KR100870961B1 - Portable monitoring system of body activity - Google Patents

Abstract

The present invention relates to a portable physical activity monitoring system that monitors physical activity according to human motion in real time using a three-axis acceleration sensor to classify and analyze into various activities.

The present invention is a portable monitoring device for detecting and wirelessly transmitting the X-axis motion signal, Y-axis motion signal, Z-axis motion signal from the 3-axis acceleration sensor, the X-axis motion signal, Y-axis motion signal, Z received from the portable monitoring device An activity motion signal analysis method of a portable physical activity monitoring system having an analysis system for analyzing motion from an axis motion signal, the method comprising: detecting an X-axis motion signal, a Y-axis motion signal, and a Z-axis motion signal received from the portable monitoring device with 0; By separating the positive (+,-) data based on the X-axis positive operation signal, the x-axis negative operation signal, the x-axis negative operation signal, the y-axis positive operation signal, the y-axis negative operation signal, a government data separation step of obtaining a z-axis positive operation signal and a z-axis secondary operation signal; The absolute value of the absolute value of the X-axis operation signal, the absolute value of the Y-axis operation signal, and the absolute value of the Z-axis operation signal is applied by taking the absolute values of the X-axis operation signal, the Y-axis operation signal, and the Z-axis operation signals received from the portable monitoring device. step; A moving average of the X-axis positive operation signal, the X-axis negative operation signal, the Y-axis positive operation signal, the Y-axis negative operation signal, the Z-axis positive operation signal, and the Z-axis negative operation signal output in the government data separation step is obtained. In addition, a moving average calculation step of obtaining a moving average of the absolute value of the X-axis operation signal, the absolute value of the Y-axis operation signal, the absolute value of the Z-axis operation signal received in the absolute value applying step; A digital low pass filtering step of passing the moving averages of the respective operation signals obtained in the moving average calculation step through a digital low pass filter; An operation classification step of storing each operation (posture) separately from the output signals of the digital low pass filtering step; And a daily exercise state analyzing step of analyzing the daily exercise state from the data stored for one day, divided into nine pieces of motion data in the operation classification step.

Acceleration sensor, motion state, motion, posture, monitoring

Description

Portable monitoring system of body activity

1 is an explanatory diagram for schematically illustrating a portable physical activity monitoring system according to an embodiment of the present invention.

FIG. 2 is an explanatory diagram for explaining a portable monitoring device in FIG. 1.

Figure 3 is a schematic diagram of a portable physical activity monitoring system according to an embodiment of the present invention.

FIG. 4 is a flowchart of an activity operation signal analysis in the signal analyzer of FIG. 3.

FIG. 5 is a flowchart for distinguishing nine operations in the operation classification step of FIG. 4.

6 is a flowchart of a daily exercise state analysis step of FIG. 4.

FIG. 7 is an example of a moving average operation signal obtained from an output signal of the government data separation step of FIG. 4.

FIG. 8 is an example of a moving average operation signal obtained from signals passing through an absolute value applying step of FIG. 4.

<Explanation of symbols for main parts of the drawings>

50: portable monitoring device 100: 3-axis acceleration sensor

200: preamplifier 300: filter unit

400: A / D converter 500: microprocessor

600: transmitter 650: analysis system

700: receiver 800: signal analyzer

850: doctor's computer

The present invention relates to a portable physical activity monitoring system that monitors physical activity according to human motion in real time using a three-axis acceleration sensor to classify and analyze into various activities.

Physical activity monitoring system is a device for monitoring the activity of the body according to the movement of a person.

In particular, as industrial development and an aging society have recently come, the number of the elderly and the disabled living alone is increasing, and it is difficult for the caregiver, the caregiver, or the attending physician to take care of the elderly, the disabled, or the patients.

Therefore, there is a need for a physical activity monitoring device capable of prompt first aid in an emergency by monitoring a 24 hour operation of an elderly person or a disabled person living alone and transmitting it to a guardian, a caregiver, or a attending physician without the caregiver, the caregiver or the attending physician.

In addition, a physical activity monitoring system that can be attached to the general public exercising in a fitness center and can be supervised by a central computer without being directly observed by an exercise prescriber is required.

