JP2016177397A - Fall detection terminal and program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a fall detection terminal capable of performing a high-accuracy fall determination.SOLUTION: A fall detection terminal 1 comprises an acceleration sensor 2 and a fall monitoring unit 15. The acceleration sensor 2 detects the movement of the fall detecting terminal to output acceleration data. The fall monitoring unit 15 includes an impact detection part 16, a fall detection part 17, and a fall determination part 18. The impact detection part 16 detects an impact generated in the fall detection terminal 1 from the acceleration data to calculate an impact evaluation value indicating a fall possibility on the basis of the impact. The fall detection part 17 detects fall characteristics, from the acceleration data, appearing when the fall detection terminal freely falls and calculates a fall evaluation value indicating a fall possibility on the basis of the fall characteristics. The fall determination part 18 determines whether a user is falling or not, on the basis of a comprehensive evaluation value in which the impact evaluation value and the fall evaluation value are comprehensively evaluated.SELECTED DRAWING: Figure 1

Description

  The present invention relates to a fall detection terminal having a function of detecting a fall of a user (carrier).

  Conventionally, methods for detecting a person's fall have been proposed. For example, in the method of Patent Document 1, the absolute value of the acceleration digital signal value (acceleration absolute value) obtained from an acceleration sensor attached to a human trunk is greater than or equal to a threshold value, that is, in normal operation such as walking. A person's fall is detected based on whether a large acceleration that is rarely detected is detected. Moreover, in the method of patent document 2, a person's fall is detected based on whether the acceleration sensor fixed to the human body detected the acceleration close | similar to free fall.

JP 2009-163538 A JP 2004-252618 A

  However, there are a wide variety of fall modes, and it is often difficult to detect with the conventional fall detection methods. For example, when a person loses consciousness and falls, the free fall characteristic is easily observed due to weakness, but the impact tends to be small because the head and body collide with the ground first. On the other hand, when a person falls down in a conscious state, such as when he hits and falls, the arm or the like is moved to protect the body, so the free fall characteristic tends to be difficult to observe. Therefore, there has been a problem that it is difficult to determine a fall with high accuracy simply by observing only the absolute value (peak value) of acceleration or simply the drop characteristics as in the prior art.

  The present invention has been made in view of the above problems, and an object of the present invention is to provide a fall detection terminal that can perform a fall determination with high accuracy.

  The fall detection terminal according to the present invention is a fall detection terminal that is carried by a user and detects the fall of the user. The fall detection terminal detects a fall of the fall detection terminal and outputs a motion data. Impact detection means for detecting an impact generated in the fall detection terminal and calculating an impact evaluation value indicating a fall possibility based on the impact, and a drop characteristic that appears when the fall detection terminal falls freely from the motion data. Based on the drop detection means for detecting and calculating a drop evaluation value indicating the possibility of falling based on the drop characteristics, and the overall evaluation value obtained by comprehensively evaluating the impact evaluation value and the drop evaluation value, the user A fall judging means for judging whether or not the vehicle has fallen.

  With this configuration, the possibility of a fall based on the impact (impact evaluation value) is obtained from the magnitude of the impact applied when the fall falls, and the fall based on the fall characteristics from the fall characteristics of the free fall that occurs during the fall Find the possibility (drop evaluation value). Based on the overall evaluation value that comprehensively evaluates the possibility of falling based on impact (impact evaluation value) and the possibility of falling based on drop characteristics (fall evaluation value), it is determined whether or not the user has fallen ( (Falling judgment). As a result, it is possible to perform the fall determination with higher accuracy than in the case where the determination is made based solely on the peak value of the acceleration or simply based on the drop characteristic.

  Further, in the fall detection terminal of the present invention, the fall determination means calculates the activity amount of the user from the motion data, and responds to the activity amount of the user in a predetermined period based on the occurrence of the impact. Then, the overall evaluation value may be obtained by adding a weight to the impact evaluation value and the drop evaluation value.

  With this configuration, the activity amount of the user is calculated from the motion data, and when calculating the overall evaluation value, the impact evaluation value and the drop are calculated according to the activity amount for a predetermined period based on the occurrence of the impact (at the time of falling). A weight is added to the evaluation value. Therefore, the fall determination can be performed with high accuracy based on the comprehensive evaluation value in consideration of the activity amount (activity state) of the user at the time of the fall.

