hindering management of the pandemic. The reason is that people believe that they are not sick when they are in fact sick. Because people with COVID-19 believe they are not sick, they interact with other persons and unintentionally infect them as well. COVID-19 is not the only infectious disease that sometimes displays mild symptoms. Furthermore, unlike many other diseases, COVID-19 symptoms may be delayed. As a result, early warning of covid-19 identification is very important; yet further hindered by defining a fever as 100.4 degrees Fahrenheit or higher.
- the Personal Warning Temperature (PWT) method of the present invention overcomes the deficiencies of prior-art methods for defining a fever, by creating an individualized personal warning temperature for every person using a set of their own body temperature measurements taken when they are healthy. It is commonly known that normal body temperature varies by individual for a variety of reasons; perhaps including body weight, age and metabolism. As a result, fever, which is a body temperature higher than normal, also varies between individuals. For particular individuals, PWT uses statistical measures such as average (or mean) and standard deviation to define a personal warning temperature, which defines a fever for that specific person. PWT assumes normal body temperature taken over time is normally distributed in the statistical sense, and it uses some multiple of standard deviation above the person's average normal temperature in order to define the personal warning temperature for the specific individual.
- body temperature is often part of an exemplary embodiment.
- body temperature is not necessary in order to calculate or display a personal channel of expected normal body temperatures in order to calculate or know a person's personal warning temperature. Displaying the channel also facilitates understanding and is often displayed on a graph, along with the personal warning temperature of the individual.
- Your personal warning temperature is more accurate and usually lower than the traditional definition of a fever such as 100.4 degrees Fahrenheit. This results in the feasibility of early warning when contracting a disease.
- FIG. 1 provides a graphical representation of a temperature chart with the traditional fever temperature bar of 100.4 Fahrenheit.
- FIG. 2 provides a graphical representation of the results of an individual taking their personal temperature over time.
- FIG. 3 shows a graphical representation of computing a personal temperature average, standard deviation channel, and personal warning temperature.
- FIG. 4 shows a graphical representation of personal temperatures taken with the recorded time of day.
- FIG. 5 shows a graphical representation of using personal temperatures collected and grouped in the morning; then computing averages, standard deviation, and a new lower personal warning temperature.
- FIG. 6 shows a graphical representation of using personal temperatures collected and grouped in the evening; then computing averages, standard deviation, and a new higher personal warning temperature.
- FIG. 7 shows a graphical representation of grouping an individual's personal temperatures by time of day modeling the normal temperature rise and fall thought the day; and calculating the averages by time such as each hour and the new personal warning temperature based on time of day.
- Element 1 represents a graphical representation of temperatures taken by an individual; warning lines, and timelines.
- Element 2 represents the standard used fever temperature of 100.4 line.
- Element 3 is a timeline in days.
- Element 4 is a temperature scale.
- Element 20 are personal temperatures taken by an individual.
- Element 31 represents the lower bound of the personal temperature standard deviation channel (SDC).
- SDC personal temperature standard deviation channel
- Element 32 represents the average personal temperature line (APT).
- Element 33 represents the upper bound of the personal temperature standard deviation channel (SDC).
- SDC personal temperature standard deviation channel
- Element 34 represents the personal warning temperature (PWT) line.
- Element 40 represents personal temperatures taken in the morning.
- Element 41 represents personal temperatures taken in the evening.
- Element 52 represents the new average personal temperature line (APT) based only on morning temperatures.
- Element 54 represents the new personal warning temperature (PWT) line based on only morning temperatures.
- Element 55 represents the change in the personal warning temperature line based on using only morning temperatures.
- Element 56 represents the change in average personal temperature (APT) based only on evening temperatures.
- Element 62 represents the new average personal temperature line (APT) base only on morning temperatures.
- Element 64 represents the new personal warning temperature (PWT) line based on only evening temperatures.
- Element 65 represents the change in the personal warning temperature line based using only evening temperatures.
- Element 66 represents the change in average personal temperature (APT) based only on evening temperatures.
- Element 70 represents time of day that the personal temperatures were taken.
- Element 71 represents the average temperature based on time of day that temperature was taken.
- Element 72 represents the personal warning temperature (PWT) line based on time of day that temperature was taken.