Conventionally, an expensive three-dimensional motion analyzer has been developed to analyze the activity of the body according to motion, but this is subject to space-time constraints such as experimental constraints in a laboratory environment, and does not specifically classify physical activity according to motion. .

Accordingly, there is a need for a portable physical activity monitoring system that is low cost, easy to carry, convenient to use, and specifically capable of distinguishing physical activity according to movement.

Therefore, the present invention provides a portable physical activity monitoring system that is low cost, easy to carry, convenient to use, and specifically classifies physical activity according to the operation.

The portable physical activity monitoring system of the present invention uses a small three-axis acceleration sensor, and is clipped to fit on the belt or the waist of the pants, so that it is easy to wear, and the signal can be measured in the unrestrained state. In addition, wireless signals can be transmitted and checked on a PC, so there is no need for direct human monitoring or management.

The technical problem to be achieved by the present invention is to provide a portable physical activity monitoring system that can be divided into a variety of activities by real-time monitoring the physical activity according to the human motion using a three-axis acceleration sensor.

Another technical problem to be achieved by the present invention is to provide a portable physical activity monitoring system that can be low-cost, easy to carry, easy to use, specifically to distinguish the physical activity according to the operation.

Another technical problem to be achieved by the present invention is to use a small three-axis acceleration sensor, the portion of the signal detection is made of a clip type, it is possible to monitor the unsupervised state by transmitting a signal wirelessly to check on a computer The present invention provides a portable physical activity monitoring system.

Hereinafter, the configuration and operation of a portable physical activity monitoring system according to an embodiment of the present invention will be described in detail with reference to the accompanying drawings.

1 is an explanatory diagram for schematically explaining a portable physical activity monitoring system according to an embodiment of the present invention, Figure 2 is an explanatory diagram for explaining a portable monitoring device in FIG.

The portable physical activity monitoring system of the present invention comprises a portable monitoring device 50 and an analysis system 650.

The portable monitoring device 50 detects the motion of the body and transmits it to the analysis system 650. The portable monitoring device 50 has a clip type, and is fitted to a belt or a waist pants, and includes an acceleration sensor, a preamplifier, a filter unit, an A / D converter, a microprocessor, and a wireless transmitter. On the upper side, the antenna connected from the wireless transmitter is made to protrude out.

The analysis system 650 analyzes the operation signal received from the portable monitoring device 50. The analysis system 650 includes a receiver and a signal analyzer. The analysis system 650 transmits the operation data and the analysis data to the guardian and the attending physician's computer 850 through the Internet.

3 is a schematic configuration diagram of a portable physical activity monitoring system according to an embodiment of the present invention, a three-axis acceleration sensor unit 100, pre-amplifier 200, filter unit 300, A / D converter 400 , A microprocessor 500, a transmitter 600, a receiver 700, and a signal analyzer 800.

The 3-axis acceleration sensor unit 100 is mounted on the subject to convert the acceleration of the X, Y, Z axis into an electrical signal and outputs it. The three-axis acceleration sensor unit 100 separately measures three-dimensional operation signals of the X, Y, and Z axes. That is, the three-axis acceleration sensor unit 100 detects the X-axis operation signal, Y-axis operation signal, Z-axis operation signal.

The preamplifier 200 amplifies the output of the three-axis acceleration sensor unit 100 and transmits the amplified output to the filter unit 300.

The filter unit 300 primarily performs analog filtering to remove unnecessary noise components such as 60 Hz noise, high frequency noise, and the like by using a low pass filter of 20 Hz. The preamplifier 200 and the filter unit 300 constitute a signal preprocessor.

The A / D converter 400 converts the analog filtered X, Y, and Z-axis operation signals output from the filter unit 300 into digital signals, respectively.

The microprocessor 500 transmits acceleration data received from the A / D converter 400, that is, operation signals of the X, Y, and Z axes to the transmitter 600.

The transmitter 600 wirelessly transmits an output signal of the microprocessor 500. Wireless transmission using Bluetooth, and in some cases RF wireless transmission.

