JP2014135987A - Sensor terminal for acquiring signal including quasi-periodic delimiter pulse, and signal acquisition method - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a sensor terminal capable of acquiring data by accurate sensing even for a sudden steep change in a signal quantity while decreasing power consumption for ADC by reducing an amount of data acquired by sensing.SOLUTION: A sensor terminal includes analog-digital conversion means 11 for acquiring a signal including a delimiter pulse that appears in a quasi-periodic manner and converting the signal into a digital signal, and timing calculation means 13 for determining a signal acquisition time to acquire the signal. The timing calculation means 13 determine a subsequent signal acquisition time based on the converted digital signal each time the conversion is made.

Description

  The present invention relates to a sensor terminal that acquires a signal including a break pulse that appears quasi-periodically, and more particularly to a technique for reducing the power consumption thereof.

  Battery-powered small wireless sensor terminals are expected to be useful in many applications. For example, in the healthcare field, an electrocardiographic small wireless sensor terminal is expected as a device that continues to wirelessly transmit electrocardiographic data to a smartphone carrying the electrocardiogram for a long time while continuing daily life. These sensor terminals are required to be small in order to eliminate a sense of incongruity, and accordingly, low power consumption is required to drive for a long time with a small battery.

  In order to reduce the power consumption, a method of compressing data acquired by sensing has been proposed. This is because power consumption for wireless communication can be reduced by reducing the amount of data to be wirelessly transmitted by data compression. However, the data compression method has a problem that power consumption associated with ADC (analog-digital conversion) cannot be reduced. The reason is that all acquired data is subjected to ADC processing. Particularly, in the electrocardiographic wireless sensor terminal, it is known that the power consumption associated with the ADC is dominant in the total power consumption, and the reduction thereof is required.

  Patent Document 1 discloses a method of reducing power consumption by reducing the amount of data acquired by changing the sampling frequency of an ADC. This reduces the overall data amount by increasing the sampling frequency in a region where the signal amount changes sharply, such as an R wave of an electrocardiogram, and lowering the sampling frequency in a region where the signal amount changes slowly, This is to reduce the power consumption of the ADC.

JP 2010-94236 A

  In Patent Document 1, the amount of data acquired by changing the sampling frequency of the ADC is reduced, and the power consumption of the ADC is reduced. However, since Patent Document 1 is a method of switching the sampling frequency, there is a problem that the sampling interval can be changed only in a rough section unit. Further, in addition to the electrocardiographic data, information on the sampling frequency that is variable is stored or transmitted together, and there is a problem that the data increases accordingly. If this method is made to deal with fine waveform fluctuations, the additional information becomes large and the effect of sample point saving is impaired. Therefore, there is a problem that only a rough waveform fluctuation can be dealt with, and fine power saving cannot be performed.

  The present invention has been made in view of the above problems, and its purpose is to reduce the power consumption of the ADC while reducing the data acquisition amount by finely controlling the data acquired by sensing, and suddenly. It is to provide a sensor terminal that can acquire data by accurate sensing even with a sudden change in signal amount.

  An analog-to-digital converter that acquires a signal including a delimiter pulse that appears quasi-periodically and converts the signal into a digital signal; and a timing calculator that determines a signal acquisition time for acquiring the signal. The calculation means is a sensor terminal that determines the next signal acquisition time based on the converted digital signal each time the conversion is performed.

  An analog-to-digital conversion step of acquiring a signal including a delimiter pulse that appears quasi-periodically and converting the signal into a digital signal; and a timing calculation step of determining a signal acquisition time for acquiring the signal. The calculation step is a signal acquisition method in which the next signal acquisition time is determined based on the converted digital signal every time the conversion is performed.

  According to the present invention, it is possible to reduce the power consumption of an ADC by reducing the amount of data to be acquired by sensing, and to acquire data by accurate sensing even for a sudden change in signal amount. It is possible to provide a sensor terminal that can In addition, the storage capacity and data transmission power can be reduced as the amount of data to be acquired is reduced.

