KR20100023388A - Apparatus and methods for motion pattern measurement using complex sensors - Google Patents

Abstract

PURPOSE: A method for motion pattern measurement using complex sensors and an apparatus thereof are provided to use cheap pedometer sensors and signals of motion sensors, thereby improving accuracy of the motion pattern measurement. CONSTITUTION: A walking peak candidate detector(132) detects a walking peak. A primary walking number detector(131) detects the first walking reference point. A walking waveform analyzer(133) inputs the walking reference point to analyze a walking waveform. A walking pattern detector(134) decides the walking pattern with the inputted signal. A final walking number detector decides a final walking number.

Description

Apparatus and method for measuring living pattern using composite sensor {APPARATUS AND METHODS FOR MOTION PATTERN MEASUREMENT USING COMPLEX SENSORS}

The present invention relates to a device for measuring a living pattern, and more particularly, a life pattern measuring device using signals of a low-cost pedometer sensor and a motion sensor in order to increase the measurement accuracy of the number of walks or types of living patterns for measuring the amount of activity of a user. And to a method. The applicant has filed a patent application 2008-0011270, "Lifestyle correction method and apparatus."

As the life expectancy increases, societal concern for health is inevitable. Therefore, in order to manage an individual's health-related lifestyles on their own, a simple inclination sensor or one end of an elastic wire is fixed, and a heavy weight is attached to the other end, and there is a contact point on the upper and lower sides of the weight so that the additional shaking is caused by the wearer's movement. Pedometers equipped with pedometer sensors, such as the pendulum using gravity and the law of inertia, which increase numbers one by one by touching additional switches, have been favored by people as inexpensive motion management measuring devices. In addition, as an essential measurement factor of remote health management, researches on exercise management measuring devices using expensive motion sensors that measure people's exercise patterns or calories burned are being actively conducted, and are trying to replace existing pedometers.

Korean Utility Model Registration No. 0249091 proposes a counter. A pedometer actuator, such as a tilt sensor, is installed and can be used as a pedometer, as well as a guide that is bent at an angle and installed at a certain angle so that the count can be counted only when the counting operation is completed. An improved counter is showing a type of conventional pedometer sensor.

However, a pedometer adopting a pedometer sensor such as an inclination sensor or the above-mentioned pendulum detects the number of steps only by the signal output from the pedometer sensor, so that the person who wears the pedometer may not detect the number of steps when walking very carefully. Also, if you jump rope or run fast, you can count the number of steps more than the actual number of steps, and if the user moves the pedometer up and down quickly by hand, the number of steps can be counted without actually moving.

It also recognizes key life patterns that may occur as people move in everyday life, such as slow walking, fast walking, running and climbing stairs, which can be detected using a combination of precision motion sensors or senders, such as accelerometers, velometers and geomagnetic sensors. There is a problem that cannot be done.

Japanese Laid Open Patent Application 2002-200059 proposes a body motion detecting device, a medical device and a body motion detecting method using the same. Provides a stable and discernible body movement detection device that can determine the type of movement such as flat walking, stair climbing, and stair lowering, and correlates with the amount of metabolism or calories burned based on the type of exercise such as flat walking, stair climbing, stair lowering The present invention specifically provides a device for detecting a body motion that can easily measure a high index. In other words, acceleration sensing means fixed to the human body and capable of independently detecting two or three mutually orthogonal accelerations, converting means for converting the output of the acceleration sensing means into the front and rear acceleration signals and the vertical acceleration signals of the human body; Calculated continuously the forward and backward acceleration signal and the up and down acceleration signal output from the conversion means, the calculation means for continuously outputting the calculation result and the signal strength for each predetermined period of the output of the calculation means Disclosed is a physical motion detection device, characterized in that it comprises a period signal intensity calculating means and an exercise shape discriminating means for discriminating the motion form of the human body based on the signal strength output from the period signal intensity calculating means.

In order to more accurately detect living patterns such as slow walking, fast walking, running, and climbing stairs, many precise devices or patents using accelerometers have been published. In addition, it is easy to find the peak in the conventional method of recognizing the peak of the input accelerometer signal and finding the pattern to calculate the number of walking, but it is difficult to develop the algorithm because there are too many cases to find the analog signal pattern. There is also a problem that the precision is also reduced. For this reason, the lifestyle measuring device using the accelerometer often shows only the life pattern and the amount of exercise calories rather than the number of walking. In addition, to measure patterns accurately, not only motion sensors but also devices that simultaneously measure human body temperature or heat rate using a heat flux sensor are commercially available. There is a discomfort to wear on the body at the same time. It is difficult to develop an algorithm that detects the movement patterns of all people because there are a variety of people's walking patterns, and the algorithm that calculates the number of people's walking using motion sensors has similar disadvantages to those of pedometers using pedometer sensors. Because there is.

