Patent Description:
For example, there is a system that monitors itchiness of a patient by performing a movement measurement of the body of the patient. A more accurate measurement is desirable.

PTL <NUM>: <CIT>. <CIT> discloses behavioural detection system using motion sensing. <CIT> discloses a scratching detection system for animals.

Embodiments provide a scratching detection system in which scratching behavior can be more accurately detected.

According to an embodiment, a scratching detection system includes a sensor, and a processor. The sensor is configured to detect an acceleration corresponding to a movement of a hand of a person and is configured to output a first signal including a detected value corresponding to the acceleration in a first cycle. The processor is configured to acquire the first signal and to detect a scratching behavior of the person in a second cycle based on a plurality of parameters. The second cycle includes a plurality of the first intervals. The plurality of parameters includes a first occurrence count and a first consecutive count. The first occurrence count is a number of times that an absolute value of the detected value exceeds a threshold value in the second cycle. The first consecutive count is a maximum value of a number of times that the absolute value of the detected value consecutively exceeds the threshold value in the second cycle.

According to embodiments, a scratching detection system can be provided in which scratching behavior can be more accurately detected.

Various embodiments will be described hereinafter with reference to the accompanying drawings.

The drawings are schematic or conceptual. The size ratio between the portions is not necessarily identical to those in reality. Furthermore, the same portion may be shown with different dimensions or ratios in different figures.

In the present specification and drawings, the same elements as those described previously with reference to earlier figures are labeled with like reference numerals, and the detailed description thereof is omitted as appropriate.

<FIG> is a schematic view illustrating a scratching detection system according to a first embodiment.

<FIG> are schematic views illustrating the scratching detection system according to the first embodiment.

As shown in <FIG>, the scratching detection system <NUM> according to the embodiment includes a measuring device <NUM>. As described below, for example, the measuring device <NUM> is configured to detect whether a person is scratching a part of the body of the person. The person is a user of the scratching detection system <NUM>, for example. The scratching detection system <NUM> can detect a scratching behavior of the person, that includes a scratching, a rubbing, a tapping and the like. The scratching detection system <NUM> may further include an output device <NUM>.

As shown in <FIG>, the measuring device <NUM> includes a sensor <NUM> and a processor <NUM>. The measuring device <NUM> may include a communicator <NUM>, a memory <NUM>, a power supply <NUM>. The output device <NUM> includes a display <NUM>. The output device <NUM> may include a processor <NUM>, a communicator <NUM>, a memory <NUM>, and a power supply <NUM>.

The measuring device <NUM> and the output device <NUM> can communicate with each other. For example, the communicator <NUM> and the communicator <NUM> can communicate with each other. For example, the communicator <NUM> may be able to communicate with a server <NUM> via a network <NUM>. The server <NUM> may include, for example, a processor such as a CPU, etc..

As described below, for example, in the measuring device <NUM>, the sensor <NUM> is configured to detect the movement (e.g., the body movement) of the person. The processor <NUM> is configured to perform a determination operation (i.e., a first operation) to determine whether the person is scratching based on data received from the sensor <NUM> and generate the detection result. The detection result can be output to the output device <NUM> via the communicator <NUM>. The detection result and a program or software relating to the processing of the processor <NUM> may be stored in the memory <NUM>. The sensor <NUM>, the processor <NUM>, the communicator <NUM>, and the memory <NUM> can be operated by power supplied from the power supply <NUM>. The power supply <NUM> may include, for example, a battery.

In the output device <NUM> as described below, the display <NUM> is configured to display information related to the detection result of the sensor <NUM>. For example, the output device <NUM> acquires the information (e.g., signal) based on the detection result of the sensor <NUM> via the communicator <NUM>. For example, the processor <NUM> may calculate information from the detection result. The information includes a period when the person is scratching during a one night for example. The information in detail is described later. For example, the display <NUM> displays the information. For example, the memory <NUM> is configured to store the detection result acquired via the communicator <NUM>, and the information calculated by the processor <NUM>. The display <NUM>, the processor <NUM>, the communicator <NUM>, and the memory <NUM> can be operated by power supplied from the power supply <NUM>. The power supply <NUM> may include, for example, a battery. The output device <NUM> may include, for example, a portable terminal (e.g., a smartphone, etc.). Any wireless or wired communication method is applicable to the communication between the communicator <NUM> and the communicator <NUM>.

As shown in <FIG>, in one example, the measuring device <NUM> may include a housing <NUM>. The sensor <NUM>, the processor <NUM>, etc., may be provided in the housing <NUM>. The communicator <NUM>, the memory <NUM>, the power supply <NUM>, etc., are not illustrated in <FIG>. As shown in <FIG>, a power input terminal <NUM> (e.g., USB: universal serial bus) can be provided in the measuring device <NUM>. As shown in <FIG>, the measuring device <NUM> may include an adhesive member <NUM>. For example, the adhesive member <NUM> is provided on the bottom face of the housing <NUM>. The adhesive member <NUM> may include, for example, a sheet-like adhesive material.