It is a block diagram which shows the structure of the sensor terminal of embodiment of this invention. It is a graph explaining the mode of the data acquisition of the sensor terminal of embodiment of this invention. It is a figure explaining the problem which arises when adaptive sensing is applied to an electrocardiogram. It is a flowchart which shows the algorithm of the sensor terminal of embodiment of this invention. It is a flowchart which shows the algorithm which discriminate | determines whether it is in a delimiter pulse in the algorithm of the sensor terminal of embodiment of this invention. It is a figure which shows feature-value | v '|. It is a figure which shows feature-value | v '' |. It is a figure which shows feature-value px | v '| + qx | v' '|. It is a graph explaining the mode of the data disposal of the sensor terminal of a comparative example. It is a block diagram which shows the structure of the sensor terminal of a comparative example.

  Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings. However, the preferred embodiments described below are technically preferable for carrying out the present invention, but the scope of the invention is not limited to the following.

  FIG. 1 is a block diagram showing a configuration of a sensor terminal according to an embodiment of the present invention. In FIG. 1, an analog signal input from the electrode 10 is converted into a digital signal by an ADC (analog-digital converter) 11. The next data acquisition timing calculation 13 is performed based on this digital signal, and the calculated next data acquisition timing is transmitted to the ADC 11 through the path 20. The ADC 11 acquires data at the data acquisition timing given through the path 20. The acquired data may be wirelessly transmitted 15 after being stored in the storage 14 or may be directly wirelessly transmitted 15. In the above, the data acquisition timing calculation 13 is performed by a calculation resource such as a processor built in the sensor terminal.

Data acquired at time t_i is described as v (t_i). A set of data {v (t_k), v (t_ (k-1)), v (t_ (k-2)), v (t_ (k-3)), ...} acquired until a certain time t_k. Abbreviated as {v (t_k_)}. In the present embodiment, the next data acquisition timing t_ (k + 1) is calculated based on the previously acquired data set {v (t_k_)}. This means that t_ (k + 1) is a function of {v (t_k_)}, and t_ (k + 1) = f ({v (t_k_)})
It can be expressed as. Here, f () is a function, and a specific example thereof will be described later. The range of data used in Equation 1, that is, the number of previous points used depends on the algorithm used, and can be used from using only the previous data to using all data. is there.

  FIG. 2 shows a graph for explaining the state of data acquisition by the sensor terminal of this embodiment. This is an example of an electrocardiogram, where v is an electrocardiogram signal (voltage) and t is time. A curve is an electrocardiogram, and a black circle 1 represents acquired sample data, and a white circle 3 represents sample data not acquired. In the present embodiment, all sample data is not acquired once at a constant interval T, but the next data acquisition timing is sequentially calculated based on Equation 1 from the data acquired up to that point, and sensing is performed at that timing.

  That is, the data acquisition interval Δ changes adaptively depending on the state of the electrocardiogram waveform. Δ is shortened when the signal changes rapidly, and Δ is lengthened when the signal changes slowly. As a result, the amount of acquired data can be reduced while capturing the characteristics of the signal waveform. In FIG. 2, the data of the white circle 3 does not perform sensing itself. For this reason, since the amount of acquired data is reduced, the power consumption accompanying ADC can be reduced. As described above, a method of adaptively determining the next data acquisition timing from acquired data based on Formula 1 is hereinafter referred to as adaptive sensing.

  In adaptive sensing, the data acquisition interval is not constant. However, it is not necessary to store information about the data acquisition interval separately. This is because Δ can be calculated sequentially by applying Formula 1 to the acquired data in chronological order. In general data compression methods, there is a case where information on Δ is separately stored. Therefore, there is a problem that the amount of data increases accordingly. In adaptive sensing, there is an advantage that there is no need to separately store information about Δ, and the amount of data does not increase accordingly.

  As a method for controlling the sensing interval, Patent Document 1 discloses a method for controlling the sampling frequency. However, since the sampling frequency is switched, there is a problem that the sampling interval can be changed only in a rough section unit. Further, in addition to the electrocardiographic data, information on the sampling frequency that is variable is stored or transmitted together, and there is a problem that the data increases accordingly. If this method is made to cope with fine waveform fluctuations, the amount of additional information increases and the effect of sample point saving is impaired. Therefore, there is a problem that only a rough waveform fluctuation can be dealt with, and fine power saving cannot be performed.

  On the other hand, the adaptive sensing according to the present embodiment has an advantage that the next data acquisition timing is adaptively determined every time data is acquired, so that it is possible to sequentially follow small changes in the signal.