  Further, in the fall detection terminal of the present invention, the fall determination means, when the user's activity amount in a predetermined period before the occurrence of the impact is in a resting state smaller than a reference activity amount, the fall evaluation value from the impact evaluation value. The comprehensive evaluation value may be obtained by increasing the weight of the value.

  With this configuration, when the amount of activity before the occurrence of the impact is small, that is, when the user is in a resting state before the occurrence of the impact, the weight of the drop evaluation value is set higher than the impact evaluation value. When a user who has been resting falls due to loss of consciousness or the like, the possibility of falling is more easily reflected in the fall evaluation value than the impact evaluation value. Therefore, it is possible to determine the fall of the user who has been at rest (for example, fall due to loss of consciousness) with high accuracy.

  In the fall detection terminal of the present invention, the fall detection means detects the fall characteristic from the motion data in a first predetermined period before the occurrence of the impact, and the fall determination means has the fall detection means before the occurrence of the impact. The activity amount of the user may be calculated from the motion data in a second predetermined period longer than the first predetermined period.

  With this configuration, the fall characteristic can be appropriately detected from the motion data of the first predetermined period before the occurrence of the impact, and the user's activity amount is determined based on the second predetermined period (the first It is possible to appropriately calculate from motion data for a period longer than the predetermined period.

  In the fall detection terminal of the present invention, the second predetermined period may be set so as not to include the first predetermined period.

  With this configuration, the activity amount of the user can be appropriately calculated from the motion data in the second predetermined period (period not including the first predetermined period) before the occurrence of the impact.

  A fall detection terminal according to the present invention is a fall detection terminal that is carried by a user and detects the fall of the user. The fall detection terminal detects movement of the user and outputs motion data indicating the movement of the user. An impact detection means for detecting an impact generated in the fall detection terminal from the motion data; a fall detection means for detecting a fall characteristic that appears when the fall detection terminal falls freely from the motion data; and A fall determination unit that determines whether or not the user has fallen based on a detection result and a detection result of the drop characteristic, wherein the fall determination unit calculates the activity amount of the user from the motion data. When the user's activity amount in a predetermined period based on the occurrence of the impact is in a resting state that is smaller than the reference activity amount, the fall characteristic is detected from the impact detection result. By increasing the weighting of the results to determine whether the user has fallen.

  With this configuration, whether or not the user has fallen is determined based on the detection result of the impact applied when the user falls and the detection result of the free fall characteristics that occur during the fall (fall determination) I do. In this case, the activity amount of the user is calculated from the motion data, and the impact detection result and the fall characteristic detection result are weighted according to the activity amount for a predetermined period based on the impact occurrence (falling). It is done. When the amount of activity before the occurrence of the impact is small, that is, when the user is in a resting state, the weighting of the detection result of the drop characteristic is made higher than the detection result of the impact. When a user who has been resting falls due to loss of consciousness or the like, the possibility of falling is more easily reflected in the fall evaluation value than the impact evaluation value. Therefore, it is possible to determine the fall of the user who has been at rest (for example, fall due to loss of consciousness) with high accuracy.

  The program of the present invention is a program executed by a fall detection terminal that detects a fall of a user, and the fall detection terminal includes a motion sensor that detects movement of the fall detection terminal and outputs motion data. The program includes a process for detecting an impact generated in the fall detection terminal from the motion data and calculating an impact evaluation value indicating a fall possibility based on the impact, and from the motion data. A process of detecting a fall characteristic that appears when the fall detection terminal falls freely and calculating a fall evaluation value indicating a fall possibility based on the fall characteristic, and comprehensively evaluating the impact evaluation value and the fall evaluation value And a process of determining whether or not the user has fallen based on the comprehensive evaluation value.

  Like this terminal, this program also determines the possibility of falling (impact evaluation value) based on the impact from the magnitude of the impact applied when it falls, and the fall characteristics of free fall that occurs during the fall From this, the possibility of falling (drop evaluation value) based on the drop characteristics is obtained. Based on the overall evaluation value that comprehensively evaluates the possibility of falling based on impact (impact evaluation value) and the possibility of falling based on drop characteristics (fall evaluation value), it is determined whether or not the user has fallen ( (Falling judgment). As a result, it is possible to perform the fall determination with higher accuracy than in the case where the determination is made based solely on the peak value of the acceleration or simply based on the drop characteristic.

  According to the present invention, it is possible to make a fall determination with high accuracy by comprehensively evaluating the magnitude of impact and the free fall characteristics and making a fall decision.