- PWT personal warning temperature
- a healthy person's body temperature measurements are used to calculate an average (or mean) temperature; as well as a standard deviation.
- a multiplier (M1) of the standard deviation is used to define a channel of expected normal temperatures, which are defined by lower and upper temperature bounds which are above and below the average temperature by the same magnitude. The multiplier is selected so that nearly all the healthy person's body temperatures are within the channel. It is well known that, for normally distributed data, such as healthy body temperature, appear to be, about 95% of measurements are within 2 standard deviations from the average. Therefore, in an exemplary embodiment, the channel multiplier M1 is 2. Further, the person's personal warning temperature is calculated using the average, the standard deviation, and another multiplier M2 such that the personal warning temperature is M2 times the standard deviation above the average temperature.
- the channel of expected normal temperatures for the healthy person is defined by the lower bound, L, and the upper bound, U, and the personal warning temperature, W, is defined as:
- An exemplary embodiment of PWT analyzes a healthy person's body temperature measurements in order to correct for time-of-day differences or interpret a body temperature measurement in order to take time-of-day into account.
- Measurement methods also affect the body temperature value. For example, different thermometers may measure the body temperature differently; and, in addition to time-of-day, the location on the body that the temperature is taken also affects the value.
- the particular measurement device utilized and method is not as important as making sure the temperature is measured in a systematic and consistent way every time, so that the set of measurements are precise, if not accurate. It is not so important what the particular temperature level is. Instead, it is only important to understand what the normal range of temperatures is when healthy; have relatively small variation in the range of normal temperatures, and to know when the temperature rises above the normal range of values, indicating possible illness.
- a healthy person's body temperature measurements are plotted on a graph against the time of measurement along with the channel of expected normal temperatures and the personal warning temperature to facilitate understanding of body temperature history; and observe very clearly when a particular, future body temperature, rises above the personal warning temperature.
- FIG. 1 illustrates a graph ( 1 ) of body temperature, measured in degrees Fahrenheit ( 4 ), with respect to time, measured in elapsed days ( 3 ), used in an embodiment. Other exemplary embodiments use other temperature or time units. There is no body temperature illustrated in FIG. 1 . However, the traditional definition of the fever threshold, 100.4 degrees Fahrenheit ( 2 ), is shown.
- FIG. 2 illustrates the same graph ( 1 ) of FIG. 1 but now with some body temperature measurements ( 20 ) added for the temperature of an individual.
- PWT requires at least two body temperature measurements for the person, when healthy, to calculate non-trivial values for the average body temperature and standard deviation.
- PWT uses a few body temperature measurements to calculate a non-changing channel and personal warning temperature.
- the channel and personal warning temperature are calculated from all normal body temperature measurements, a moving time window of normal body temperature measurements, or some set of normal and/or all body temperature measurements.
- FIG. 3 illustrates the graph ( 1 ) of FIG. 1 with the average body temperature ( 32 ), the lower ( 31 ) and upper ( 33 ) bounds defining the expected normal temperature channel for the person when healthy, and the person's personal warning temperature ( 34 ).
- FIG. 4 illustrates the graph ( 1 ) of FIG. 1 with at least 20 days of the person's body temperature data, now distinguished by whether the temperature measurement was obtained in the morning ( 40 ) or evening ( 4 ). Because of circadian rhythm, body temperature is often higher in the evening than it is in the morning, assuming the person sleeps at night as most people do (if not, then a different but explainable pattern can be observed).
- the person measures body temperature at the same time each day to remove the time-of-day effects.
- the person uses a morning hour each day.
- the person uses an evening hour each day.
- the person uses both a morning hour and an evening hour.
- FIG. 5 shows only body temperature measurements ( 40 ) of the person for the morning hour.
- the average ( 52 ) considering only morning-hour body temperature measurement ( 40 ) will be lower than the average considering all body temperature measurements ( 40 and 41 ) in FIG. 4 .
- the personal warning temperature ( 54 ) calculated using only morning-hour body temperature measurement ( 40 ) will be lower than the personal warning temperature ( 34 ) considering all body temperature measurements ( 40 and 41 ) in FIG. 4 and also shown in FIG. 5 ( 34 ), the difference being shown ( 55 ).