  By the way, only adaptive sensing may fail to capture the characteristics of the waveform. For example, the electrocardiogram includes a plurality of characteristic waves such as a Q wave, an R wave, an S wave, a T wave, and a P wave as shown in FIG. Of these, the R wave is a sharp pulse wave that protrudes and has a high wave height, and is the most important wave that characterizes the electrocardiogram, but it can sometimes be missed by adaptive sensing.

  FIG. 3 is a diagram for explaining problems that occur when adaptive sensing is applied to an electrocardiogram. In FIG. 3, it is assumed that data is sampled as indicated by black circles 6 and 7 in a slowly changing portion before the R wave. Since the signal amount does not change much at these two points, the next data acquisition interval Δ calculated based on them becomes long. However, at the time of the black dot 8 of the next sampling, most of the sharp R waves are finished, resulting in a failure to catch the R waves.

  Thus, adaptive sensing alone has a risk of failing to capture a sharp pulse. Therefore, in this embodiment, the algorithm shown in FIG. 4 is used. Basically, adaptive sensing is used, and the data acquisition interval is adjusted according to the situation, thereby preventing the loss of sharp pulses.

  After the start of the algorithm (step 100), the sense value v (t_k) at the current time t_k is read by sensing (step 110). Next, based on the acquired data, a feature amount (details will be described later) that characterizes the degree of waveform change is calculated (step 120). In adaptive sensing, the next data acquisition timing t_ (k + 1) is basically calculated based on this feature amount (step 160).

This calculation formula, that is, the function f () of Formula 1, includes several parameters, and the calculated value of t_ (k + 1) depends on the parameters. When a set of these parameters is denoted as {P}, Equation 1 more specifically shows t_ (k + 1) = f ({v_k_}, {P}) Equation 2
It is expressed. By changing this parameter {P} according to the situation, the number of acquired data can be reduced without losing the characteristics of the waveform. In the present embodiment, it is assumed that a sharp pulse with a high wave height, which is a distinguishing feature of a signal waveform, like a R wave of an electrocardiogram, periodically occurs to some extent. Since such a pulse appears as a block of a group of signals, it is referred to herein as a pulse.

  In the algorithm of FIG. 4, if the current sensing is within the delimiter pulse (YES branch of step 130), Equation 1 is calculated using the intra-department pulse interval parameter (step 180) (step 160). If the current sensing is not within a delimiter pulse (NO branch at step 130) and is not near the next delimiter pulse (NO branch at step 140), then using the wide interval parameter (step 150), the formula 1 is calculated (step 160). If the current sensing is in the vicinity of the next delimiter pulse (NO branch of step 140), Equation 1 is calculated using the narrow interval parameter (step 170) (step 160).

  Here, the vicinity of the break pulse means a time somewhat before the start of the break pulse. Since the signal changes slowly as long as it is in the vicinity of the delimiter pulse, the data acquisition interval may be wide. For this reason, parameters for wide intervals are used. On the other hand, after the vicinity of the delimiter pulse, it is necessary to narrow the data acquisition interval so that it is not missed whenever the delimiter pulse occurs. For this reason, parameters for narrow intervals are used. Since a large signal change occurs within a short time within the delimiter pulse, it is necessary to make the data acquisition interval as narrow as possible. For this reason, the parameter for the interval in a delimiter pulse suitable for it is used.

  The above operation will be described with reference to FIG. In the electrocardiogram of FIG. 2, the above-described delimiter pulse corresponds to the R wave. When the left R wave in FIG. 2 ends, the next R wave arrival time (that is, the next R wave interval) is predicted. As the prediction method, for example, there is a method of using the previous R wave interval (the interval between the left R wave in FIG. 2 and the left R wave not shown in the figure). Since the previous R wave interval has been determined at the end of the R wave on the left side of FIG. 2, it can be used as the next expected R wave interval.