It is a block diagram of a fall detection terminal in an embodiment of the present invention. (A) It is a figure which shows an example of the acceleration data of a fall. (B) It is a figure which shows an example of the acceleration data of non-falling (when a hand is collided with a wall). (A) It is a figure which shows an example of the acceleration data of the fall at the time of rest. (B) It is a figure which shows an example of the acceleration data of the fall during walking. (C) It is a figure which shows an example of the acceleration data of the fall during driving | running | working. It is a flowchart for operation | movement description of the fall detection terminal in embodiment of this invention.

  Hereinafter, a fall detection terminal according to an embodiment of the present invention will be described with reference to the drawings. In the present embodiment, a case of a fall detection terminal used in a monitoring system or the like that detects the fall of an elderly person is exemplified. The function of the fall detection terminal (fall detection function) can be realized by a program stored in a memory or the like of the terminal.

  The fall detection terminal is a terminal device carried by a user (such as an elderly person). For example, the fall detection terminal is composed of a wristband type (watch type) wearable terminal, and a user (such as an elderly person) is worn on the wrist or arm. The fall detection terminal may be a pendant type that hangs from the neck, a head-mounted type that is attached to the head or ear, or a waist-mounted type such as a belt type.

  First, the configuration of the fall detection terminal of the present embodiment will be described with reference to the drawings. FIG. 1 is a block diagram showing the configuration of the fall detection terminal of the present embodiment. As shown in FIG. 1, the fall detection terminal 1 includes an acceleration sensor 2, a mounting sensor 3, an altitude sensor 4, and an emergency button 5. The fall detection terminal 1 includes an operation display unit 6, a vibration unit 7, a power supply unit 8, a wireless communication unit 9, and a monitoring control unit 10.

  The acceleration sensor 2 is a sensor having a function of detecting a user's movement and outputting movement data (acceleration data) indicating the movement of the user, and is composed of, for example, a three-axis acceleration sensor. The acceleration sensor 2 outputs acceleration data detected at a predetermined sampling period. The mounting sensor 3 is a sensor having a function of detecting a mounting state (whether or not it is mounted) on a human body (arm). For example, a capacitive sensor or the like can be used to electrically detect contact and proximity of a human body mounting site. It consists of sensors that can be detected automatically. The altitude sensor 4 is a sensor having a function of detecting a change in the height of the terminal using a change in atmospheric pressure or the like, and is composed of, for example, an atmospheric pressure sensor. The emergency button 5 is an operation unit (emergency operation unit) for a user to make an emergency report in an emergency.

  The operation display unit 6 includes, for example, a touch panel display, and includes a function for displaying various information such as abnormality notification (information display function) and a function for performing various input operations such as a cancel operation (input operation function). Yes. The vibration unit 7 is composed of a vibration device or the like, and has a function of notifying a user of an abnormality or operation acceptance by a stimulus by vibration. The power supply unit 8 includes a battery such as a battery. The wireless communication unit 9 has a function of performing wireless communication with a security terminal 12 installed in a remote monitoring center 11 via, for example, a mobile communication network. In addition, the wireless communication unit 9 has a function of performing wireless communication with a security terminal (not shown) installed in the house by Bluetooth (registered trademark) or specific low power wireless.

  The monitoring control unit 10 is an emergency monitor having a function of performing emergency monitoring based on input from each sensor (acceleration sensor 2, wearing sensor 3, altitude sensor 4) and operation unit (emergency button 5, operation display unit 6). A life monitoring unit 14 having a function of performing life monitoring, and a fall monitoring unit 15 having a function of performing fall monitoring. In addition, the monitoring control unit 10 has a function of controlling each unit of the fall detection terminal 1.

  The emergency monitoring unit 13 has a function of making an emergency report to the monitoring center 11 when the user operates the emergency button 5 in response to an emergency response. For example, if the emergency button 5 is kept pressed for 2 seconds, it is determined that the emergency is abnormal, the emergency abnormality is notified to the monitoring center 11, and the fact that the abnormality is confirmed is notified to the surroundings by vibration, screen, sound, light, or the like. Since the emergency abnormality is an abnormality caused by conscious operation, the cancel operation is not accepted. In addition, even if a life abnormality or a fall abnormality (described later) occurs, if an emergency abnormality occurs, the emergency abnormality takes priority.