As shown in <FIG>, the measuring device <NUM> can be detachably fixed to a hand <NUM> of the person <NUM>. For example, the housing <NUM> is fixed to the hand <NUM> by the adhesive member <NUM>. As shown in <FIG>, a protrusion 19p can be provided in the housing <NUM>. The person <NUM> can easily attach and release the housing <NUM> from the hand <NUM> by handling the protrusion 19p.

As shown in <FIG>, a glove-shaped member <NUM> may be provided in the measuring device <NUM>. At least one finger of the person <NUM> can pass through a portion of the glove-shaped member <NUM>, for example. The housing <NUM> is placed between the back <NUM> of the hand <NUM> and the glove-shaped member <NUM>. The glove-shaped member <NUM> may fix the position of the housing <NUM> with respect to the hand <NUM>. The measuring device <NUM> may be detachably fixed to the hand <NUM> by the glove-shaped member <NUM>. In the example shown in <FIG>, the adhesive member <NUM> may not be provided.

For example, the measuring device <NUM> is fixed to a back <NUM> of the hand <NUM>. By fixing the measuring device <NUM> to the back <NUM>, the person <NUM> can perform any operation using the palm and fingers of the hand <NUM> without discomfort.

The sensor <NUM> is configured to detect an acceleration corresponding to the movement of the hand <NUM> of the person <NUM>. The sensor <NUM> includes, for example, an acceleration sensor. The acceleration sensor includes, for example, a MEMS (Micro Electro Mechanical Systems) device.

As shown in <FIG>, the sensor <NUM> is configured to detect the acceleration in a first cycle and output a first signal S1. The first signal S1 includes a detected value. The detected value corresponds to the movement of the hand <NUM>. For example, the first signal S1 changes when the hand <NUM> moves. The detected value that is included in the first signal S1 changes according to the acceleration corresponding to the movement of the hand <NUM>. The sensor <NUM> is configured to output the first signal S1 in the first cycle. The first cycle is, for example, the sampling cycle of the acceleration. The first cycle is, for example, not less than <NUM> and not more than <NUM>. The first cycle is defined by a timer provided in the processor <NUM> of the measuring device <NUM>. The processor <NUM> may change a length of the first cycle.

As shown in <FIG>, the first signal S1 is supplied to the processor <NUM>. The processor <NUM> is configured to acquire the first signal S1.

The direction of the acceleration detected by the sensor <NUM> is arbitrary. The acceleration in any direction is represented as components along three axes. The detected value that is included in the first signal S1 includes a component along a first axis of the acceleration, a component along a second axis of the acceleration, and a component along a third axis of the acceleration. The second axis crosses the first axis. The third axis crosses a plane including the first and second axes. For example, the first axis is an X-axis direction, the second axis is a Y-axis direction, and the third axis is a Z-axis direction.

Thus, the detected value may include three values in accordance with the three axes. Then, considering the orientation of the acceleration, the detected value may be divided into following six components: a first component of one of positive or negative along the first axis, a second component of one of positive or negative along the second axis of the acceleration, a third component of one of positive or negative along the third axis of the acceleration, a fourth component of the other of positive or negative along the first axis of the acceleration, a fifth component of the other of positive or negative along the second axis of the acceleration, and a sixth component of the other of positive or negative along the third axis of the acceleration. The acceleration that is received by the sensor <NUM> according to the movement of the hand <NUM> can be represented by the magnitudes (the absolute values) of these six components. The three axes may be arbitrarily set in the sensor <NUM>. For convenience of description hereinbelow, the first to third components are taken to be positive, and the fourth to sixth components are taken to be negative.

<FIG> are schematic views illustrating a signal of the scratching detection system according to the first embodiment.

In these figures, the horizontal axis is a time tm. The vertical axis of <FIG> is an acceleration Ax along the first axis. The vertical axis of <FIG> is an acceleration Ay along the second axis. The vertical axis of <FIG> is an acceleration Az along the third axis.

As shown in <FIG>, accelerations that relate to the three axes are detected in the first cycle T1. As shown in <FIG>, a first component threshold value V1 that relates to a first component A1 (e.g., a positive value) along the first axis and a fourth component threshold value V4 that relates to a fourth component A4 (e.g., a negative value) along the first axis are established. For example, the movement of the hand <NUM> relating to the first axis is large when the first component A1 (e.g., a positive value) exceeds the first component threshold value V1. For example, the movement of the hand <NUM> relating to the first axis is large when the absolute value of the fourth component A4 (e.g., a negative value) exceeds the fourth component threshold value V4.

Similarly, as shown in <FIG>, a second component threshold value V2 that relates to a second component A2 (e.g., a negative value) along the second axis and a fifth component threshold value V5 that relates to a fifth component A5 (e.g., a negative value) along the second axis are established. As shown in <FIG>, a third component threshold value V3 that relates to a third component A3 (e.g., a negative value) along the third axis and a sixth component threshold value V6 that relates to a sixth component A6 (e.g., a negative value) along the third axis are established.