Alternatively, an average value of some recent R wave intervals or an appropriately weighted average value may be used. This corresponds to the use of the output of a finite impulse response (abbreviated as FIR) filter having several recent R wave intervals as input data for the next expected R wave interval. Alternatively, an infinite impulse response (abbreviated as Infinite Impulse Response, IIR) filter may be used instead of the FIR filter. For example, the following IIR filter is one of the preferred examples.
y (k + 1) = (1−a) × x (k) + a × y (k) Equation 3
0 ≦ a ≦ 1 Formula 4
Here, x (k) is the last (latest) actually observed R wave interval, y (k) is the previous expected R wave interval, and y (k + 1) is the next expected R wave interval. When a = 0, the immediately preceding actual R wave interval is used as the next expected R wave interval, which corresponds to the example already described. a = 1 corresponds to using a predetermined constant as the expected R wave interval. Using the intermediate a corresponds to calculating the next expected R wave interval by appropriately weighting and averaging the past actual R wave interval. By using the value of a <1 in Equation 3, it is possible to adaptively determine the next expected R wave interval sequentially based on the actually observed R wave interval.

  Using the time t_n somewhat before the predicted next R-wave arrival time, the vicinity of the above-described delimiter pulse is defined. In general, R waves are roughly constant intervals, but are not strictly constant, and vary within a certain range depending on the situation of the person. In particular, there are many biological signals of this type. In order to cope with such fluctuations in the R wave interval, a time t_n slightly before the expected R wave interval is taken, a wide data acquisition interval is allowed before that, and the data acquisition interval thereafter, that is, in the vicinity of the delimiter pulse To narrow. As a result, even if the delimiter pulse (R wave in the electrocardiogram) fluctuates to some extent, it is prevented from being missed.

A preferred example of calculating the vicinity of the next break pulse is c × y (k + 1) + d Equation 5
It is. Here, c and d are coefficients determined so that the value of Equation 5 is smaller than y (k + 1). T_n that defines the vicinity of the delimiter pulse is determined as the time after the time calculated by Equation 5 from the time of the previous delimiter pulse (the time of the R wave on the left side of FIG. 2). Details of the time of the separation pulse will be described later.

  The NO branch of the end condition (step 190) of the algorithm of FIG. 4 is not only when the next data acquisition is simply performed, but after the acquired data is accumulated to some extent, the next data acquisition is performed after wireless transmission. It includes cases where it is performed. When the predetermined data acquisition is completed, the end condition is satisfied (YES branch at step 190), and the entire process ends (step 200).

Next, the feature amount calculation (step 120) in FIG. 4 will be described. What is calculated here is an amount representing the speed of change of the signal waveform v (t). As a typical example, the absolute value | v ′ (t) | = | dv (t) / dt | Use. Actually, in order to calculate the derivative from the discrete acquired data, for example, it is calculated by the following difference equation.
v ′ (t_k) = (v (t_k) −v (t_ (k−1))) / (t_k−t_ (k−1)) Equation 6
Various differential expressions have been proposed as a method for calculating the differential in the discrete sample data, and any of them may be used. Equation 6 is the simplest example of them. Actually, time series data after smoothing the acquired data string with a low-pass filter or removing noise is used as v (t_k) instead of the acquired data itself.

In addition, as described later, the speed of change in the signal waveform v (t) is obtained by combining the first derivative v ′ (t) and the second derivative v ″ (t) = d ² v (t) / dt ^2. It may be used as a feature amount representing.

Now, a feature amount indicating the speed of change of the signal v at time t_k is denoted as F (t_k). At this time, the next data acquisition interval Δ (t_ (k + 1)) is expressed as follows.
Δ (t_ (k + 1)) = t_ (k + 1) −t_k = g (F (t_k)) Equation 7.1
However, when g (F (t_k)) is smaller than a certain threshold value,
Δ (t_ (k + 1)) = T Equation 7.2
Here, F () is a function having a value of 0 or more, and g (x) is a decreasing function of the argument x and takes a value that is an integral multiple of T. T is the minimum sampling interval. A preferable example of g () that is most easily calculated is g (x) = T × INT (h−e × x).
It is. Here, INT () is a function that makes an argument an integer, for example, a function that takes the largest integer that does not exceed the argument, a function that takes the smallest integer that does not fall below the argument, or a function that rounds off the decimal point. It is. h and e are parameters having positive values, and are examples of the parameter {P} in Equation 2.

  h determines the maximum value that Formula 8 can take, that is, the maximum data acquisition interval. For this reason, h having a large value h1 as the wide interval parameter already described, h having a small value h2 as the narrow interval parameter, and h having a value h3 smaller than h2 as the intra-interval pulse interval parameter are used. Use is one of the preferred embodiments. Alternatively, the minimum sampling interval T may always be used within the delimiter pulse.