  Based on the output of the acceleration sensor 2, the life monitoring unit 14 monitors whether the user (carrier or wearer) is moving to an extent that occurs in daily life, and confirms that the state where the user cannot move due to sudden illness or the like continues. It has a function to report. The life monitoring unit 14 performs life monitoring when in the wearing state (mounted state). For example, a user's movement (body movement) is detected from the output of the acceleration sensor 2, and it is determined that there is a life abnormality if body movement at the daily life level does not occur continuously for a certain period (for example, 1 hour). If it is determined that the life is abnormal, the user is notified by vibration, screen, sound, light, or the like. The life monitoring unit 14 cancels the life abnormality when the body movement is detected during a predetermined cancel time (for example, 10 seconds) or a cancel operation is input after determining / notifying the life abnormality ( Do not report errors). The body movement detection process is the same as the process in the fall monitoring (described later).

  The fall monitoring unit 15 has a function of automatically detecting the fall of a user (carried person, wearer) based on the output of the acceleration sensor 2 and reporting the occurrence of a fall accident. The fall monitoring unit 15 performs fall monitoring when in the mounted state (mounted state). In this case, “impact” and “free fall characteristics” are detected from the output of the acceleration sensor 2, and the detection result is evaluated to detect a fall. Then, after detecting a fall, if a body movement such as standing up and walking cannot be detected for a predetermined time (for example, 15 seconds), it is determined that the fall is abnormal. This predetermined time does not include several seconds (for example, 5 seconds) immediately after the fall detection. If it is determined that the fall is abnormal, the user is notified of this by vibration, screen, sound, light, or the like. The fall monitoring unit 15 performs a process of canceling the fall abnormality (not reporting an abnormality) when a cancel operation is input during a predetermined cancel time (for example, 20 seconds) after determining and notifying that the fall is abnormal.

  Here, the basic logic of the fall detection will be described with reference to FIG. The fall monitoring unit 15 evaluates the impact applied to the terminal during the fall and the drop characteristics generated in the process of the fall, and detects the fall. In particular, in the present invention, the final fall determination is performed by comprehensively evaluating the fallability based on the impact and the fallability based on the drop characteristic. As a configuration for this, the fall monitoring unit 15 includes an impact detection unit 16, a fall detection unit 17, and a fall determination unit 18.

  The impact detection unit 16 has a function of detecting an impact generated in the fall detection terminal 1 from the acceleration data and calculating an impact evaluation value indicating the possibility of the fall based on the impact. Since the fall involves a body collision with the ground or the like, the acceleration changes greatly due to an impact applied to the fall detection terminal 1. Therefore, the impact detection unit 16 is configured to detect an impact applied to the terminal at the time of falling based on the peak value of acceleration.

2 and 3 are diagrams showing examples of acceleration data. In this case, as preprocessing, acceleration data output for each of the three axes (X axis, Y axis, Z axis) is scalarized and subjected to moving average processing (low-pass filter). The impact detection unit 16 detects an acceleration peak (time T1) equal to or greater than a predetermined threshold (for example, 30 m / sec ² ), and obtains the peak value P. And the impact detection part 16 calculates | requires the impact evaluation value Es based on the peak value P by using the following formula | equation 1, for example. Here, c is an adjustment coefficient for comprehensive evaluation.
Es = P × c (Formula 1)

The fall detection unit 17 has a function of detecting a fall characteristic that appears when the fall detection terminal 1 freely falls from the acceleration data, and calculating a fall evaluation value that indicates a fall possibility based on the fall characteristic. Since the fall is a phenomenon that causes a fall toward the ground, the acceleration during the fall is smaller than the gravitational acceleration (9.8 m / sec ² ). Therefore, the fall detection unit 17 goes back from the acceleration peak (time T1), finds the intersection with the gravitational acceleration (time T2), and calculates the average acceleration value A from that point to a certain period before (time T3). To do. A period from time T3 to time T2 (for example, 500 msec) can be called a fall determination period. And the fall detection part 17 calculates | requires the fall evaluation value Ef based on the acceleration average value A by using the following formula | equation 2, for example. Here, a is a drop standard constant, and b is an adjustment coefficient for comprehensive evaluation.
Ef = (a−A) × b (Formula 2)

The constant a is an acceleration serving as a reference point for discriminating whether or not there is a drop characteristic, and for example, 8 m / sec ² or gravity acceleration 9.8 m / sec ² is used. If the acceleration average value A is smaller than the constant a, the drop evaluation value Ef takes a positive value.