In the embodiment, the processor <NUM> can determine whether the person <NUM> is scratching based on at least one of such six components relating to the acceleration. For example, when at least one of such six components exceeds the threshold value, it is determined that the person <NUM> is scratching. Such determination operation is executed in a second cycle T2 (referring to <FIG>).

The second cycle T2 is, for example, not less than <NUM> seconds and not more than <NUM> seconds. In one example, the second cycle T2 is not less than <NUM> times and not more than <NUM> times, e.g., <NUM> times the first cycle T1.

For example, the processor <NUM> is configured to perform the following determination operation based on the first signal S1. The second cycle T2 includes multiple first cycles T1. For example, the determination operation is executed for each second cycle T2. The determination operation includes detecting whether the person <NUM> is scratching based on multiple parameters.

In one example, the multiple parameters include the following first occurrence count and the following first consecutive count. The first occurrence count is the number of times that the absolute value of the detected value (e.g., the component of the acceleration relating to one of the three axes) exceeds the threshold value in one of the second cycles T2. The first consecutive count is the maximum value of the number of times that the absolute value of the detected value consecutively exceeds the threshold value in one of the second cycles T2. It was found that scratching behavior can be more accurately detected by detecting whether the person <NUM> is scratching based on such multiple parameters.

In one example, positive acceleration components (the first component A1, the second component A2, and the third component A3) may be used as the detected value. In such a case, in the example of <FIG>, the number of times (the first occurrence count) that the positive acceleration Ax exceeds the first component threshold value V1 in one of the second cycles T2 is <NUM>. In the example of <FIG>, the maximum value (the first consecutive count) of the number of times that the absolute value of the detected value consecutively exceeds the threshold value in one of the second cycles T2 is <NUM>. The processor <NUM> detects whether the person <NUM> is scratching based on such an occurrence count and such a consecutive count regarding to one of the three axes. It is determined that the person <NUM> is scratching when the occurrence count and the consecutive count (or a function of these) regarding to the positive acceleration Ax are greater than threshold values. It is determined that the person <NUM> is not scratching when the occurrence count and the consecutive count (or a function of these) regarding to the positive acceleration Ax are not more than the threshold values.

It can be determined that the person <NUM> is scratching when the occurrence count and the consecutive count (or a function of these) regarding to the positive acceleration Ay are greater than threshold values, even if the occurrence count and the consecutive count (or a function of these) regarding to the positive acceleration Ax are not more than the threshold values.

It can be determined that the person <NUM> is scratching when the occurrence count and the consecutive count (or a function of these) regarding to the positive acceleration Az are greater than threshold values, even if the occurrence count and the consecutive count (or a function of these) regarding to the positive acceleration Ay and the positive acceleration Ax are not more than the threshold values.

For example, whether the person <NUM> is scratching may be comprehensively determined by performing such processing for the three axes. For example, the scratching may be determined using the result of a calculation (including the sum, etc.) of detected values relating to two or more of the three axes.

In the embodiment, the multiple parameters may further include the following first average count. The first average count is the average value of the absolute values of the detected value exceeding the threshold value in one of the second cycles T2. For example, in the example of <FIG>, the absolute value of the detected value (e.g., the positive acceleration Ax) exceeding the threshold value (e.g., the first component threshold value V1) in one of the second cycles T2 is averaged. In the example of <FIG>, eleven detected values (e.g., the positive accelerations Ax) exceed the threshold value (e.g., the first component threshold value V1). The average value of the eleven detected values (e.g., the positive accelerations Ax) corresponds to the first average count. The scratching can be more accurately detected by detecting whether the person <NUM> is scratching based on multiple parameters that include the first average count.

In the embodiment, the scratching behavior may be detected by considering positive and negative accelerations relating to the three axes.

For example, as described above, the detected value in the first signal S1 may include the first to sixth components A1 to A6 described above. The first component A1 is the component of one of positive or negative along the first axis of the acceleration. The second component A2 is the component of one of positive or negative along the second axis of the acceleration. The third component A3 is the component of one of positive or negative along the third axis of the acceleration. The fourth component A4 is the component of the other of positive or negative along the first axis of the acceleration. The fifth component A5 is the component of the other of positive or negative along the second axis of the acceleration. The sixth component A6 is the component of the other of positive or negative along the third axis of the acceleration. The processor <NUM> is configured to perform the determination operation which includes determining the person <NUM> is scratching when at least one of a first axis parameter based on the first and fourth components A1 and A4, a second axis parameter based on the second and fifth components A2 and A5, or a third axis parameter based on the third and sixth components A3 and A6 exceeds an threshold value.

For example, a first axis parameter P1 is represented by <MAT>.