  Alternatively, a wide interval parameter is determined so that the average value of the calculated data acquisition interval is increased, and a narrow interval parameter is determined so that the average value of the calculated data acquisition interval is smaller than that. The parameter for the interval within the delimiter pulse may be determined so that the average value of the interval is further reduced.

  In the adaptive sensing of the present invention, the next data acquisition time t_ (k + 1) is sequentially calculated from the acquired data up to time t_k using Equation 7.1 and Equation 7.2.

  Next, step 130 in FIG. 4 will be described in detail. This is a step of determining whether the signal is within the delimiter pulse at the current time. FIG. 5 is a flowchart showing details of step 130. The state variable S = 1 indicates that the signal is within the delimiter pulse at the current time, and otherwise S = 0.

  The flow i from the previous step of step 130 is first checked whether S = 0. When S = 0, that is, when the previous acquired data is not a delimiter pulse (YES branch of step 131), whether or not the current acquired data is included in the delimiter pulse, that is, whether or not the current acquired data is the start of the delimiter pulse Is checked (step 132). If it is the start of the delimiter pulse (YES branch of step 132), the state variable S is set to 1 (step 133), and the process proceeds to the YES branch of step 130 (y flow in FIG. 5). This is determined to be within the break pulse at the present time. On the other hand, if NO in step 132, it is determined that the flow is n flow in FIG.

  On the other hand, if S = 1 in the flow i from the previous step of step 130, that is, if the previous acquired data is within the delimiter pulse, the process proceeds to the NO branch of step 131 to determine whether the current acquired data is outside the delimiter pulse. Is checked (step 134). If it is outside the delimiter pulse (YES branch of step 134), it means that the delimiter pulse has ended, the state variable S is returned to 0 (step 135), and the flow proceeds to the n flow of FIG. In the case of NO branch at step 134, the current acquisition data is still within the delimiter pulse (similar to the previous acquisition data), so the state variable S remains 1 (step 133) and the y flow of FIG. Proceed to

Next, a method for determining whether or not the acquired data is within the delimiter pulse used in step 130 will be described. For this determination, the feature amount F () representing the speed of change of the signal v already described is used. The simplest method is to make a determination based on whether or not F () exceeds a certain threshold value THf. That is,
THf <F (t_k) Formula 9
Is satisfied, it is determined in step 132 of FIG. 5 that the delimiter pulse is started (YES branch is taken). In step 134 in FIG. 5, when F (t_k) ≦ THf, it is determined that the separation pulse is finished (YES branch is taken).

  Alternatively, as a determination as to whether or not it is within a delimiter pulse, it may be conditional on Equation 9 being continuously established for a certain period of time. Accordingly, it is possible to prevent a very narrow spike-like pulse due to noise or the like from being determined as a separation pulse. Or conversely, it may be determined that the delimiter pulse ends only when a certain time F (t_k) ≦ THf holds (YES branch of step 134 in FIG. 5). As a result, it can be prevented that the short-term F (t_k) ≦ THf is erroneously determined as the end of the separation pulse.

  The example using | v ′ | (the absolute value of the derivative of the signal) as F () has already been described. FIG. 6A shows a graph of the differential absolute value | v ′ | of the electrocardiogram v of FIG. The operation of time differentiation is a kind of high-pass filter, which eliminates the effects of baseline shift and drift of the original signal as shown in FIG. There is merit that can prevent judgment. In this case, a time when | v ′ | exceeds a certain threshold value THf is set as a delimiter pulse start time. However, an erroneous determination occurs if it is determined that the end of the separation pulse is simply when | v ′ | is less than THf. This is because, like the sampling point 5a in FIG. 6A, it may temporarily fall below THf. In this case, as described above, when | v ′ |

