HK1222363B - Electronic thermometer - Google Patents

Description

electronic thermometer

Technical Field

The present invention relates to an electronic thermometer.

Background Art

Typically, in electronic thermometers, the prediction calculation starts when the measured value exceeds a specified value and the temperature rise rate exceeds a specified value. The prediction is established when the change in the predicted equilibrium temperature falls within the specified value. For example, if the predicted value is Y, the measured value is T, and the increase is U, the prediction formula is Y = T + U. For example, if t is the time elapsed from the prediction start point, the increase U can be calculated as U = a1 × dT/dt + b1, or U = (a2 × t + b2) × dT + (c2 × t + d2). Here, a1, b1, a2, b2, c2, and d2 are pre-set coefficients.

The hypothetical test subject's characteristics are divided into multiple groups, and a calculation formula (prediction formula) for the predicted value Y is determined for each group. More specifically, coefficients are determined for the formula used to calculate the increase U in the predicted value Y = T + U. In this method, the group to which the test subject's current condition belongs is selected from the multiple groups based on the actual measured values, and the increase U is calculated using the formula corresponding to that group.

Patent Document 1: Japanese Patent Application Laid-Open No. 2007-24531

In electronic thermometers, if the actual value detected by the temperature sensor changes abnormally, for example, if a monotonically increasing actual value begins to decrease, or if a temperature increase that was previously converging increases sharply, the predicted value cannot be accurately calculated. Examples of situations in which the actual value changes abnormally include re-clamping the electronic thermometer's temperature sensor under the armpit or suddenly increasing the force holding the temperature sensor.

Patent Document 1 describes a technique that changes the elapsed time t substituted into a prediction formula to 0 even if the actual measured value decreases after measurement begins, provided that specified conditions are met. However, in the electronic thermometer described in Patent Document 1, the candidate prediction formula used when the elapsed time t is changed to 0 upon detection of a decrease in the actual measured value is the same as the candidate prediction formula used when no decrease in the actual measured value is detected. In other words, in the electronic thermometer described in Patent Document 1, whether the elapsed time t is changed to 0 upon detection of a decrease in the actual measured value or the elapsed time t is not detected, one group is selected from the common 12 groups for both cases, and the prediction value is calculated using the prediction formula corresponding to the selected group. Therefore, the accuracy of the prediction value when the elapsed time t is changed to 0 upon detection of a decrease in the actual measured value may decrease, leading to a loss of time before the prediction is established or errors may occur.

Alternatively, from another perspective, the electronic thermometer described in Patent Document 1 sets the elapsed time t to 0 when the measured value begins to rise after it has decreased. However, in this configuration, if the temperature sensor of the electronic thermometer is re-clamped under the armpit and the body's movements are delayed, the measured value may become unstable even after the elapsed time t is set to 0, leading to a high possibility of errors.

Summary of the Invention

The present invention is made based on the recognition of the above-mentioned problems, and its purpose is to provide an electronic thermometer that can more accurately or reliably predict the equilibrium temperature even when the actual measurement value measured by the temperature sensor becomes abnormal temporarily due to body movement, etc.

The electronic thermometer involved in the first aspect of the present invention predicts the equilibrium temperature based on the measured value of the temperature of the measured part detected by the temperature sensor, and is characterized in that it has a processing unit that, when an abnormality occurs in the measured value after the prediction of the equilibrium temperature is started based on the measured value and a first prediction model, predicts the equilibrium temperature based on the measured value and a second prediction model different from the first prediction model after the abnormality is eliminated.

The electronic thermometer according to the second aspect of the present invention predicts the equilibrium temperature based on the actual measured value of the temperature of the measured part detected by the temperature sensor, and is characterized in that it includes a processing unit that stops the prediction of the equilibrium temperature based on the actual measured value if an abnormality occurs in the actual measured value after the prediction of the equilibrium temperature based on the actual measured value is started, and then restarts the prediction of the equilibrium temperature based on the actual measured value in response to the second differential value of the actual measured value changing from positive to negative.

According to the present invention, there is provided an electronic thermometer capable of more accurately or reliably predicting the equilibrium temperature even when the actual measurement value detected by the temperature sensor becomes abnormal temporarily due to body movement or the like.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a diagram showing the appearance of an electronic thermometer according to an embodiment of the present invention.

FIG2 is a block diagram of an electronic thermometer according to an embodiment of the present invention.

FIG3 is a diagram showing grouping according to one embodiment of the present invention.

FIG. 4 is a diagram illustrating a process related to prediction of the equilibrium temperature by a processing unit of the electronic thermometer according to one embodiment of the present invention.

FIG. 5 is a diagram illustrating typical changes in actual values (temperature data) of temperature measured by the temperature sensor when the subject re-holds the temperature sensor between their armpits.