- FIG. 6 shows only body temperature measurements ( 41 ) of the person for the evening hour.
- the average ( 62 ) considering only evening-hour body temperature measurements will be higher than the average considering all body temperature measurements ( 40 and 41 ) in FIG. 4 .
- the personal warning temperature ( 64 ) calculated using only evening-hour body temperature measurement ( 41 ) will be higher than the personal warning temperature ( 34 ) considering all body temperature measurements ( 40 and 41 ) in FIG. 4 and also shown in FIG. 6 ( 34 ), the difference being shown ( 65 ).
- FIG. 7 shows the result of analyzing the person's body temperature measurements and correcting the channel and personal warning temperature for the effect of time-of-day variations expected for a person's body temperature. It is commonly known that a person's body temperature varies throughout the day in a phenomenon known as circadian rhythm. There are two ways to address time-of-day expected variations in body temperature. In an embodiment, indicated by FIG. 7 , the personal warning temperature and channel are modified to enable correct interpretation of the body temperature measured. In another exemplary embodiment, the measured body temperature is adjusted for time-of-day expected variations and plotted on a graph with fixed values defining the channel and personal warning temperature.
- a healthy person's body temperature measurements are analyzed, using machine learning or an equivalent technique, to determine how other factors affect body temperature and calculates a correction or method of interpreting body temperature in the context of those other factors.
Abstract
Description
- NONE
- NONE
- The invention relates to the field of medical screening and health monitoring; and particularly to reduce the spread of COVID-19 and other infectious diseases. The invention reduces or prevents the spread of infectious diseases by enabling the early detection of body temperature abnormalities based on personal warning temperatures that are otherwise not noticed by widely accepted minimum temperature thresholds for identification of fever.
- Fever is defined as having a body temperature above the normal range. When a person is healthy, body temperature naturally varies for a variety of factors such as sex, time of day, current metabolic activity, ambient temperature, and biological events. When a person is not healthy, body temperature may become elevated as fever is a common, natural response to infection. It is helpful to know the expected body temperature of an individual so that a person can determine whether or not they are healthy. There have been many medical studies conducted to determine the average body temperature and the average fever temperature, or the range of these temperatures. For purposes of simplicity, the US Centers for Disease Control (CDC) advise that a person who has a temperature of 100.4 degrees Fahrenheit or above has a fever. That value of 100.4 degrees Fahrenheit comes from research published in 1868 by the German doctor Carl Reinhold August Wunderlich. Today, defining a fever as 100.4 degrees Fahrenheit is considered a bit incorrect. However, for many infectious diseases, there is a large difference between normal and fever temperatures, so this definition is often useful anyway.
- For COVID-19, people often have very mild symptoms, or symptoms may be delayed. Even though fever is a significant symptom for COVID-19, people with elevated temperatures are often considered asymptomatic if their fever fails to rise to 100.4 degrees Fahrenheit. However, for COVID-19, defining a fever as 100.4 degrees Fahrenheit is hindering management of the pandemic. The reason is that people believe that they are not sick when they are in fact sick. Because people with COVID-19 believe they are not sick, they interact with other persons and unintentionally infect them as well. COVID-19 is not the only infectious disease that sometimes displays mild symptoms. Furthermore, unlike many other diseases, COVID-19 symptoms may be delayed. As a result, early warning of covid-19 identification is very important; yet further hindered by defining a fever as 100.4 degrees Fahrenheit or higher.
- The current state of the art is insufficient to control the spread of the coronavirus and other infectious diseases. It is based on old, outdated data and people are grasping at non-medical, non-FDA approved, very expensive, and flashy technologies that do not reach the resolution for finding and identifying mild symptoms.
- The Personal Warning Temperature (PWT) method of the present invention overcomes the deficiencies of prior-art methods for defining a fever, by creating an individualized personal warning temperature for every person using a set of their own body temperature measurements taken when they are healthy. It is commonly known that normal body temperature varies by individual for a variety of reasons; perhaps including body weight, age and metabolism. As a result, fever, which is a body temperature higher than normal, also varies between individuals. For particular individuals, PWT uses statistical measures such as average (or mean) and standard deviation to define a personal warning temperature, which defines a fever for that specific person. PWT assumes normal body temperature taken over time is normally distributed in the statistical sense, and it uses some multiple of standard deviation above the person's average normal temperature in order to define the personal warning temperature for the specific individual.