Alternatively, as the feature amount F (), the differential and second-order differential combination F (t_k) = p × | v ′ (t_k) | + q × | v ″ (t_k) |
May be used. Here, p and q are certain constants. The second-order differential absolute value | v ″ | of the electrocardiogram of FIG. 2 is shown in FIG. 6B. In this case, the sampling point 5b corresponding to the sampling point 5a that is temporarily depressed in FIG. 6A has a large value. Therefore, according to Equation 10, it is possible to create a feature amount in which the temporary dent in the separation pulse is corrected. FIG. 6C shows an example of Equation 10. The sampling point 5c corresponding to the temporary dimple sampling point 5a in FIG. 6A can be made equal to or higher than the threshold value THf. As a method for calculating v ″ in discrete sampling, the following difference equation can be used.
v ″ (t_k) = (v ′ (t_k) −v ′ (t_ (k−1))) / (t_k−t_ (k−1)) Equation 11
v ′ can be calculated by Equation 6.
In addition to the above, various methods can be used for taking the feature amount, such as incorporating the original signal as shown in FIG.

  Further, the case has been described above where the feature quantity used to calculate the data acquisition interval (Formula 7.1) and the feature quantity used to determine whether or not it is a delimiter pulse are the same. These feature amounts do not necessarily have to be the same, and may differ depending on the purpose. For example, | v ′ | may be used as the former feature quantity and Equation 10 may be used as the latter feature quantity.

  According to the embodiment described above, there is an advantage that the large power consumption required for the ADC can be reduced. The reason is that adaptive sensing is used in which a feature amount representing the speed of signal change is calculated from acquired data, and the next data acquisition interval is determined in real time by online processing based on the calculated feature amount. As a result, the number of data acquisitions in a slowly changing portion can be reduced, and the power consumption of the ADC can be reduced.

  In addition, when data is acquired at all points of a fine sampling interval, there is a problem that not only the power consumption associated with the ADC increases, but also storage (or memory) having a capacity capable of holding such data is necessary. It was. However, this embodiment has an advantage that only a small amount of data for calculating the feature amount needs to be held in the memory, and it is not necessary to hold all data at a fine sampling interval. In this embodiment, it is only necessary to have a storage (storage 14 in FIG. 1) that holds data after being thinned out by adaptive sensing. As described above, this embodiment has an advantage of reducing the capacity of the data storage device. In adaptive sensing, data is acquired at unequal intervals. However, since the data acquisition interval can be sequentially calculated from the acquired data, it is not necessary to retain information regarding the data interval.

Furthermore, according to the present embodiment, there is an advantage that adaptive sensing can be applied even to a steep pulse with a high wave height whose appearance period varies, that is, a signal including a delimiter pulse. This is because the signal is divided into regions that are close to the delimiter pulse, regions other than those, and characteristic regions, and data acquisition intervals suitable for the respective regions are calculated. As a result, adaptive sensing can be applied without losing a steep break pulse.
(Comparative example)
As a comparative example for the present invention, an example will be described in which a certain amount of data acquired by sensing at regular intervals is discarded, thereby reducing the amount of data to be wirelessly transmitted and reducing power consumption. FIG. 7 is a graph showing ECG sensing. This shows the change of the electrocardiogram data (voltage) v with time t, the curve represents the electrocardiogram, and the circles (black circle 1 and white circle 2) represent the data acquired by sensing. Sensing is performed at regular time intervals T.

  In this comparative example, first, data of all the circles (black circle 1 and white circle 2) is acquired at a constant sensing interval T, and thereafter, the data is discarded within a range in which the waveform features remain. In FIG. 7, only black circle 1 data is left and white circle 2 data is discarded. That is, in a portion where the waveform changes slowly, the characteristics of the waveform can be captured even with sparse data. Therefore, the interval between the black circles 1 is lengthened and a lot of white circle 2 data is discarded. In a portion where the waveform changes sharply such as an R wave, fine data is left so as not to impair the characteristics of the sharp pulse waveform. The data is compressed by discarding the acquired data to the extent that the waveform characteristics are not impaired by the above method.

  FIG. 8 shows a block diagram of a wireless sensor terminal that performs the above-described data compression processing. The analog electrocardiogram signal input from the electrode 10 is converted into a digital signal by the ADC 11 and stored in the storage 12. Here, the ADC 11 includes analog processing such as an analog filter and an analog amplifier before the signal is digitally converted. The storage 12 may be any of magnetic storage, nonvolatile memory, and volatile memory. After a certain amount of data is accumulated in the storage 12, the data is compressed 16. The compressed data is temporarily stored in the storage 14 and then wirelessly transmitted 15. In some cases, the wireless transmission 15 may be directly performed without being temporarily stored in the storage 14, or the wireless transmission 15 may be performed only by being held in the storage 14. The storages 12 and 14 may be the same or different.