The receiver 700 wirelessly receives signals transmitted from the transmitter 600, that is, operation signals on the X, Y, and Z axes, and transmits the signals to the signal analyzer 800. In the present invention, the wireless transmission and reception may use a Bluetooth module or an RF module. The Bluetooth module can use PROMI-ESD (Chipsen, South Korea), and the RF module can use Radio Matrix's TX1-173.250-10 and RX1-173.250-10.

The signal analyzer 800 analyzes an operation state from the operation signal received from the receiver 700. The data obtained by analyzing the signal analysis unit 800 is transmitted to the guardian and the attending physician's computer 850 through the Internet network.

4 is a flowchart of an activity operation signal analysis according to an embodiment of the present invention.

In the operation signal receiving step (S110), the signal detected by the portable monitoring device 50 and transmitted to the analysis system 650, that is, detected by the acceleration sensor unit and the signal analyzer 800 through the A / D converter and the microprocessor. In operation S110, the X-axis operation signal, the Y-axis operation signal, and the Z-axis operation signal are received.

In the step of separating the positive (+,-) data (S120), the X, Y, and Z-axis operation signals received in the operation signal receiving step are separated by 0 from the government data, and the X-axis positive (+) The operation signal, the X-axis negative operation signal, the Y-axis positive operation signal, the Y-axis negative operation signal, the Z-axis positive operation signal, and the Z-axis negative operation signal are obtained (S120).

Here, the X-axis positive operation signal is referred to as an X-axis positive operation signal by detecting only a signal having a positive sign from the X-axis operation signal received in the operation signal receiving step. This is represented by the equation (1).

The X-axis sub operation signal is referred to as an X-axis sub operation signal by detecting only a signal having a negative sign from the X-axis operation signal received in the operation signal receiving step. This is expressed as an equation (2).

Here, the Y-axis positive operation signal is referred to as a Y-axis positive operation signal by detecting only a signal having a positive sign from the Y-axis operation signal received in the operation signal receiving step. This is represented by the equation (3).

The Y-axis sub operation signal is referred to as a Y-axis sub operation signal by detecting only a signal having a negative sign from the Y axis operation signal received in the operation signal receiving step. This is expressed as an equation (4).

Here, the Z-axis positive operation signal is referred to as a Z-axis positive operation signal by detecting only a signal having a positive sign from the Z-axis operation signal received in the operation signal receiving step. This is represented by the equation (5).

The Z-axis sub operation signal is referred to as a Z-axis sub operation signal by detecting only a signal having a negative sign from the Z axis operation signal received in the operation signal receiving step. This is expressed as an equation (6).

In the absolute value applying step (S130), the absolute value of the X-axis operation signal, the absolute value of the Y-axis operation signal, the absolute value of the Z-axis operation signal received in the operation signal receiving step (S110) is obtained (S130).

In the moving average calculation step (S140), the X-axis positive operation signal, the X-axis negative operation signal, the Y-axis positive operation signal, the Y-axis negative operation signal, the Z-axis positive operation signal output from the government data separation step (S120), The moving average of the Z-axis sub-operation signals is calculated, and the moving average of the absolute value of the X-axis operation signal, the absolute value of the Y-axis operation signal, and the absolute value of the Z-axis operation signal received in the absolute value applying step (S130) (S130). ). That is, a total of six moving average operation signals obtained from the output signal of the government data separation step S120 and a total of three moving average operation signals obtained from the signals passing through the absolute value applying step S130 are three. Here, the interval for obtaining the moving average, that is, the number of samples for obtaining the moving average may be 60 points.

In the digital low pass filtering step S150, the moving averages of the respective operation signals obtained in the moving average calculation step S140 are passed through the digital low pass filter S150. Here, the digital low pass filter may use a 1 Hz low pass filter.

In general, a person's movement is about 2 steps per second, and elderly people living alone may be slower because of their careful walking characteristics. In order to remove the components and smooth the signal, a lowpass filter was applied here.

In the operation classification step S160, each operation (posture) is separately stored from the output signals of the digital low pass filtering step S150 (S160). That is, in the motion analysis step (S160), the nine motions of lying down, turning over, lying down, sitting, running, going down the stairs, going up the stairs, standing, and walking are distinguished.