The fall determination unit 18 has a function of determining whether or not the user has fallen based on a comprehensive evaluation value obtained by comprehensively evaluating the impact evaluation value and the drop evaluation value. There are various modes of falling, and there are cases where the impact is large and the drop characteristics are remarkable, but there are cases where the impact is small and the drop characteristics are noticeable. Therefore, the fall determination unit 18 obtains the overall evaluation value Et by adding the impact evaluation value and the drop evaluation value by using the following expression 3, and when the overall evaluation value is equal to or more than the reference value Th for the fall determination, To detect a person's fall.
Et = Es + Ef (Formula 3)

  In this fall determination part 18, the automatic cancellation determination by a body movement detection is performed. Even if a fall is detected, it is considered that the user is not in a dangerous state if body movement is detected thereafter, so the fall determination unit 18 automatically cancels the fall detection. In this body movement detection (automatic cancellation determination), body movement at a predetermined delay time (for example, 5 seconds) from the peak time point (T1) when the fall determination is made is ignored, and subsequent body movement determination time (for example, 15 seconds). ) To determine whether there is any body movement. The presence or absence of body movement is determined by observing the peak value of acceleration or the amount of change. For example, body movement is detected when a peak value equal to or greater than a predetermined threshold is detected a predetermined number of times, or when the sum of the peak values exceeds a certain value. Alternatively, when the total amount of change in acceleration over a certain period (eg, several seconds) or body movement determination time exceeds a certain value, or when the amount of change in acceleration per unit time exceeds a certain value, Is detected. If no body movement is detected after the automatic cancellation determination, that is, by the time when the body movement determination time elapses, the fall determination unit 18 determines the fall abnormality.

  Further, the fall determination unit 18 performs fall determination in consideration of the user's activity status (activity amount). The impact and drop characteristics obtained differ depending on the manner of falling, such as whether or not there is consciousness or whether or not it is moving violently. Therefore, the fall determination unit 18 calculates the activity amount of the user from the acceleration data, and weights the impact evaluation value and the fall evaluation value according to the activity amount of the user in a predetermined period with respect to the time of occurrence of the impact. In addition, a comprehensive evaluation value is obtained.

  That is, the fall determination unit 18 does not simply obtain the overall evaluation value as “Et = Es + Ef”, but weights each evaluation value (Es, Ef) according to the user's activity status (activity amount) at the time of the fall. Are added together to obtain a comprehensive evaluation value Et. In this case, the activity state (activity amount) of the user is analyzed by analyzing the movement in a certain period (activity determination period) before a fall (impact) is detected, and the acceleration data (for example, 3 seconds) ( (Peak and change amount). Note that the above-described fall determination period (for example, the period from time T3 to time T2) includes the movement during the fall, so this activity determination period (for example, the period from time T4 to time T3). The period after the fall determination period (for example, the period from time T3 to time T1) is not included, and the period before the fall determination period is set (see FIG. 3).

Specifically, the fall determination unit 18 obtains an amount of exercise W that is a total amount of change in acceleration during the activity determination period, and determines an activity status (motion intensity) by comparing the amount of exercise W with an activity reference value. . The amount of exercise W is obtained by, for example, integrating the differences of acceleration data (scalarized acceleration data a1, a2,..., An−1, an) in the activity determination period by using the following expression 4. Can do. This momentum W may be calculated for each of the three axes (X axis, Y axis, Z axis), or may be calculated as the sum or average of the three axes.
W = | a1-a2 | + | a2-a3 | +... || an-1-an | (Formula 4)

  The amount of exercise W can also be obtained based on the magnitude of the peak value and the number of detections. For example, the amount of exercise W may be the number of times a peak value above a certain level is detected, the sum of the peak values, or the number of times weighted by the maximum or average of the peak values.