  As an effect of the above comparative example, the capacity of the storage 14 can be reduced, and the power consumption of the wireless transmission 15 can be reduced. However, the power consumption associated with ADC processing cannot be reduced with the method of the comparative example. On the other hand, according to the present invention, the power consumption associated with the ADC processing can be reduced. Therefore, in this respect, the present invention has an advantageous performance over the comparative example.

  The present invention is not limited to the above-described embodiment, and various modifications are possible within the scope of the invention described in the claims, and it is also included within the scope of the present invention. Not too long. Moreover, although a part or all of said embodiment may be described also as the following additional remarks, it is not restricted to the following.

Appendix (Appendix 1)
An analog-to-digital conversion means for acquiring a signal including a break pulse appearing quasi-periodically and converting the signal into a digital signal; and a timing calculation means for determining a signal acquisition time for acquiring the signal,
The timing calculation means determines the next signal acquisition time based on the converted digital signal each time the conversion is performed.
(Appendix 2)
The timing calculation means sequentially calculates a feature amount corresponding to the magnitude of change of the signal based on the converted digital signal every time the conversion is performed, and the next signal based on the feature amount The sensor terminal according to attachment 1, wherein the acquisition time is determined.
(Appendix 3)
The sensor terminal according to appendix 2, wherein the timing calculation unit determines whether the digital signal is within the pulse period based on the feature amount, and determines the next signal acquisition time based on the determination.
(Appendix 4)
The timing calculation means predicts the next pulse appearance start time and end time based on the past appearance interval of the pulse, and the vicinity of the pulse from a predetermined time before the pulse appearance start time to the pulse appearance start time. A calculation method for determining the signal acquisition time in accordance with a period, an intra-pulse period from the pulse appearance start time to the pulse appearance end time, and a period other than both the pulse vicinity period and the pulse period. The sensor terminal according to one of the supplementary notes 1 to 3, which is defined.
(Appendix 5)
The maximum value of the signal acquisition time interval in the intra-pulse period is smaller than the maximum value of the signal acquisition time interval in the near pulse period,
The maximum value of the signal acquisition time interval in a period other than the two periods is greater than the maximum value of the signal acquisition time interval in the pulse vicinity period.
The sensor terminal according to appendix 4.
(Appendix 6)
The average value of the signal acquisition time intervals in the intra-pulse period is smaller than the average value of the signal acquisition time intervals in the near pulse period.
An average value of the signal acquisition time intervals in a period other than the two periods is greater than an average value of the signal acquisition time intervals in the pulse vicinity period.
The sensor terminal according to appendix 4.
(Appendix 7)
The sensor terminal according to any one of appendices 2 to 6, wherein the feature amount is an amount proportional to an absolute value of a difference between the digital signals.
(Appendix 8)
8. The sensor terminal according to claim 1, wherein the feature amount is based on an absolute value of a difference between the digital signals and an absolute value of the difference between the differences.
(Appendix 9)
An analog-to-digital conversion step of acquiring a signal including a break pulse appearing quasi-periodically and converting the signal into a digital signal, and a timing calculation step of determining a signal acquisition time for acquiring the signal,
The timing calculation step is a signal acquisition method in which a next signal acquisition time is determined based on the converted digital signal every time the conversion is performed.
(Appendix 10)
The timing calculation step sequentially calculates a feature amount corresponding to the magnitude of change of the signal based on the converted digital signal every time the conversion is performed, and the next signal based on the feature amount The signal acquisition method according to appendix 9, wherein the acquisition time is determined.
(Appendix 11)
The signal acquisition method according to appendix 10, wherein the timing calculation step determines whether the digital signal is within the pulse period based on the feature amount, and determines the next signal acquisition time based on the determination. .
(Appendix 12)
The timing calculation step predicts the next pulse appearance start time and end time based on the previous pulse appearance interval, and the vicinity of the pulse from a predetermined time before the pulse appearance start time to the pulse appearance start time. A calculation method for determining the signal acquisition time in accordance with a period, an intra-pulse period from the pulse appearance start time to the pulse appearance end time, and a period other than both the pulse vicinity period and the pulse period. 12. The signal acquisition method according to one of appendices 9 to 11, which is defined.
(Appendix 13)
The maximum value of the signal acquisition time interval in the intra-pulse period is smaller than the maximum value of the signal acquisition time interval in the near pulse period,
The maximum value of the signal acquisition time interval in a period other than the two periods is greater than the maximum value of the signal acquisition time interval in the pulse vicinity period.
The signal acquisition method according to attachment 12.
(Appendix 14)
The average value of the signal acquisition time intervals in the intra-pulse period is smaller than the average value of the signal acquisition time intervals in the near pulse period.
An average value of the signal acquisition time intervals in a period other than the two periods is greater than an average value of the signal acquisition time intervals in the pulse vicinity period.
The signal acquisition method according to attachment 12.
(Appendix 15)
15. The signal acquisition method according to claim 10, wherein the feature amount is an amount proportional to an absolute value of a difference between the digital signals.
(Appendix 16)
16. The signal acquisition method according to claim 10, wherein the feature amount is based on an absolute value of the difference between the digital signals and an absolute value of the difference between the differences.