"N1" is the number of times that the absolute value of the first component A1 exceeds the absolute value of the first component threshold value V1 relating to the first component A1 in one of the second cycles T2. "C1" is the maximum value of the number of times that the absolute value of the first component A1 consecutively exceeds the absolute value of the first component threshold value V1 in one of the second cycles T2. "M1" is the average value of the absolute values of the first component A1 exceeding the absolute value of the first component threshold value V1 in one of the second cycles T2. "n1" is a coefficient. "c1" is a coefficient. "m1" is a coefficient. These coefficients are, for example, positive. "N4" is the number of times that the absolute value of the fourth component A4 exceeds the absolute value of the fourth component threshold value V4 relating to the fourth component A4 in one of the second cycles T2. "C4" is the maximum value of the number of times that the absolute value of the fourth component A4 consecutively exceeds the absolute value of the fourth component threshold value V4 in one of the second cycles T2. "M4" is the average value of the absolute values of the fourth component A4 exceeding the absolute value of the fourth component threshold value V4 in one of the second cycles T2. "n4" is a coefficient. "c4" is a coefficient. "m4" is a coefficient. For example, "n1", "n4", "m1", and "m4" are one of positive or negative, and "c1" and "c4" are the other of positive or negative. For example, "n1", "n4", "m1", and "m4" are positive, and "c1" and "c4" are negative.

For example, a second axis parameter P2 is represented by <MAT>.

"N2" is the number of times that the absolute value of the second component A2 exceeds the absolute value of the second component threshold value V2 relating to the second component A2 in one of the second cycles T2. "C2" is the maximum value of the number of times that the absolute value of the second component A2 consecutively exceeds the absolute value of the second component threshold value V2 in one of the second cycles T2. "M2" is the average value of the absolute values of the second component A2 exceeding the absolute value of the second component threshold value V2 in one of the second cycles T2. "n2" is a coefficient. "c2" is a coefficient. "m2" is a coefficient. "N5" is the number of times that the absolute value of the fifth component A5 exceeds the absolute value of the fifth component threshold value V5 relating to the fifth component A5 in one of the second cycles T2. "C5" is the maximum value of the number of times that the absolute value of the fifth component A5 consecutively exceeds the absolute value of the fifth component threshold value V5 in one of the second cycles T2. "M5" is the average value of the absolute values of the fifth component A5 exceeding the absolute value of the fifth component threshold value V5 in one of the second cycles T2. "n5" is a coefficient. "c5" is a coefficient. "m5" is a coefficient. For example, "n2", "n5", "m2", and "m5" are one of positive or negative, and "c2" and "c5" are the other of positive or negative. For example, "n2", "n5", "m2", and "m5" are positive, and "c2" and "c5" are negative.

For example, a third axis parameter P3 is represented by <MAT>.

"N3" is the number of times that the absolute value of the third component A3 exceeds the absolute value of the third component threshold value V3 relating to the third component A3 in one of the second cycles T2. "C3" is the maximum value of the number of times that the absolute value of the third component A3 consecutively exceeds the absolute value of the third component threshold value V3 in one of the second cycles T2. "M3" is the average value of the absolute values of the third component A3 exceeding the absolute value of the third component threshold value V3 in one of the second cycles T2. "n3" is a coefficient. "c3" is a coefficient. "m3" is a coefficient. "N6" is the number of times that the absolute value of the sixth component A6 exceeds the absolute value of the sixth component threshold value V6 relating to the sixth component A6 in one of the second cycles T2. "C6" is the maximum value of the number of times that the absolute value of the sixth component A6 consecutively exceeds the absolute value of the sixth component threshold value V6 in one of the second cycles T2. "M6" is the average value of the absolute values of the sixth component A6 exceeding the absolute value of the sixth component threshold value V6 in one of the second cycles T2. "n6" is a coefficient. "c6" is a coefficient. "m6" is a coefficient. For example, "n3", "n6", "m3", and "m6" are one of positive or negative, and "c3" and "c6" are the other of positive or negative. For example, "n3", "n6", "m3", and "m6" are positive, and "c3" and "c6" are negative.

As described below, it was found that the detection result using the first to third axis parameters P1 to P3 such as those described above had a good match to the actual observation result indicating whether the person <NUM> is scratching. For example, "N1" corresponds to the number of times that the absolute value of the acceleration exceeds the absolute value of the threshold value. The scratching behavior of the person <NUM> corresponds to a back and forth motion (a repeated movement) of the hand. Accordingly, it is considered that "N1" becomes relatively large when the person <NUM> is scratching. On the other hand, "C1" is the number of times that the absolute value of the acceleration consecutively exceeds the absolute value of the threshold value. The movement of the person <NUM> seems not to correspond to a back and forth motion of the hand when the number of times (i.e., duration of the acceleration) that the absolute value of the acceleration consecutively exceeds the absolute value of the threshold value. Accordingly, it is considered that scratching behavior can be more accurately evaluated by removing the movement of "C1", which does not correspond to scratching behavior, from the movement corresponding to "N1", which indicates the simple number of times. For example, by setting "n1" to be positive and "c1" to be negative, the effects of "C1", which does not correspond to scratching, can be effectively removed.

For example, by using "N1" and "C1", by setting the coefficient "n1" to one of positive or negative, and by setting "c1" to the other of positive or negative, the effects of "C1", which does not correspond to scratching behavior, can be removed more effectively than the result of the evaluation using only "N1".