In the present embodiment, the fall determination unit 18 increases the weight of the drop evaluation value higher than the impact evaluation value when the user's activity amount in the predetermined period before the occurrence of the impact is smaller than the reference activity amount. Obtain an evaluation value. That is, when the momentum W is very small and the state immediately before the fall is in a resting state (such as standing upright), the overall evaluation value Et is obtained by weighting the drop evaluation Ef more than the impact evaluation value Es. For example, when the exercise amount W is smaller than the reference activity amount Th1 (the total change amount is extremely small), a weighting process such as the following Expression 5 is performed. Note that the weighting coefficient may be changed according to the amount of exercise W so that Es is reduced (Ef is increased) as the amount of exercise W is smaller (away from Th1).
Et = Es × 1/2 + Ef × 3/2 (Formula 5)

  Next, the operation of the fall detection terminal 1 of the present embodiment will be described with reference to the flowchart of FIG. In the fall detection terminal 1 of the present embodiment, first, a peak is detected from the acceleration data to calculate the impact evaluation value Es (S1), and a free fall characteristic is detected from the acceleration data to calculate the drop evaluation value Ef. (S2). And after calculating | requiring a user's active mass, the impact evaluation value Es and the fall evaluation value Ef are weighted, the comprehensive evaluation value Et is calculated (S3), and the user fell based on the comprehensive evaluation value Et? It is determined whether or not (S4).

  If it is determined to fall, the fall flag is turned ON (S5). Thereafter, body motion detection (automatic cancellation determination) is performed. If body movement is detected (S6), the fall flag is turned off (S7). On the other hand, when no body movement is detected (S6), after a predetermined cancellation time has elapsed (S8), a fall abnormality is determined (S9).

  According to the fall detection terminal 1 of the present embodiment as described above, the fall possibility (impact evaluation value Es) based on the impact is obtained from the magnitude of the impact applied when the fall occurs, and it occurs in the process leading to the fall. Based on the fall characteristics of the free fall, the possibility of falling (drop evaluation value Ef) based on the drop characteristics is obtained. Whether or not the user has fallen is determined based on the comprehensive evaluation value Et that comprehensively evaluates the possibility of falling based on the impact (impact evaluation value Es) and the possibility of falling based on the drop characteristics (drop evaluation value Ef). Judgment (falling judgment) is performed. As a result, it is possible to perform the fall determination with higher accuracy than in the case where the determination is made based solely on the peak value of the acceleration or simply based on the drop characteristic.

  In short, in the present embodiment, whether or not the user has fallen is determined based on the detection result of the impact applied when the vehicle falls and the detection result of the fall characteristics of the free fall that occurs during the fall. (Falling judgment) is performed. In this case, the activity amount W of the user is calculated from the acceleration data, and the impact detection result and the fall characteristic detection result are weighted according to the activity amount W for a predetermined period with reference to the time of occurrence of the impact (at the time of falling). Is added. When the amount of activity W before the occurrence of the impact is small, that is, when the user is in a resting state, the weighting of the drop characteristic detection result is set higher than the impact detection result. When a user who has been resting falls due to loss of consciousness, etc., the fall evaluation value is more likely to be reflected in the fall evaluation value than the impact evaluation value. Can be determined.

  In the present embodiment, when calculating the comprehensive evaluation value Et, the impact evaluation value Es and the drop evaluation value Ef are weighted according to the amount of activity in a predetermined period with reference to the time of occurrence of the impact (at the time of falling). Added. Therefore, it is possible to perform the fall determination with high accuracy based on the comprehensive evaluation value Et in consideration of the activity amount W (activity state) of the user at the time of the fall.

  Specifically, when the activity amount W before the occurrence of the impact is small, that is, when the user is in a resting state before the occurrence of the impact, the weight of the drop evaluation value Ef is set higher than the impact evaluation value Es. As described above, when a user who has been resting falls due to loss of consciousness or the like, the fall evaluation value is more likely to be reflected in the fall evaluation value than the impact evaluation value. (Falling) can be determined with high accuracy.

  In the present embodiment, the drop characteristic can be appropriately detected from the acceleration data in the drop determination period (first predetermined period before the occurrence of the impact). Further, the amount of activity of the user can be appropriately calculated from the acceleration data in the activity determination period (second predetermined period before the occurrence of the impact).

  In the present embodiment, the impact detection unit 16 is configured to obtain the impact evaluation value Es based on the acceleration peak value P. However, instead of the peak value P, the total change amount of acceleration is used. It may be configured to detect the impact. In this case, the difference from the immediately preceding acceleration data is obtained for a predetermined period (for example, about 50 msec before and after) when the peak value is detected, and the absolute value of this difference is integrated over the predetermined period to calculate the acceleration change amount h. . Then, the impact evaluation value Es is obtained by multiplying the acceleration change amount h by the adjustment coefficient. Further, by using the acceleration change amount h and the acceleration peak value P in combination, the impact evaluation value Es can be obtained based on the sum or ratio of the two.