1, 6, 7, 8 Black circle 2 White circle 3 White circle 5a, 5b, 5c Sampling point 10 Electrode 11 ADC (Analog-to-digital converter)
13 Data acquisition timing calculation 12, 14 Storage 15 Wireless transmission 16 Data compression 20 Path

Claims (10)

An analog-to-digital conversion means for acquiring a signal including a break pulse appearing quasi-periodically and converting the signal into a digital signal; and a timing calculation means for determining a signal acquisition time for acquiring the signal,
The timing calculation means determines the next signal acquisition time based on the converted digital signal each time the conversion is performed. The timing calculation means sequentially calculates a feature amount corresponding to the magnitude of change of the signal based on the converted digital signal every time the conversion is performed, and the next signal based on the feature amount The sensor terminal according to claim 1, wherein an acquisition time is determined. The sensor terminal according to claim 2, wherein the timing calculation unit determines whether the digital signal is within the pulse period based on the feature amount, and determines the next signal acquisition time based on the determination. . The timing calculation means predicts the next pulse appearance start time and end time based on the past appearance interval of the pulse, and the vicinity of the pulse from a predetermined time before the pulse appearance start time to the pulse appearance start time. A calculation method for determining the signal acquisition time in accordance with a period, an intra-pulse period from the pulse appearance start time to the pulse appearance end time, and a period other than both the pulse vicinity period and the pulse period. The sensor terminal according to claim 1, wherein the sensor terminal is defined. The maximum value of the signal acquisition time interval in the intra-pulse period is smaller than the maximum value of the signal acquisition time interval in the near pulse period,
The maximum value of the signal acquisition time interval in a period other than the two periods is greater than the maximum value of the signal acquisition time interval in the pulse vicinity period.
The sensor terminal according to claim 4. The average value of the signal acquisition time intervals in the intra-pulse period is smaller than the average value of the signal acquisition time intervals in the near pulse period.
An average value of the signal acquisition time intervals in a period other than the two periods is greater than an average value of the signal acquisition time intervals in the pulse vicinity period.
The sensor terminal according to claim 4. The sensor terminal according to claim 2, wherein the feature amount is an amount proportional to an absolute value of a difference between the digital signals. The sensor terminal according to claim 2, wherein the feature amount is based on an absolute value of the difference between the digital signals and an absolute value of the difference between the differences. An analog-to-digital conversion step of acquiring a signal including a break pulse appearing quasi-periodically and converting the signal into a digital signal, and a timing calculation step of determining a signal acquisition time for acquiring the signal,
The timing calculation step is a signal acquisition method in which a next signal acquisition time is determined based on the converted digital signal every time the conversion is performed. The timing calculation step sequentially calculates a feature amount corresponding to the magnitude of change of the signal based on the converted digital signal every time the conversion is performed, and the next signal based on the feature amount The signal acquisition method according to claim 9, wherein an acquisition time is determined.

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