FIG. 6 is a diagram illustrating typical changes in actual values (temperature data) of temperature measured by the temperature sensor when the subject re-holds the temperature sensor between their armpits.

Description of reference numerals: 1 ...electronic thermometer; 2 ...main body shell; 3 ...metal cap.

DETAILED DESCRIPTION

Hereinafter, the present invention will be described by way of the following exemplary embodiments with reference to the drawings.

Figure 1 shows the appearance of an electronic thermometer 1 according to one embodiment of the present invention. The electronic thermometer 1 has a metal cap 3 at the front end of a main body case 2. The portion where the metal cap 3 is located is the temperature measurement unit that contacts the measured area. A display unit 30 is located on one surface of the main body case 2.

Figure 2 shows a block diagram of an electronic thermometer 1. The electronic thermometer 1 includes a temperature sensor 10, a processing unit 20, a display unit 30, a buzzer 40, and a power supply and power switch (not shown). The temperature sensor 10 includes a thermistor 12, a temperature measuring element, located inside a metal cap 3, and a circuit (not shown) that converts the resistance value of the thermistor 12 into temperature data and provides it to the processing unit 20. Temperature data refers to data indicating temperature. In the following description, the "actually measured temperature value" refers to the temperature value indicated by the temperature data.

The processing unit 20 performs processing to predict the equilibrium temperature based on the actual temperature measurement value (temperature data) of the measured area (typically, a person's armpit or oral cavity) measured by the temperature sensor 10. The equilibrium temperature refers to the temperature at which the temperature of the measured area and the temperature of the thermistor 12 reach equilibrium, that is, the temperature of the measured area. Before the temperature of the thermistor 12 reaches the equilibrium temperature, the actual temperature value of the measured area measured by the temperature sensor 10 indicates a temperature lower than the equilibrium temperature.

The processing unit 20 includes, for example, a CPU 22 and a memory 24. The memory 24 includes a nonvolatile memory storing a control program 26 and a RAM for calculation processing. The CPU 22 operates based on the control program 26, thereby realizing the functions of the processing unit 20.

The display unit 30 displays the equilibrium temperature (i.e., the predicted value of the temperature of the measured portion) predicted by the processing unit 20 according to the instruction from the processing unit 20. When the temperature prediction is completed or an error occurs, the buzzer 40 reports the above according to the instruction from the processing unit 20.

The following describes the basic principles of the equilibrium temperature prediction method used by the electronic thermometer 1. The electronic thermometer 1 uses the time when the measured temperature value of the measured portion of the temperature sensor 10 exceeds a specified value and the temperature rise rate exceeds a specified value as the starting point for the prediction calculation. The prediction is established when the change in the predicted equilibrium temperature value falls within the specified value. For example, if the predicted value is Y, the measured value is T, and the increase is U, the prediction formula (prediction model) is Y = T + U. For example, if t is the time elapsed from the prediction starting point, the increase U can be calculated as U = a1 × dT/dt + b1, or U = (a2 × t + b2) × dT + (c2 × t + d2). Here, a1, b1, a2, b2, c2, and d2 are pre-set coefficients. In another example, dT is the temperature rise over the past 5 seconds, and dt is 5 seconds.

In this embodiment, the characteristics of the hypothetical measured part (subject) are divided into multiple groups. A calculation formula (prediction model) for the predicted value Y is determined for each group. More specifically, coefficients are determined for the formula used to calculate the increase U in the predicted value Y = T + U. Based on the actual measured value T, the processing unit 20 selects the group to which the current state of the measured part belongs from the multiple groups and calculates the increase U using the prediction model corresponding to that group.

FIG3 illustrates 12 groups classified according to the temperature rise value (horizontal axis) between 15 and 20 seconds from the start of temperature measurement (t=0) (prediction starting point), and the temperature measured at 20 seconds from the start of temperature measurement. The first group (marked as "one group" in FIG3. The same applies to other groups) is the group with the fastest thermal response, and is the group in which the initial temperature is high but the rise ends immediately. The eighth group is the group with the slowest thermal response, and is the group in which the initial temperature is low but the temperature rise continues until very late. The second to seventh groups are groups between the first and eighth groups. The ninth and tenth groups are groups that deviate greatly from the usual measured value changes. When the measured values are classified into these groups, for example, it can be configured to end in error as unpredictable, or it can be configured to display the measured values without making a prediction. In addition, the eleventh and twelfth groups are groups in which the body temperature is above 36.5 degrees at 20 seconds.

FIG4 illustrates the processing related to the prediction of the equilibrium temperature (i.e., the temperature prediction of the measured site) by the processing unit 20 of the electronic thermometer 1. The processing illustrated in FIG4 is executed by the CPU 22 in the processing unit 20 based on the control program 26. Steps S10, S12, S14, S18, and S20 are conventional processing, while steps S16, S22, S24, S26, S28, S30, S32, and S34 are unique to this embodiment.