- Although it is not necessary to display body temperatures in a time-based graph in any specific exemplary embodiment, doing so facilitates understanding. Therefore, body temperature is often part of an exemplary embodiment. Similarly, body temperature is not necessary in order to calculate or display a personal channel of expected normal body temperatures in order to calculate or know a person's personal warning temperature. Displaying the channel also facilitates understanding and is often displayed on a graph, along with the personal warning temperature of the individual.
- Not only is a personal warning temperature more accurate at identifying fever, it also triggers earlier because it is generally lower than the traditional definition of 100.4 degrees Fahrenheit. By accurately defining a fever and identifying a fever earlier, people with the potential to infect others with disease can take appropriate action at an earlier date, in order to avoid or reduce the chances of infecting others.
- Knowing your personal warning temperature, and specifically knowing when your body temperature measurement rises above your personal warning temperature, creates a new capability to provide early warning of possible illness, infectiousness, and ability to appropriately react in a timely manner, in order to help manage an epidemic, pandemic, or other health crisis with visibility that has never been known before. Your personal warning temperature is more accurate and usually lower than the traditional definition of a fever such as 100.4 degrees Fahrenheit. This results in the feasibility of early warning when contracting a disease.
- When displaying a person's body temperature data on a graph, it easy to see that, when healthy, the person's body temperature is usually within a channel. It is also relatively easy to determine when the person's body temperature rises above their personal warning temperature. The graph, with the personal warning temperature displayed, removes the need to remember their personal warning temperature.
- In the case of COVID-19 and perhaps other infectious diseases, people with mild symptoms of the disease are not currently being detected when they are infectious; and thus they do not take precautions to avoid infecting others. Temperature screening devices are not identifying these people either because the devices are often set to trigger at 100.4 degrees, which is the traditional definition of fever. PWT can more accurately and more quickly inform people when they are getting sick. If every person knew their personal warning temperature and monitored their body temperature daily, we would be in a much better position to manage the COVID-19 pandemic, and perhaps future pandemics.
-
FIG. 1 provides a graphical representation of a temperature chart with the traditional fever temperature bar of 100.4 Fahrenheit. -
FIG. 2 provides a graphical representation of the results of an individual taking their personal temperature over time. -
FIG. 3 shows a graphical representation of computing a personal temperature average, standard deviation channel, and personal warning temperature. -
FIG. 4 shows a graphical representation of personal temperatures taken with the recorded time of day. -
FIG. 5 shows a graphical representation of using personal temperatures collected and grouped in the morning; then computing averages, standard deviation, and a new lower personal warning temperature. -
FIG. 6 shows a graphical representation of using personal temperatures collected and grouped in the evening; then computing averages, standard deviation, and a new higher personal warning temperature. -
FIG. 7 shows a graphical representation of grouping an individual's personal temperatures by time of day modeling the normal temperature rise and fall thought the day; and calculating the averages by time such as each hour and the new personal warning temperature based on time of day. -
Element 1 represents a graphical representation of temperatures taken by an individual; warning lines, and timelines. -
Element 2 represents the standard used fever temperature of 100.4 line. -
Element 3 is a timeline in days. -
Element 4 is a temperature scale. -
Element 20 are personal temperatures taken by an individual. -
Element 31 represents the lower bound of the personal temperature standard deviation channel (SDC). -
Element 32 represents the average personal temperature line (APT). -
Element 33 represents the upper bound of the personal temperature standard deviation channel (SDC). -
Element 34 represents the personal warning temperature (PWT) line. -
Element 40 represents personal temperatures taken in the morning. -
Element 41 represents personal temperatures taken in the evening. -
Element 52 represents the new average personal temperature line (APT) based only on morning temperatures. -
Element 54 represents the new personal warning temperature (PWT) line based on only morning temperatures. -
Element 55 represents the change in the personal warning temperature line based on using only morning temperatures. -
Element 56 represents the change in average personal temperature (APT) based only on evening temperatures. -
Element 62 represents the new average personal temperature line (APT) base only on morning temperatures. -
Element 64 represents the new personal warning temperature (PWT) line based on only evening temperatures. -
Element 65 represents the change in the personal warning temperature line based using only evening temperatures. -
Element 66 represents the change in average personal temperature (APT) based only on evening temperatures. -
Element 70 represents time of day that the personal temperatures were taken. -
Element 71 represents the average temperature based on time of day that temperature was taken. -
Element 72 represents the personal warning temperature (PWT) line based on time of day that temperature was taken. - In an exemplary embodiment of the Personal Warning Temperature (PWT) of the present invention, a healthy person's body temperature measurements are used to calculate an average (or mean) temperature; as well as a standard deviation. A multiplier (M1) of the standard deviation, is used to define a channel of expected normal temperatures, which are defined by lower and upper temperature bounds which are above and below the average temperature by the same magnitude. The multiplier is selected so that nearly all the healthy person's body temperatures are within the channel. It is well known that, for normally distributed data, such as healthy body temperature, appear to be, about 95% of measurements are within 2 standard deviations from the average. Therefore, in an exemplary embodiment, the channel multiplier M1 is 2. Further, the person's personal warning temperature is calculated using the average, the standard deviation, and another multiplier M2 such that the personal warning temperature is M2 times the standard deviation above the average temperature.