In the daily exercise status analysis step (S170), the motion classification step (S160) divided into nine motion data from the data stored for one day, each posture activity (%) of the day, posture activity (%) by time zone Analyze

FIG. 5 is a flowchart for distinguishing nine operations in the operation classification (posture) stage of FIG. 4.

In the lying down step, the moving average M _{lower , X} of the X-axis negative operation signal output from the digital low pass filtering step S150 is smaller than -15 and the moving average of the absolute value of the X-axis operation signal M _X is If greater than 10 (S210), the current operation is determined to be "down" (S220). Here _, -15 used as the reference value of the moving average of the X-axis sub-operation signal (M _{lower , X} ) and 10 used as the reference value of the moving average (M _X ) of the absolute value of the X-axis sub-signal are obtained in the repeated experiments. It is a shame.

In other words, when M _{lower , X} <-15 and M _X > 10, it is determined to be "down". Where M _{lower, X} is the moving average of the X-axis sub-operation signal output from the digital low pass filtering step (S150), M _X is the moving average of the absolute value of the X-axis operation signal.

The inverting division step receives the output signal of the digital low pass filtering step S150, and the moving average M _{upper , x} of the X-axis positive operation signal is greater than 10, and the moving average of the absolute value of the Z-axis operation signal ( If M _z ) is greater than 15 (S230), the current operation is determined as "turn over" (S240). Here _, 10 used as a turning reference value of the moving average (M _{upper , x} ) of the _X- axis motion signal and 15 used as a turning reference of the moving average (M _z ) of the absolute value of the Z-axis motion signal are repeated in the repeated experiments. It is the numerical value obtained.

In other words, when M _{upper , x} > 10 and M _z > 15, it is determined as "turn over". Where M _{upper and x} are moving averages of the X-axis positive operation signals output from the digital low pass filtering step S150, and M _z is a moving average of the absolute values of the Z-axis operation signals.

In the step of sorting, the output signal of the digital low pass filtering step S150 is received, and the moving average M _{upper , x} of the X-axis positive operation signal is greater than 20 and the moving average of the absolute value of the X-axis operation signal M. _x ) is greater than 20 (S250), the current operation is determined to be "overturning" (S260). Here _, 20 used as the reference value for the moving average of the X-axis motion signal (M _{upper , x} ) and 20 used as the value for the straight-up value of the moving average (M _x ) of the absolute value of the X-axis motion signal are repeated in the repeated experiments. It is the numerical value obtained.

In other words, when M _{upper , x} > 20 and M _x > 20, it is determined as "turn on". Where M _{upper and x} are the moving averages of the X-axis positive operation signals output from the digital low pass filtering step S150, and M _x is the moving averages of the absolute values of the X-axis operation signals.

Sitting nine minutes stage, receives the output signal of the digital low-pass filtering step (S150), the moving average of the X-axis direct-acting signal (M _{upper, x)} a moving average (M _upper large and Y axis direct-acting signal than the _{second, If y} ) is less than 1 (S270), the current operation is determined to be "sit down" (S280). Here _, 2 used as the sitting reference value of the moving average (M _{upper , x} ) of the X-axis positive operating signal and 1 used as the sitting reference value of the moving average (M _{upper , y} ) of the Y-axis positive operating signal are obtained from the repeated experiments. It is a shame.

In other words, when M _{upper , x} > 2 and M _{upper , Y} <1, it is determined as "sitting." Here, M _{upper and x} are moving averages of the X-axis positive operation signals output from the digital low pass filtering step S150, and M _{upper and Y} are moving averages of the Y-axis positive operation signals.

The run segmentation step receives the output signal of the digital low pass filtering step (S150), so that the moving average (M _{lower , Y} ) of the Y-axis sub-operation signal is less than -5 and the moving average (M) of the absolute value of the Y-axis operation signal. _{If Y} ) is greater than 15 (S290), the current operation is determined to be "run" (S300). Here _, -5 used as the running reference value of the moving average (M _{lower , Y} ) of the Y-axis sub-movement signal and 15 used as the running reference value of the moving average (M _Y ) of the absolute value of the Y-axis operating signal are obtained from the repeated experiments. It is a shame.