First, the processing in steps S10, S12, S14, S18, and S20 will be described. In step S10, a power switch (not shown) is turned on. Then, in step S12, a pre-measurement is performed. In this pre-measurement, when the actual measured value of the temperature of the measured portion of the temperature sensor 10 (for example, sampled at intervals of 0.5 seconds) becomes greater than a specified value (for example, 30°C) and the temperature rise rate becomes greater than a specified value (for example, 0.03°C/0.5 seconds), the temperature sensor 10 is deemed to be placed at the specified measurement portion, and this moment is used as the starting point of the pre-measurement calculation (i.e., t=0), and the process moves to the actual measurement in step S14.

The formal measurement is started in step S14. In the formal measurement, grouping is performed based on the temperature rise value (horizontal axis of FIG. 3) with a time of 15 to 20 seconds since the start of the formal measurement (t=0) (prediction starting point) and the actual temperature value (vertical axis of FIG. 3) with a time of 20 seconds since the start of the temperature measurement. That is, it is determined to which group the characteristics of the measured part belong among the multiple groups illustrated in FIG. 3. In each group, the prediction formula (prediction model) is associated as described above, and the equilibrium temperature is predicted based on this. In step S18, the change in the predicted value of the equilibrium temperature is monitored, and the formal measurement is ended (that is, the predicted value is determined to be the temperature of the measured part) at the moment when the change becomes within the specified value. Then, in step S20, the predicted value is displayed on the display unit 30.

The following describes the processing of steps S16, S22, S24, S26, S28, S30, S32, and S34. Step S16 is executed between steps S14 and S18, that is, during the actual measurement period. Step S16 is executed at predetermined time intervals (typically, the sampling interval of the actual temperature value) from the start of the actual measurement in step S14 until the end of the actual measurement in step S18. In step S16, it is determined whether the actual temperature value detected by the temperature sensor 10 is abnormal.

An abnormality in the actual value of the temperature detected by the temperature sensor 10 can occur, for example, when the subject re-clamps the temperature sensor 10 with their armpits, or when the force clamping the temperature sensor 10 is suddenly increased. Whether an abnormality has occurred in the actual value of the temperature detected by the temperature sensor 10 can be determined in various ways, for example, by performing a differential operation on the temperature data (for example, a first differential, a second differential, etc.). If the first differential of the temperature data is T', the specified time is Δt, and the amount of change in the temperature data (measured value) during the specified time Δt is ΔT, then the first differential T' is T' = ΔT/Δt. Furthermore, if the second differential of the temperature data is T", and the amount of change in T' during the specified time Δt is ΔT', then the second differential T" is T" = ΔT'/Δt.

FIG5 illustrates a typical change in the actual temperature value (temperature data) measured by temperature sensor 10 when the subject re-holds temperature sensor 10 between their armpits. FIG5 also illustrates the first and second differentials of the temperature data. The temperature data and the first and second differentials of the temperature data are updated at a predetermined sampling interval.

When the subject re-clamps temperature sensor 10 with their armpit, the temperature data value temporarily decreases and then increases. This is manifested, for example, by the second derivative of the temperature data changing from negative to positive. Therefore, based on this change in the second derivative of the temperature data, it is possible to detect that the subject has re-clamped temperature sensor 10 with their armpit. In the example shown in FIG5 , the change in the second derivative of the temperature data from negative to positive is detected at time t11.

FIG6 illustrates a typical change in the actual value (temperature data) of the temperature measured by the temperature sensor 10 when the force with which the subject clamps the temperature sensor 10 increases sharply. FIG6 also illustrates the first differential and second differential of the temperature data. When the force with which the subject clamps the temperature sensor 10 increases sharply, the rising speed of the temperature data increases, which is manifested, for example, as the second differential value of the temperature data changes from negative to positive. Therefore, based on the change from negative to positive in the second differential value of the temperature data, it is possible to detect that the force with which the subject clamps the temperature sensor 10 increases sharply. In the example shown in FIG6 , the second differential value of the temperature data changes from negative to positive, which is detected at t21.

The criterion of determining an abnormality based on the change of the secondary differential value of the temperature data from negative to positive can cope with both the situation where the subject re-clamps the temperature sensor 10 with his armpit and the situation where the force clamping the temperature sensor 10 suddenly increases.

As described above, an abnormality in the actual temperature value detected by the temperature sensor 10 can be determined, for example, by the second differential value of the temperature data changing from negative to positive. If an abnormality is determined in the actual temperature value detected by the temperature sensor 10, the actual measurement is stopped in step S22.