  In the present embodiment, the drop detection unit 18 is configured to obtain the drop evaluation value Ef based on the average acceleration value A during the fall determination period, but instead of the average acceleration value A, the fall detection period 18 A configuration may be adopted in which a drop is detected based on a continuous time or an accumulated time in which an acceleration lower than a minimum acceleration or a reference acceleration (the above constant a) is detected.

  The embodiments of the present invention have been described above by way of example, but the scope of the present invention is not limited to these embodiments, and can be changed or modified according to the purpose within the scope of the claims. is there.

  As described above, the fall detection terminal according to the present invention has the effect of being able to make a fall determination with high accuracy, and is useful for use in a monitoring system that detects the fall of an elderly person.

1 Fall detection terminal 2 Acceleration sensor (motion sensor)
DESCRIPTION OF SYMBOLS 3 Wear sensor 4 Altitude sensor 5 Emergency button 6 Operation display part 7 Vibration part 8 Power supply part 9 Wireless communication part 10 Monitoring control part 11 Monitoring center 12 Security terminal 13 Emergency monitoring part 14 Life monitoring part 15 Fall monitoring part 16 Impact detection part ( Impact detection means)
17 Fall detection unit (fall detection means)
18 Fall determination unit (fall determination means)

Claims (7)

In a fall detection terminal that is carried by a user and detects the fall of the user,
A motion sensor that detects movement of the fall detection terminal and outputs movement data;
An impact detection means for detecting an impact generated in the fall detection terminal from the motion data, and calculating an impact evaluation value indicating a fall possibility based on the impact;
A fall detection means for detecting a fall characteristic that appears when the fall detection terminal falls freely from the movement data, and calculating a fall evaluation value indicating a fall possibility based on the fall characteristic;
Based on a comprehensive evaluation value that comprehensively evaluates the impact evaluation value and the drop evaluation value, a fall determination unit that determines whether or not the user has fallen,
A fall detection terminal comprising:   The fall determination means calculates an activity amount of the user from the motion data, and the impact evaluation value and the drop evaluation are determined according to the activity amount of the user in a predetermined period with reference to the occurrence of the impact. The fall detection terminal according to claim 1, wherein the comprehensive evaluation value is obtained by adding a weight to the value.   When the user's activity amount in a predetermined period before the occurrence of the impact is in a resting state smaller than a reference activity amount, the fall determination means increases the weight of the fall evaluation value from the impact evaluation value and performs the comprehensive evaluation. The fall detection terminal according to claim 2, wherein a value is obtained. The drop detection means detects the drop characteristic from the motion data in a first predetermined period before the occurrence of the impact,
The fall determination means calculates the activity amount of the user from the motion data in a second predetermined period that is longer than the first predetermined period before the occurrence of the impact. The fall detection terminal described.   The fall detection terminal according to claim 4, wherein the second predetermined period is set so as not to include the first predetermined period. In a fall detection terminal that is carried by a user and detects the fall of the user,
A motion sensor that detects the movement of the user and outputs movement data indicating the movement of the user;
An impact detection means for detecting an impact generated in the fall detection terminal from the motion data;
Fall detection means for detecting a fall characteristic that appears when the fall detection terminal falls freely from the movement data;
A fall determination means for judging whether or not the user has fallen based on the detection result of the impact and the detection result of the drop characteristic;
With
The fall determination means calculates the amount of activity of the user from the movement data, and when the amount of activity of the user in a predetermined period based on the occurrence of the impact is in a resting state smaller than a reference amount of activity, A fall detection terminal characterized in that weighting of the fall characteristic detection result is made higher than an impact detection result to determine whether or not the user has fallen. A program executed on a fall detection terminal that detects a user's fall,
The fall detection terminal is provided with a motion sensor that detects movement of the fall detection terminal and outputs movement data,
The program detects a shock generated in the fall detection terminal from the motion data to a computer, and calculates an impact evaluation value indicating a fall possibility based on the shock;
A process of detecting a fall characteristic that appears when the fall detection terminal falls freely from the motion data, and calculating a fall evaluation value indicating a fall possibility based on the fall characteristic;
Based on a comprehensive evaluation value that comprehensively evaluates the impact evaluation value and the drop evaluation value, a process of determining whether or not the user has fallen;
A program characterized by having executed.

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