In other words, when M _{lower , Y} <-5 and M _Y > 15, it is determined to "run". Here, M _{lower and Y} are moving averages of the Y-axis sub-operation signal output from the digital low pass filtering step S150, and M _Y is a moving average of the absolute values of the Y-axis operation signal.

The step descending step receives the output signal of the digital low pass filtering step (S150), and the moving average (M _{upper , Y} ) of the Y-axis positive operation signal is greater than 3 and the moving average of the absolute value of the Y-axis operation signal ( If M _Y ) is greater than 4 (S310), the current operation is determined as "down the stairs" (S320). Here _, 3 used as a reference value for moving down the stairs of the Y-axis motion signal (M _{upper , Y} ) and 4 used as a reference value for moving down the stairs (M _Y ) of the absolute value of the Y-axis motion signal are repeated. This is the numerical value obtained from the experiment.

In other words, when M _{upper , Y} > 3 and M _Y > 4, it is determined to "go down the stairs". Here, M _{upper and Y} are moving averages of the Y-axis positive operation signals output from the digital low pass filtering step S150, and M _Y is a moving average of the absolute values of the Y-axis operating signals.

The step climbing step receives the output signal of the digital low pass filtering step (S150), and the moving average (M _{upper , X} ) of the X-axis positive operation signal is smaller than 1 _and the moving average (M) of the X-axis sub operation signal. _{lower , X} ) is less than 1.5 (S330), the current operation is determined to "step up" (S340). Here _, 1 is used as a step-up reference value of the moving average (M _{upper , X} ) of the X-axis positive motion signal and -1.5 is used as a step-up reference value of the moving average (M _{lower , X} ) of the X-axis negative motion signal. Is a value obtained in a repeated experiment.

In other words, when M _{upper , X} <1 and M _{lower , X} <-1.5, it is determined to "step up". Here, M _{upper and X} are moving averages of the X-axis positive operating signals output from the digital low pass filtering step S150, and M _{lower and X} are moving averages of the X-axis negative operating signals.

AD nine minutes stage, receives the output signal of the digital low-pass filtering step (S150), Y-axis positive moving average of the operating signal (M _{upper, Y)} is smaller than 1 Y absolute movement of the axial motion signal average (M _Y Is smaller than 1 (S350), the current operation is determined as "standing" (S360). Here _, 1 used as the reference value of the moving average (M _{upper , Y} ) of the Y-axis positive motion signal and 1 used as the reference value of the moving average (M _Y ) of the absolute value of the Y-axis motion signal is a numerical value obtained in a repeated experiment. to be.

In other words, when M _{upper , Y} <1 and M _Y <1, it is determined as "standing". Here, M _{upper and Y} are moving averages of the Y-axis positive operation signals output from the digital low pass filtering step S150, and M _Y is a moving average of the absolute values of the Y-axis operation signals.

If not all of them, it is determined as "walk" (S380).

In the signal analyzer 800, the static postures such as sitting, standing, lying down, turning back, lying down, and dynamic movements such as running, walking, climbing stairs, descending stairs, and the like are displayed on the monitor screen.

6 is a flowchart of a daily exercise state analysis step of FIG. 4. That is, in FIG. 6, signals are measured for one day, and these signals are stored in the memory of the signal analyzer 800 for one day in the attitude data divided into nine operations through the operation classification step S160. From this, the activity for each posture (%) and the posture for each time period (%) are calculated.

In the data reading step S410, the posture data for one day is read from the memory of the signal analyzing unit 800 (S410).

In the selection step of the analysis type (S420), it is selected whether to perform a full analysis, daily analysis by posture, or posture analysis by time zone (S420).

When the entire analysis is selected in the selection step of the analysis type (S420), one-day data, that is, 24-hour data is output in chronological order (S430).