- In mathematical terms representing the exemplary embodiment, if the average body temperature is A, and the standard deviation is Sd, then the channel of expected normal temperatures for the healthy person is defined by the lower bound, L, and the upper bound, U, and the personal warning temperature, W, is defined as:
-
L=A−(M1*Sd) -
U=A+(M1*Sd) -
W=A+(M2*Sd) - Many factors affect an individual's personal warning temperature such as time-of-day. An exemplary embodiment of PWT analyzes a healthy person's body temperature measurements in order to correct for time-of-day differences or interpret a body temperature measurement in order to take time-of-day into account.
- Measurement methods also affect the body temperature value. For example, different thermometers may measure the body temperature differently; and, in addition to time-of-day, the location on the body that the temperature is taken also affects the value. For the determination of fever, however, the particular measurement device utilized and method is not as important as making sure the temperature is measured in a systematic and consistent way every time, so that the set of measurements are precise, if not accurate. It is not so important what the particular temperature level is. Instead, it is only important to understand what the normal range of temperatures is when healthy; have relatively small variation in the range of normal temperatures, and to know when the temperature rises above the normal range of values, indicating possible illness.
- In an exemplary embodiment of PWT, a healthy person's body temperature measurements are plotted on a graph against the time of measurement along with the channel of expected normal temperatures and the personal warning temperature to facilitate understanding of body temperature history; and observe very clearly when a particular, future body temperature, rises above the personal warning temperature.
-
FIG. 1 illustrates a graph (1) of body temperature, measured in degrees Fahrenheit (4), with respect to time, measured in elapsed days (3), used in an embodiment. Other exemplary embodiments use other temperature or time units. There is no body temperature illustrated inFIG. 1 . However, the traditional definition of the fever threshold, 100.4 degrees Fahrenheit (2), is shown. -
FIG. 2 illustrates the same graph (1) ofFIG. 1 but now with some body temperature measurements (20) added for the temperature of an individual. PWT requires at least two body temperature measurements for the person, when healthy, to calculate non-trivial values for the average body temperature and standard deviation. InFIG. 2 , there are ten body temperature measurements (20) taken at different times. In an exemplary embodiment, PWT uses a few body temperature measurements to calculate a non-changing channel and personal warning temperature. In another exemplary embodiment, the channel and personal warning temperature are calculated from all normal body temperature measurements, a moving time window of normal body temperature measurements, or some set of normal and/or all body temperature measurements. -
FIG. 3 illustrates the graph (1) ofFIG. 1 with the average body temperature (32), the lower (31) and upper (33) bounds defining the expected normal temperature channel for the person when healthy, and the person's personal warning temperature (34). -
FIG. 4 illustrates the graph (1) ofFIG. 1 with at least 20 days of the person's body temperature data, now distinguished by whether the temperature measurement was obtained in the morning (40) or evening (4). Because of circadian rhythm, body temperature is often higher in the evening than it is in the morning, assuming the person sleeps at night as most people do (if not, then a different but explainable pattern can be observed). In an exemplary embodiment, the person measures body temperature at the same time each day to remove the time-of-day effects. In another exemplary embodiment, the person uses a morning hour each day. In yet another exemplary embodiment, the person uses an evening hour each day. In yet a further exemplary embodiment, the person uses both a morning hour and an evening hour. -