The description of the coefficients described above is applicable also to the other axes. For example, coefficients n1, n2, n3, n4, n5, and n6 are one of positive or negative, and coefficients c1, c2, c3, c4, c5, and c6 are the other of positive or negative. The effects of the acceleration (the movement) that does not correspond to scratching behavior can be effectively removed thereby.

In the embodiment, it is favorable for the absolute value of "n1" to be not less than <NUM>/<NUM> and not more than <NUM> times the absolute value of "c1". It is favorable for the absolute value of "n2" to be not less than <NUM>/<NUM> and not more than <NUM> times the absolute value of "c2". It is favorable for the absolute value of "n3" to be not less than <NUM>/<NUM> and not more than <NUM> times the absolute value of "c3". It is favorable for the absolute value of "n4" to be not less than <NUM>/<NUM> and not more than <NUM> times the absolute value of "c4". It is favorable for the absolute value of "n5" to be not less than <NUM>/<NUM> and not more than <NUM> times the absolute value of "c5". It is favorable for the absolute value of "n6" to be not less than <NUM>/<NUM> and not more than <NUM> times the absolute value of "c6". For example, "N1" to "N6" are values corresponding to the occurrence count (the number of times in the second cycle T2), and "C1" to "C6" correspond to the consecutive occurrence count. Accordingly, the units of the values of "N1" to "N6" are the same as the units of the values of "C1" to "C6". Accordingly, because the absolute values of the coefficients n1, n2, n3, n4, n5, and n6 are relatively near the absolute values of the coefficients c1, c2, c3, c4, c5, and c6, the effects of the acceleration (the movement) that does not correspond to scratching can be effectively removed.

In the embodiment, the polarity (positive of negative) of coefficients m1 to m6 is the same as the polarity (positive of negative) of the coefficients n1 to n6. For example, "M1" corresponds to the magnitude of the acceleration. A large "M1" corresponds to a movement of the person <NUM> being scratching. Because the polarity (positive of negative) of the coefficients m1 to m6 is the same as the polarity (positive of negative) of the coefficients n1 to n6, the scratching behavior can be more accurately determined (and detected).

The first to sixth components A1 to A6 can be defined by using the acceleration G due to gravity, for example. The acceleration G due to gravity is, for example, <NUM>/s<NUM>. When the first to third components A1 to A3 are positive, the first to third component threshold values V1 to V3 are, for example, <NUM>. When the fourth to sixth components A4 to A6 are negative, the fourth to sixth component threshold values V4 to V6 are, for example, -<NUM>.

By such first to third axis parameters P1 to P3, the scratching behavior of the person <NUM> can be detected with high accuracy.

<FIG> is a graph illustrating a characteristic obtained by the scratching detection system according to the first embodiment.

<FIG> illustrates the result of evaluating the scratching behavior of the person <NUM> in one evaluation period. The horizontal axis of <FIG> corresponds to the result of the processor <NUM> detecting the scratching behavior based on the first to third axis parameters P1 to P3 described above. The horizontal axis of <FIG> is an estimated scratching time ES in which it is determined that the person <NUM> is scratching. The vertical axis corresponds to the result of detecting the scratching behavior from the result of imaging the person <NUM>. The vertical axis is an observed scratching time OS in which it is determined that the person <NUM> is scratching from the imaging result. In the example of <FIG>, the first to third parameters P1 to P3 are represented by Formulas (<NUM>) to (<NUM>) described above.

As shown in <FIG>, the estimated scratching time ES has a good match with the observed scratching time OS. Spearman's correlation coefficient of the two is <NUM>.

In the embodiment, the first axis parameter P1 may be represented by <MAT>.

The second axis parameter P2 may be represented by <MAT>.

The third axis parameter P3 may be represented by <MAT>.

The scratching can be detected with relatively high accuracy even when the determination operation is executed based on the first to third axis parameters P1 to P3. The motion seems not to correspond to a back and forth motion when the number of times that the detected value of the acceleration consecutively exceeds the threshold value is large. The scratching behavior can be detected with relatively high accuracy by employing a parameter that excludes the number of times that the acceleration consecutively exceeds the threshold value from the number of times that the detected value of the acceleration exceeds the threshold value.

In the embodiment, the scratching behavior may be detected based on the acceleration that is one of positive or negative. For example, the detected value includes the first to third components A1 to A3 described above. The determination operation of the processor <NUM> may include, for example, determining that the person <NUM> is scratching when at least one of the first axis parameter P1 based on the first component A1, the second axis parameter P2 based on the second component A2, or the third axis parameter P3 based on the third component A3 exceeds an threshold value.

For example, the first axis parameter P1 may be represented by <MAT>.

As described above, "N1" is the number of times that the absolute value of the first component A1 exceeds the absolute value of the first component threshold value V1 in one of the second cycles T2. "C1" is the maximum value of the number of times that the absolute value of the first component A1 consecutively exceeds the absolute value of the first component threshold value V1 in one of the second cycles T2.

As described above, "N2" is the number of times that the absolute value of the second component A2 exceeds the absolute value of the second component threshold value V2 in one of the second cycles T2. "C2" is the maximum value of the number of times that the absolute value of the second component A2 consecutively exceeds the absolute value of the second component threshold value V2 in one of the second cycles T2.