In the step S420 of the analysis type selection step (S420), if you select the analysis by posture, the whole time of movement in the day data, that is, lying down, turning, lying down, sitting, running, going down the stairs, going up the stairs, standing , Dividing the total time for each of the walks by 24, calculates the percentage of each movement for one day (S440). This is shown in Equation 7.

Rate of movement per day = total time of movement / 24

Here, the total time of movement means the total time of the movements totaled in one day.

As a result of output analysis for each posture per day, the ratio of each movement (posture) of the day is output (S450). From the ratio of each movement of the day, the ratio of each movement movement during the day activity can be known, and from this, the degree of activity of the day can be known.

When the posture analysis by time zone is selected in the selection step of the analysis type (S420), the total minutes of the movements in each time zone are divided by 60 to calculate the ratio of each movement during each time (S460). This is shown in Equation 8.

Hourly Movement Rate = Total Movements / 60

Here, the total motion refers to the total motion of one motion in an hour.

As a step of outputting a posture analysis result for each time zone, a ratio for each movement is output during each time (S470). From the ratio of each movement per hour, the ratio of each movement movement in each time zone of the day can be known. From this, you can find out the time zone during which you exercise a lot.

In the present invention, the result of classifying the operation (posture) can be viewed in real time on the monitor screen of the signal analyzer, and the current operation status is fed back to the user. The daily exercise status analysis can be used when a doctor or exercise prescriber wants to see past data and compare it with the present based on the stored posture classification data. In the present invention, the data stored in the memory of the signal analyzer is stored in each classified posture result every 1 second after data acquisition, and the data is read and analyzed.

FIG. 7 is an example of a moving average operation signal obtained from an output signal of the government data separation step of FIG. 4.

FIG. 7 illustrates an example of analyzing data at one second intervals. When data is acquired at a sampling frequency of 60 Hz, there are 60 sample data every second.

(A), (b) and (c) of FIG. 7 are raw data of the X-axis motion signal, the Y-axis motion signal, and the Z-axis motion signal of the acceleration signal. This is the case when you want to proceed in the order of -stand-step-step-stand-run-walk-stand-sit-stand-walk-stand-stand-over-turn-down-down-stander. The acceleration 3-axis data received in the operation signal receiving step S110, that is, the X-axis operation signal, the Y-axis operation signal, and the Z-axis operation signal correspond to FIGS. 7A, 7B, and 7C.

7 (a), 7 (b) and 7 (c) pass through the government data separation step S120 and the moving average calculation step S140, the results are shown in FIGS. (D) and (e) of FIG. 7 are results of the government data separation step S120 and the moving average calculation step S140 of FIG. 7a, and FIGS. The result of going through the government data separation step S120 and the moving average calculation step S140 of FIG. 7B, and FIGS. 7H and i show the government data separation process of FIG. 7C. The result of the step S120 and the moving average operation step S140 is obtained.

FIG. 8 is an example of a moving average operation signal obtained from signals passing through an absolute value applying step of FIG. 4. 8 illustrates an example in which the absolute value applying step (S130) is passed without passing through the government data separation step (S120).

(A), (b) and (c) of FIG. 8 are raw data of the X-axis motion signal, the Y-axis motion signal, and the Z-axis motion signal of the acceleration signal, in which the examinee climbs the stair-step by 30 seconds. This is the case when you want to proceed in the order of -stand-step-step-stand-run-walk-stand-sit-stand-walk-stand-stand-over-turn-down-down-stander. The acceleration 3-axis data received in the operation signal receiving step (S110), that is, the X-axis operation signal, the Y-axis operation signal, and the Z-axis operation signal correspond to FIGS. 8A, 8B, and 8C.

8A, 8B, and 8C show the results of going through the absolute value applying step S130 and the moving average calculating step S140 of FIGS. FIG. 8 (d) shows the result of applying the absolute value (S130) and the moving average calculation step (S140) to FIG. 8 (a), and FIG. 8 (e) shows the absolute value of FIG. 8 (b). Result of applying the step (S130) and the moving average calculation step (S140), Figure 8 (f) is the result of the absolute value applying step (S130) and moving average calculation step (S140) for Figure 8 (c). to be.

Numerical values used as reference values in the present invention are only one example and thus it is not intended to limit the present invention.