Next, in step S24, it is determined whether the abnormality in the actual temperature value detected by temperature sensor 10 has been resolved. In this case, an abnormality caused by the subject re-gripping temperature sensor 10 with their armpits will be resolved as the re-gripping condition stabilizes, and the actual temperature value stabilizes. An abnormality caused by the subject suddenly increasing the force applied to grip temperature sensor 10 will be resolved as that force decreases.

In this embodiment, in step S24, the abnormality is determined to have been resolved based on the change in the secondary differential value of the temperature data from positive to negative. In the example shown in FIG5 , the change in the secondary differential value of the temperature data from positive to negative is detected at t12. In the example shown in FIG6 , the change in the secondary differential value of the temperature data from positive to negative is detected at t22. The criterion for determining that the abnormality has been resolved based on the change in the secondary differential value of the temperature data from positive to negative can address both situations in which the subject re-grips the temperature sensor 10 under their armpit and situations in which the force gripping the temperature sensor 10 is suddenly increased.

If it is not determined in step S24 that the secondary differential value has changed from positive to negative, step S24 is executed again in step S26 until a predetermined time has passed from a predetermined starting point (for example, a predetermined time (for example, 5 seconds) has passed since the abnormality was detected in step S16). If it is determined in step S26 that the predetermined time has passed from the predetermined starting point, an error is displayed on the display unit 30 and/or an error sound is output from the buzzer 40.

If it is determined in step S24 that the secondary differential value has changed from positive to negative, the main measurement is resumed in step S30. In the resumed main measurement, the equilibrium temperature can be predicted based on the actual temperature value detected by the temperature sensor 10 and a new prediction formula (referred to as the second prediction formula), i.e., a new prediction model (referred to as the second prediction model). The second prediction formula is different from the prediction formula determined in step S14 (referred to as the first prediction formula). In other words, the second prediction model is different from the prediction model determined in step S14 (referred to as the first prediction model).

The reason why it is preferred to change the prediction formula (prediction model) when restarting the formal measurement is that the group selected as a candidate in step S14 and the prediction formula (prediction model) corresponding to the group do not correspond to abnormal situations such as the situation where the person being tested re-clamps the temperature sensor 10 with his armpit or the situation where the force clamping the temperature sensor 10 increases sharply.

In the restarted formal measurement, the prediction starting point can be changed to a new prediction starting point. Here, the new prediction starting point can be, for example, the time when the second differential value changes from positive to negative. That is, the time when the second differential value changes from positive to negative can be the time t=0. Here, multiple second prediction formulas (second prediction models) can be prepared, and one second prediction formula (second prediction model) can be selected from them based on the characteristics of the measured part. In addition, as multiple second prediction formulas (second prediction models), multiple second prediction formulas (second prediction models) corresponding to the actual measured values at the new prediction starting point can be prepared (for example, for 30-32°C, for 32-34°C, etc.), multiple second prediction formulas (second prediction models) corresponding to the time from the detection of an abnormality to the elimination of the abnormality can be prepared, and multiple second prediction formulas (second prediction models) corresponding to the maximum change in the second differential value of the temperature data from positive to negative when the abnormality is detected can be prepared.

In step S32, the fluctuation of the predicted equilibrium temperature value is monitored. When the fluctuation falls within the specified value, the main measurement is terminated (i.e., the predicted value is determined as the temperature of the measured part). Then, in step S34, the predicted value is displayed on the display unit 30. Here, if an abnormality is detected and the main measurement is restarted, a message indicating this may be output. For example, this message can be output by the display unit 30 and/or the buzzer 40.

Claims (4)

1. An electronic thermometer, which predicts equilibrium temperature based on the measured temperature of the measured body part detected by a temperature sensor, characterized in that, The device includes a processing unit that, after initiating an equilibrium temperature prediction based on the measured value and a first prediction model, if the measured value becomes abnormal, and after the abnormality is eliminated, predicts the equilibrium temperature based on the measured value and a second prediction model different from the first prediction model. The second prediction model is selected from multiple prediction models based on the time from the detection of the anomaly to its elimination, or the maximum change in the second derivative of the measured value from positive to negative when the anomaly is detected. If the abnormality is not eliminated within a predetermined time period from the time the abnormality is detected, the processing unit displays an error to the display unit and/or outputs an error sound from the buzzer. 2. The electronic thermometer according to claim 1, characterized in that, The processing unit determines that the anomaly has been eliminated based on the change of the second derivative value of the measured value from positive to negative, and then predicts the equilibrium temperature based on the measured value and the second prediction model. 3. The electronic thermometer according to claim 1 or 2, characterized in that, Based on the measured values, the first prediction model is selected from multiple prediction models. 4. The electronic thermometer according to claim 1 or 2, characterized in that, The processing unit detects the occurrence of the anomaly by the change of the second derivative of the measured value from negative to positive. After the anomaly is eliminated, it predicts the equilibrium temperature based on the measured value and the second prediction model.