As described above, "N3" is the number of times that the absolute value of the third component A3 exceeds the absolute value of the third component threshold value V3 in one of the second cycles T2. "C3" is the maximum value of the number of times that the absolute value of the third component A3 consecutively exceeds the absolute value of the third component threshold value V3 in one of the second cycles T2. The scratching behavior can be detected with relatively high accuracy even when the determination operation is performed based on such first to third axis parameters P1 to P3. As described above, it is considered that the scratching behavior corresponds to a repeated movement of the hand. Accordingly, it is considered that scratching behavior can be detected with relatively high accuracy by evaluating using a parameter relating to the movement toward one of positive or negative.

In the example of <FIG>, the first to third parameters P1 to P3 are represented by Formulas (<NUM>) to (<NUM>) described above. The horizontal axis of <FIG> corresponds to the result of the processor <NUM> detecting the scratching of the person <NUM> based on such first to third axis parameters P1 to P3. The horizontal axis of <FIG> is the estimated scratching time ES in which it is determined that the person <NUM> is scratching. The vertical axis is the observed scratching time OS in which it is determined that the person <NUM> is scratching from the imaging result. As shown in <FIG>, even when Formulas (<NUM>) to (<NUM>) are used, the estimated scratching time ES has a good match with the observed scratching time OS. Spearman's correlation coefficient of the two is <NUM>. Comparing the result of <FIG> and the result of <FIG>, the slopes of the actual measured value (the vertical axis) with respect to the predicted value (the horizontal axis) are different from each other. For this aspect, it is considered that scratching can be evaluated with higher accuracy by employing Formulas (<NUM>) to (<NUM>).

In the example of <FIG>, the first to third parameters P1 to P3 are represented by Formulas (<NUM>) to (<NUM>) described above. The horizontal axis of <FIG> corresponds to the result of the processor <NUM> detecting the scratching of the person <NUM> based on such first to third axis parameters P1 to P3. The horizontal axis of <FIG> is the estimated scratching time ES in which it is determined that the person <NUM> is scratching. The vertical axis is the observed scratching time OS in which it is determined that the person <NUM> is scratching from the imaging result. As shown in <FIG>, even when Formulas (<NUM>) to (<NUM>) are used, the estimated scratching time ES has a good match with the observed scratching time OS. Spearman's correlation coefficient of the two is <NUM>. Comparing the result of <FIG> and the result of <FIG>, the slopes of the actual measured value (the vertical axis) with respect to the predicted value (the horizontal axis) are slightly different. For this aspect, it is considered that scratching can be evaluated with higher accuracy by employing Formulas (<NUM>) to (<NUM>).

In the embodiment, for example, when the first axis is along the direction of the acceleration due to the movement of the hand <NUM> associated with the scratching behavior, there are cases where the scratching behavior can be accurately evaluated by an evaluation relating to the first axis. In such a case, the evaluations that relate to the second and third axes may not be performed. For example, the evaluation may be performed using a parameter relating to at least one of the first to third axes. For example, the scratching behavior may be determined using at least one of the first to third axis parameters P1 to P3.

In the embodiment described above, the processor <NUM> detects whether the person <NUM> is scratching from the comparison result between the calculation result of the first to third axis parameters and their corresponding threshold values (e.g., thresholds). As described above, the first to third axis parameters P1-P3 can be defined by one of equation sets of a first set (<NUM>)-(<NUM>), a second set (<NUM>)-(<NUM>), a third set (<NUM>)-(<NUM>), and a fourth set (<NUM>)-(<NUM>), for example. In the embodiment, one of the axis parameters can be defined by one of the equation sets, and other one of the axis parameters can be defined by different one of the equation sets. For example, the determination operation may include performing such multiple processing (e.g., the first processing, the second processing, etc.). For example, the determination operation may include a first processing based on the first equation set, and a second processing based on the second equation set. Further, the determination operation may include a first processing that relates to the first axis parameter and a second processing that relates to the second axis parameter. Then, as described above, at least one of the first processing or the second processing may include detecting (determining) the scratching behavior by comparing between the detected value relating to at least one of the first to third axes and corresponding threshold value.

The parameters (including the coefficients), etc., that are described above may be preset. In the scratching detection system, multiple modes may be provided, and different parameters (including coefficients) may be employed in the multiple modes. The parameters (including the coefficients) may be modifiable to match the state of the person <NUM>. The coefficients of the parameters may be corrected by machine learning, etc..

An example of the determination operation (i.e., the first operation) performed by the processor <NUM> will now be described.

<FIG> is a flowchart illustrating the operation of the scratching detection system according to the embodiment.

<FIG> illustrates the operation of the processor <NUM> of the measuring device <NUM>. An example of detecting the scratching behavior while sleeping will now be described.

As shown in <FIG>, the processor <NUM> acquires the first signal S1 including the detected value (the acceleration) from the sensor <NUM> (step S101). In one example, the processor <NUM> acquires the detected value in the first cycle T1.