The present invention is not limited to the above described and illustrated in the drawings, and of course, more modifications and variations are possible to those skilled in the art within the scope of the following claims.

As described above, the portable physical activity monitoring apparatus of the present invention can be divided into various activities by real-time monitoring of physical activity according to a human motion using a three-axis acceleration sensor, and is inexpensive, portable, and easy to use. It is convenient to do, and in particular, it is possible to distinguish physical activity according to the movement. In addition, the present invention uses a small three-axis acceleration sensor, and is clipped to the belt or the waist of the pants, it is possible to monitor the non-supervised state by transmitting a signal wirelessly to check the PC.

The present invention is divided into static postures such as sitting, standing, lying down, turning, lying down and dynamic movements such as running, walking, climbing the stairs, descending the stairs, etc. are displayed on the monitor screen, as seen by the elderly living alone or disabled or patients. After the device is attached, the current status is transmitted to the guardian or the attending physician of the hospital by using the internet network, so that emergency response can be taken quickly in an emergency.

In addition, the present invention can be attached to the general public exercising in a fitness center, such as exercise supervision can be managed and supervised by a central computer directly.

In other words, by monitoring data of the elderly, disabled people, and patients living alone, real-time data is sent to the central computer to monitor a large number of subjects simultaneously. In addition, it is possible to check the activity state by observing a large number of athletes on the monitor screen of the central computer without directly observing the person exercising in a fitness center or the like.

Claims (17)