FIG. 5 shows only body temperature measurements (40) of the person for the morning hour. In an embodiment, the average (52) considering only morning-hour body temperature measurement (40) will be lower than the average considering all body temperature measurements (40 and 41) inFIG. 4 . Similarly, the personal warning temperature (54), calculated using only morning-hour body temperature measurement (40) will be lower than the personal warning temperature (34) considering all body temperature measurements (40 and 41) inFIG. 4 and also shown inFIG. 5 (34), the difference being shown (55). -
FIG. 6 shows only body temperature measurements (41) of the person for the evening hour. In an embodiment, the average (62) considering only evening-hour body temperature measurements will be higher than the average considering all body temperature measurements (40 and 41) inFIG. 4 . Similarly, the personal warning temperature (64), calculated using only evening-hour body temperature measurement (41) will be higher than the personal warning temperature (34) considering all body temperature measurements (40 and 41) inFIG. 4 and also shown inFIG. 6 (34), the difference being shown (65). -
FIG. 7 shows the result of analyzing the person's body temperature measurements and correcting the channel and personal warning temperature for the effect of time-of-day variations expected for a person's body temperature. It is commonly known that a person's body temperature varies throughout the day in a phenomenon known as circadian rhythm. There are two ways to address time-of-day expected variations in body temperature. In an embodiment, indicated byFIG. 7 , the personal warning temperature and channel are modified to enable correct interpretation of the body temperature measured. In another exemplary embodiment, the measured body temperature is adjusted for time-of-day expected variations and plotted on a graph with fixed values defining the channel and personal warning temperature. - There are several well-known methods for modeling the time-of-day expected variations in body temperature. For example, normal body temperature measurements can be used to find a mathematical function that fits the curve to the data when plotted on a graph. Alternatively, machine learning methods may be used to define a function.
- There may be other identifiable factors that affect body temperature measurement in predictable ways. In an exemplary embodiment of PWT, a healthy person's body temperature measurements are analyzed, using machine learning or an equivalent technique, to determine how other factors affect body temperature and calculates a correction or method of interpreting body temperature in the context of those other factors.
- Although different exemplary embodiments have been shown and described, other exemplary embodiments would be readily understood by an artisan. The claims are not to be limited by the embodiments disclosed but rather by the scope of the appended claims.
Claims (20)
Priority Applications (5)
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Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20220110384A1 (en) * | 2020-10-14 | 2022-04-14 | Antionette Olivarez | Protective temperature-sensing face shield with temperature display |
Family Cites Families (36)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US6821249B2 (en) * | 1999-03-08 | 2004-11-23 | Board Of Regents, The University Of Texas | Temperature monitoring of congestive heart failure patients as an indicator of worsening condition |
US6838993B2 (en) * | 2002-02-22 | 2005-01-04 | Bioalert Systems, Inc. | Early warning system and methods for detection of a bioterrorism event |
DK1734858T3 (en) * | 2004-03-22 | 2014-10-20 | Bodymedia Inc | NON-INVASIVE TEMPERATURE MONITORING DEVICE |
US20070161921A1 (en) * | 2006-01-07 | 2007-07-12 | Rausch Jeffrey L | Bio-accurate temperature measurement device and method of quantitatively normalizing a body temperature measurement to determine a physiologically significant temperature event |
IL178673A0 (en) * | 2006-04-03 | 2007-02-11 | Ilan Menashe | Improved digital thermometer |