In the example, the processor <NUM> determines whether or not the person <NUM> is asleep based on the detected value (step S102). When determined to be asleep, the flow proceeds to the next step S103. When determined not to be asleep, step S101 and step S102 are repeated. In step S102, for example, sleeping is determined to be when a state in which the detected value (the acceleration) is less than a certain threshold value continues for not less than a predefined time.

In step S103, the processor <NUM> acquires the first signal S1 including the detected value (the acceleration) from the sensor <NUM>. For example, the processor <NUM> acquires the detected value in the first cycle T1.

The processor <NUM> determines the body movement of the person <NUM> (the movement of the hand <NUM>) based on the detected value (the acceleration) acquired in step S103 (and step S104). At this time, for example, the processor <NUM> determines whether there is relatively large body movement or not based on the following movement parameters.

For example, the detected value includes a first acceleration component B1 along the first axis of the acceleration, a second acceleration component B2 along the second axis of the acceleration, and a third acceleration component B3 along the third axis of the acceleration. The second axis crosses the first axis. The third axis crosses a plane including the first and second axes. The processor <NUM> determines that the hand <NUM> has moved when the following movement parameter exceed the threshold values relating to the movement parameter. The movement parameter is the root mean square of the first acceleration component B1, the second acceleration component B2, and the third acceleration component B3.

For example, a movement parameter Pm is represented by Pm = ((B1)<NUM>+ (B2)<NUM>+ (B3)<NUM>) <NUM>/<NUM> using the first acceleration component B1, the second acceleration component B2, and the third acceleration component B3. The processor <NUM> determines that there is body movement (a movement of the hand <NUM>) when such a movement parameter Pm exceeds the threshold value. When the first acceleration component B1, the second acceleration component B2, and the third acceleration component B3 are the acceleration due to gravity, in one example, the threshold value that relates to the parameter Pm is <NUM> or more, for example. For example, the threshold value that relates to the parameter Pm may be <NUM> or less. Whether there is relatively large body movement of the hand <NUM> is determined by comparing such a threshold value and the movement parameter Pm.

When the processor <NUM> determines that there is no body movement or relatively small body movement (movement of the hand <NUM>) in step S104, the flow returns to step S103. When the processor <NUM> determines that there is body movement (movement of the hand <NUM>) in step S104, the flow proceeds to the following step S110.

In step S110, the processor <NUM> performs the determination operation including the first processing described above and performs the determination of scratching behavior. For example, the processor <NUM> determines whether the person <NUM> is scratching based on at least one of the multiple parameters (the first to third axis parameters P1 to P3, etc.) described above. The determination whether the person <NUM> is scratching is repeatedly performed in the second cycle T2.

In the example, it is determined whether or not the sleeping has ended (step S115). When the sleeping has not ended, the flow returns to step S103, and the processing described above is repeated.

In the example, when the sleeping has ended and the measurement has ended, for example, the processor <NUM> outputs the scratching result (the detection result) to an external communication device or the like (step S120). For example, the detection result is output to the output device <NUM>. For example, the detection result may be performed at any step between step S103 to step S115.

For example, in the determination operation, the first processing includes outputting the detection result of the scratching behavior for each second cycle T2. For example, the first processing may include outputting the detection result of the scratching behavior in the second cycle T2.

As described above, the processor <NUM> may be configured to perform the determination operation when the hand <NUM> is determined to have moved based on the first signal S1 (step S104). The amount of processing can be reduced by performing the first processing that determines the person <NUM> is scratching when the hand <NUM> is determined to have moved. For example, the power consumption can be reduced.

An example of the operation of the output device <NUM> will now be described.

As shown in <FIG>, the output device <NUM> acquires the detection result of relating to whether the person <NUM> is scratching from the measuring device <NUM> (step S210). For example, the processor <NUM> of the output device <NUM> acquires the detection result relating to whether the person <NUM> is scratching in an evaluation period (e.g., one sleep period, etc.) designated via the communicator <NUM> and the communicator <NUM>.

The processor <NUM> of the output device <NUM> performs a calculation for the scratching time, the scratching count, etc. (step S220). For example, based on the detection result relating to whether the person <NUM> is scratching, the processor <NUM> is configured to generate information relating to at least one of the scratching count, the scratching time, or the relative scratching time from the calculation. The scratching count is the number of the second cycles T2 in which scratching behavior is determined in the evaluation period that includes the multiple second cycles T2. The scratching time is the sum of the consecutive second cycles T2 in the evaluation period in which scratching behavior is determined. The relative scratching time is the ratio of the scratching time to the evaluation period (the length thereof).

The display <NUM> of the output device <NUM> displays such information (step S230). For example, the display <NUM> is configured to display the information relating to at least one of the scratching count, the scratching time, or the relative scratching time.

The display <NUM> may be configured to display at least one of whether the person <NUM> is scratching, the first occurrence count described above, or the first consecutive count described above acquired from the measuring device <NUM>.

In the embodiment, scratching behavior that is detected with high accuracy can be displayed.

At least a portion of the processing performed by the processor <NUM> of the output device <NUM> may be performed by the server <NUM> (referring to <FIG>). The output device <NUM> may acquire the result of the processing performed by the server <NUM>, and the display <NUM> may display the result.