delete It is a clip-type, equipped with a three-axis acceleration sensor to detect the X-axis motion signal, Y-axis motion signal, Z-axis motion signal and a wireless monitoring device, the analysis to analyze the motion from the signal received from the portable monitoring device System, The analysis system A receiver configured to wirelessly receive an X-axis operation signal, a Y-axis operation signal, and a Z-axis operation signal from the portable monitoring device; The X-axis operation signal, the Y-axis operation signal, and the Z-axis operation signal from the receiving unit are separated by a positive (+,-) signal from 0 with respect to the x-axis positive (+) operation signal and the x-axis unit (否,-) operation signal, y-axis positive operation signal, y-axis negative operation signal, z-axis positive operation signal, z-axis negative operation signal, x-axis positive operation signal, x-axis negative operation signal, y-axis positive operation signal, The moving average of the y-axis sub-operation signal, the z-axis positive operation signal, and the z-axis sub operation signal is obtained, and the moving average of the absolute values of the X-axis operation signal, the Y-axis operation signal, and the Z-axis operation signal from the receiver is obtained. A signal analysis unit for determining (posture); Portable physical activity monitoring system comprising a. The method of claim 2, The analysis system is a portable physical activity monitoring system, characterized in that the data obtained by analyzing the transmission to the guardian or doctor's computer via the Internet network. Portable monitoring device for detecting and wirelessly transmitting the X-axis motion signal, Y-axis motion signal, and Z-axis motion signal from the 3-axis acceleration sensor, the X-axis motion signal received from the portable monitoring device, the Y-axis motion signal, the Z-axis motion signal An activity motion signal analysis method of a portable physical activity monitoring system having an analysis system for analyzing motion from The X-axis positive signal is separated from the X-axis motion signal, the Y-axis motion signal, and the Z-axis motion signal received from the portable monitoring device with a positive (+,-) data, and the x-axis positive (+) motion signal, a government data separation step of obtaining an x-axis negative operation signal, a y-axis positive operation signal, a y-axis negative operation signal, a z-axis positive operation signal, and a z-axis negative operation signal; The absolute value of the absolute value of the X-axis operation signal, the absolute value of the Y-axis operation signal, and the absolute value of the Z-axis operation signal is applied by taking the absolute values of the X-axis operation signal, the Y-axis operation signal, and the Z-axis operation signals received from the portable monitoring device. step; A moving average of the X-axis positive operation signal, the X-axis negative operation signal, the Y-axis positive operation signal, the Y-axis negative operation signal, the Z-axis positive operation signal, and the Z-axis negative operation signal output in the government data separation step is obtained. In addition, a moving average calculation step of obtaining a moving average of the absolute value of the X-axis operation signal, the absolute value of the Y-axis operation signal, the absolute value of the Z-axis operation signal received in the absolute value applying step; A digital low pass filtering step of passing the moving averages of the respective operation signals obtained in the moving average calculation step through a digital low pass filter; An operation classification step of storing each operation (posture) separately from the output signals of the digital low pass filtering step; A daily exercise state analysis step of analyzing the daily exercise state from the data stored for one day divided into nine operation data in the operation classification step; Activity signal analysis method of the portable physical activity monitoring system comprising a. The method of claim 4, wherein The operation of distinguishing in the operation classification step is A method for analyzing the motion signal of a portable physical activity monitoring system, characterized by nine movements of lying down, turning over, lying down, sitting, running, descending a staircase, climbing a staircase, standing, and walking. The method of claim 4 And the digital low pass filter is a 1 Hz low pass filter. The method of claim 4, wherein  1 day exercise status analysis phase An activity motion signal analysis method of a portable physical activity monitoring system, characterized in that the analysis of the rate of movement per day, the rate of movement per hour. The method of claim 7, wherein The rate of motion by day Rate of movement per day = total time of movement / 24 (Here, the total time of movement refers to the total time of the movement in one day.) An activity motion signal analysis method of a portable physical activity monitoring system characterized in that obtained by. The method of claim 7, wherein The rate per move per hour Hourly Movement Rate = Total Movements / 60 (In this case, the whole movement refers to the whole movement of one movement in an hour.) An activity operation signal analysis method of a portable physical activity monitoring system characterized in that obtained by. The method of claim 4, wherein In the operation classification step If the moving average M _{lower , X} of the X-axis sub-operation signal output from the digital low pass filtering step is less than -15, and the moving average M _X of the absolute value of the X-axis operation signal is greater than 10, the And the motion is determined to be "down". The method of claim 4, wherein In the operation classification step Receiving the output signal of the digital low pass filtering step, if the moving average (M _{upper , x} ) of the X-axis positive operation signal is greater than 10, the moving average (M _z ) of the absolute value of the Z-axis operation signal is greater than 15 And the operation is determined to be "turning over". The method of claim 4, wherein In the operation classification step Receiving the output signal of the digital low pass filtering step, if the moving average (M _{upper , x} ) of the X-axis positive operation signal is greater than 20 and the moving average (M _x ) of the absolute value of the X-axis motion signal is greater than 20, And the motion is determined to be "turning over". The method of claim 4, wherein In the operation classification step Receiving the output signal of the digital low pass filtering step, if the moving average (M _{upper , x} ) of the X-axis positive operation signal is greater than 2 and the moving average (M _{upper , y} ) of the Y-axis positive operation signal is less than 1 (S270), the activity operation signal analysis method of the portable physical activity monitoring system, characterized in that the operation is determined to "sit down." The method of claim 4, wherein In the operation classification step Receiving the output signal of the digital low pass filtering step, if the moving average (M _{lower , Y} ) of the Y-axis sub-operation signal is less than -5 and the moving average (M _Y ) of the absolute value of the Y-axis operation signal is greater than 15 And the motion is determined to be " run &quot;. The method of claim 4, wherein In the operation classification step Receiving the output signal of the digital low pass filtering step, if the moving average (M _{upper , Y} ) of the Y-axis operation signal is greater than 3 and the moving average (M _Y ) of the absolute value of the Y-axis operation signal is greater than 4, And an action is determined as "going down the stairs". The method of claim 4, wherein In the operation classification step Receiving the output signal of the digital low pass filtering step, if the moving average (M _{upper , X} ) of the X-axis positive operation signal is less than 1 and the moving average (M _{lower , X} ) of the X-axis negative operation signal is less than 1.5 And the operation is determined to be "climbing up". The method of claim 4, wherein In the operation classification step Receiving the output signal of the digital low pass filtering step, if the moving average (M _{upper , Y} ) of the Y-axis positive operation signal is less than 1 and the moving average (M _Y ) of the absolute value of the Y-axis operation signal is less than 1, And an action is determined as "standing".

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