US8160836B2 (en) | 2007-04-17 | 2012-04-17 | Exergen Corporation | Wireless transmission of temperature data for a geographic area |
US20090016404A1 (en) | 2007-07-13 | 2009-01-15 | International Business Machines Corporation | Intelligent thermometer |
US20100042013A1 (en) * | 2008-08-17 | 2010-02-18 | Innovatec Sl | System and apparatus for wireless high-frequency temperature acquisition and analysis |
US9470584B2 (en) * | 2010-05-27 | 2016-10-18 | Exergen Corporation | Method and apparatus for accurate detection of fever |
US8682417B2 (en) * | 2010-10-26 | 2014-03-25 | Intermountain Invention Management, Llc | Heat-related symptom detection systems and methods |
US8593251B2 (en) * | 2011-06-16 | 2013-11-26 | Alan Camerik Heller | Systems for detecting a febrile condition and reducing risks of spreading infection |
US8834389B2 (en) | 2011-11-25 | 2014-09-16 | Tepsync | Temperature based fertility monitoring system and related method |
US9041530B2 (en) | 2012-04-18 | 2015-05-26 | Qualcomm Incorporated | Biometric attribute anomaly detection system with adjusting notifications |
US9183738B1 (en) | 2012-04-19 | 2015-11-10 | iDevices, LLC | Wireless thermometer and method of use thereof |
US20140266694A1 (en) | 2013-03-15 | 2014-09-18 | Samuel Greyson McCluskey | Body Temperature Warning System |
US9835496B2 (en) * | 2013-04-08 | 2017-12-05 | Ann Balboni | Fever alert system |
US10295413B2 (en) * | 2013-04-08 | 2019-05-21 | Ann Balboni | Fever alert system |
US20200196962A1 (en) | 2018-12-23 | 2020-06-25 | Scanadu Incorporated | Systems, methods, and apparatus for personal and group vital signs curves |
US20190350535A1 (en) | 2013-09-09 | 2019-11-21 | Wenyi Zhao | Systems, methods, and apparatus for personal and group vital signs curves |
US20150316419A1 (en) | 2014-05-03 | 2015-11-05 | Seenath Punnakkal | Method and apparatus for monitoring body temperature and activity |
US20160331244A1 (en) * | 2015-05-12 | 2016-11-17 | Razzberry Inc. | Core body temperature system |
US10184844B2 (en) * | 2015-12-31 | 2019-01-22 | Withings | Compact home thermometer |
EP3402391A4 (en) | 2016-01-12 | 2019-07-24 | Tempdrop Ltd | System and method for determining physiological condition based on variations in body temperature |
US10582908B2 (en) * | 2016-04-01 | 2020-03-10 | Intel Corporation | Sleep management device and methods for operation |
WO2017201323A1 (en) * | 2016-05-18 | 2017-11-23 | Massachusetts Institute Of Technology | Methods and systems for pre-symptomatic detection of exposure to an agent |
WO2018033799A1 (en) * | 2016-08-19 | 2018-02-22 | Thalman Health Ltd. | Method and system for determination of core body temperature |
CN109196595A (en) * | 2017-04-03 | 2019-01-11 | 前田商事株式会社 | Software, health condition judging device and health condition judging method |
JP6711456B2 (en) * | 2017-04-27 | 2020-06-17 | 株式会社村田製作所 | Body temperature measuring device |
US11213252B2 (en) | 2017-10-20 | 2022-01-04 | Starkey Laboratories, Inc. | Devices and sensing methods for measuring temperature from an ear |
US20190192010A1 (en) * | 2017-12-22 | 2019-06-27 | Viraj Mane | Detection of flu using thermal imaging |
CN110013230A (en) * | 2018-01-08 | 2019-07-16 | 曹亮 | A kind of temperature measuring equipment and method using multiple infrared sensors |
US10841303B2 (en) * | 2018-04-12 | 2020-11-17 | Bank Of America Corporation | Apparatus and methods for micro-segmentation of an enterprise internet-of-things network |
CN114727764A (en) * | 2019-10-07 | 2022-07-08 | 蓝色火花科技有限公司 | System and method for using body temperature recording patch |
US20210186399A1 (en) * | 2019-12-19 | 2021-06-24 | Medtronic, Inc. | Urinary tract infection determination |
WO2021202922A1 (en) * | 2020-04-02 | 2021-10-07 | Anapole Technologies Inc. | Real time electronic detection/diagnostic device for covid-19 and other diseases |
US11393594B2 (en) * | 2020-06-10 | 2022-07-19 | Shane Harrah | System and method for detecting presence of illness symptoms |
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---|---|---|---|---|
US20220110384A1 (en) * | 2020-10-14 | 2022-04-14 | Antionette Olivarez | Protective temperature-sensing face shield with temperature display |
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