<FIG> is a schematic view illustrating an output device of the scratching detection system according to the embodiment. As shown in <FIG>, the output device <NUM> includes the display <NUM>. In the example, for example, the display <NUM> displays a scratching count R1 and a scratching time R2 by using figures. The display <NUM> may be configured to display a scratching intensity R3. In the example, the scratching intensity R3 corresponds to the sum in the second cycle T2 of the sums of "M1" to "M6" at times at which scratching behavior is determined. In the example, average data Ra of a prescribed period is displayed in addition to newest data Rn.

<FIG> is a schematic view illustrating an output device of the scratching detection system according to the embodiment. As shown in <FIG>, the display <NUM> of the output device <NUM> may be configured to display the change by day of the scratching count R1, the scratching time R2, etc., in a graph, etc. In the example, the scratching count R1 is displayed as a line. The scratching time R2 is displayed in a bar graph. In the example, the display <NUM> is configured to display information relating to a skin damage index of the person <NUM> by using shading (colors, etc.) of the bar graph.

For example, there is a reference example that directly measures the scratching count for a measurement time. Conversely, in the embodiment, whether the person <NUM> is scratching is determined for each second cycle T2. In the embodiment, the calculation amount can be reduced thereby. For example, the measuring device <NUM> can be downsized. For example, the power consumption of the measuring device <NUM> can be reduced. In the embodiment, for example, the scratching can be determined in substantially real time for each second cycle T2.

For example, a mountable measuring device <NUM> of the hand <NUM> of the person <NUM> is used. In the measuring device <NUM>, whether the person <NUM> is scratching is determined for each second cycle T2 (e.g., <NUM> second, etc.). The result of the determination may be stored in the memory <NUM>, etc. For example, the result can be supplied to the output device <NUM> by wireless communication or wired communication. The output device <NUM> is configured to display the information relating to whether the person <NUM> is scratching. The output device <NUM> may be configured to supply the information relating to whether the person <NUM> is scratching that is acquired to the server <NUM>, etc., via the cloud (e.g., the network <NUM>) by any wireless communication or wired communication. The output device <NUM> may be configured to receive analysis data from the cloud and may be configured to display the analysis data in the display <NUM> of the output device <NUM>.

For example, the server <NUM> is configured to store and manage the information (the data) acquired from the output device <NUM>. The server <NUM> may be configured to supply the analysis result to the output device <NUM>.

For example, the processor <NUM> of the measuring device <NUM> determines whether the person <NUM> is scratching by calculating a feature for each second cycle T2. The processor <NUM> or the server <NUM> acquires the result and calculates the scratching time, the scratching count, etc., for the evaluation period. The output device <NUM> displays this data and notifies the person <NUM> or a staff member (a health care professional, etc.) of the person <NUM>.

<FIG> is a schematic view illustrating a processing device according to the embodiment.

<FIG> shows an example of a processing device of the processor <NUM>, the processor <NUM>, the server <NUM>, etc. <FIG> is a functional block diagram. As shown in <FIG>, the processing device includes, for example, a CPU (Central Processing Unit) <NUM>, an I/F <NUM>, a display <NUM>, ROM (Read Only Memory) <NUM>, RAM (Random Access Memory) <NUM>, a memory device <NUM>, etc. The various operations described above are performed by, for example, a CPU. For example, the multiple components that are included in the processing device can communicate using a communication path <NUM>, etc. The communication may be wired or wireless.

According to the embodiments, a scratching detection system can be provided in which scratching behavior can be more accurately detected.

Hereinabove, exemplary embodiments of the invention are described with reference to specific examples. However, the embodiments of the invention are not limited to these specific examples but the invention is solely limited by the claims.

For example, one skilled in the art may similarly practice the invention by appropriately selecting specific configurations of components included in scratching detection systems such as measuring devices, sensors, processors, output devices, displays, etc., from known art. Such practice is included in the scope of the claimed invention to the extent that similar effects thereto are obtained.

Further, any two or more components of the specific examples may be combined within the extent of technical feasibility and are included in the scope of the invention as claimed.

Moreover, all semiconductor devices practicable by an appropriate design modification by one skilled in the art based on the semiconductor devices described above as embodiments of the invention also are within the scope of the invention to the extent of the claimed invention.

Claim 1:
A scratching detection system (<NUM>) comprising:
a sensor (<NUM>) configured to detect an acceleration corresponding to a movement of a hand of a person and configured to output a first signal including a detected value corresponding to the acceleration in a first cycle; and
a processor (<NUM>) configured to acquire the first signal and to detect a scratching behavior of the person in a second cycle based on a plurality of parameters, the second cycle including a plurality of the first cycles,
characterized in that
the plurality of parameters including a first occurrence count and a first consecutive count,
the first occurrence count being a number of times that an absolute value of the detected value exceeds a threshold value in the second cycle,
the first consecutive count being a maximum value of a number of times that the absolute value of the detected value consecutively exceeds the threshold value in the second cycle.