As in the above cited example, the walking counting algorithm using the accelerometer uses a method of finding and calculating the average value of the signal and the peak value of the variance by adding the vector values of the x, y, and z axes of the accelerometer for a predetermined time. The algorithm of the present invention adopts a method of classifying patterns based on a predetermined threshold value with respect to the average value of the signal. However, in order to improve the accuracy of the algorithm for classifying life patterns in this way, a lot of data must be calculated.In order to calculate in a small measuring device worn by humans, the internal parts of the device must be highly specified. There is a disadvantage in that the size increases. The reason is that people's life patterns are so diverse that finding a peak requires a lot of data and operations.

In addition, the accuracy of exercise calorie amount, which is a factor that existing pedometers and life pattern measuring devices place importance on, improves as more measurement sensors are added, but considering the size and battery capacity for user convenience, multiple sensors There is also a limit to the measurement of various biological signals using.

Unlike the conventional pedometer or life pattern measuring apparatus, the life pattern measuring apparatus according to the present invention should focus more on calorie consumption than accurate calorie consumption in order to improve compliance for user's habit correction.

This is because people are accustomed to measuring the amount of exercise based on the number of steps of the existing pedometer, and the standard of walking is fixed as a stereotype in the head, and the user can easily understand the reliability of the device. This is because the accuracy of the algorithm for calculating the accuracy is more important than the conventional pedometer or life pattern measuring device. And life pattern measuring apparatus according to the present invention is to measure the daily life patterns of people to understand the life habits of users and to provide a personalized feedback specifically, so the measurement accuracy should be higher than conventional devices.

In order to solve the above problems, by using a combination of the advantages of the low-cost pedometer sensor of the present invention and the data of the high-precision motion sensor, an algorithm for enabling accurate walking count measurement and accurate behavior pattern measurement of users can be easily implemented. It is to provide a life pattern measuring method and apparatus that can be accurately measured by the number of walking and accurate behavior patterns of the user with an easily implemented algorithm.

The device of the present invention for achieving the above object is an acceleration sensor for detecting the movement of the user, a pedometer sensor for detecting the movement of the user, the number of walking of the user by analyzing the signal input from the acceleration sensor and the pedometer sensor and It characterized in that it comprises a data processing unit for analyzing the life pattern of the user.

Here, the data processor detects a walking peak candidate by receiving a signal filtered from an acceleration sensor signal and detects a walking peak, and detects a walking reference point by receiving a signal input from a pedometer sensor, and then uses the walking acceleration received by the walking peak candidate detection unit. A first pedestrian detector detecting first pedestrian reference points by verifying a pattern, a pedestrian waveform analysis unit receiving pedestrian reference points from a first pedestrian detection unit and receiving a signal filtered by an acceleration sensor signal; A walking pattern detector for determining a walking pattern with a signal input from the walking waveform analyzer, and a final walking number detector for receiving a walking reference point from the first walking number detector and verifying the walking pattern to finally determine the number of walking. Characterized in that consisting of.

The method of the present invention is characterized by simultaneously measuring the user's movement with an accelerometer and a pedometer sensor, filtering the signal output from the accelerometer, measuring the number of walking using the accelerometer and pedometer sensor signals, and measuring life patterns. do.

The method of measuring the number of walking here detects a walking reference point using a signal input from a pedometer sensor, calculates a walking peak using filtered acceleration sensor data, and verifies the walking reference point as the walking peak to make a first walking. The method may include detecting a reference point and determining the number of walks according to the determined walk pattern type.

Here, the method of measuring a living pattern detects a walking reference point using a signal input from a pedometer sensor, calculates a walking peak using filtered acceleration sensor data, and verifies the walking reference point as the walking acceleration pattern to make a primary walking. A reference point is detected, a walking waveform is analyzed based on the walking reference point based on the signal of the filtered acceleration sensor, and a walking pattern is determined.

The life pattern measuring apparatus using the composite sensor of the present invention can easily develop a walking number and life pattern measuring algorithm by using an existing pedometer sensor as a motion sensor, and can increase the measurement accuracy of the walking number and living pattern. There is this.

Hereinafter, with reference to the accompanying drawings, it will be described in detail the present invention. As the inventive concept allows for various changes and numerous embodiments, particular embodiments will be illustrated in the drawings and described in detail in the text. However, this is not intended to limit the present invention to the specific disclosed form, it should be understood to include all modifications, equivalents, and substitutes included in the spirit and scope of the present invention.

The life-pattern measuring device of the present invention can be implemented in a portable electronic device in a portable size, for example, combined with an electronic pedometer, a portable electronic blood pressure monitor, an MP3 player, a PMP, a DMB receiver, a portable radio, or a mobile phone.

1 is a block diagram of a lifestyle measurement apparatus using a composite sensor according to the present invention.

The device 100 of FIG. 1 includes an acceleration sensor 110, a filtering unit 111, a pedometer sensor 120, a data processing unit 130, a feedback unit 140, a memory unit 150, and an interface unit 160. Include.

The acceleration sensor 110 is composed of a two-axis or three-axis accelerometer sensor. The acceleration sensor 110 outputs a detection signal in response to the movement of the user.

The filtering unit 120 filters the input acceleration signal to be used as a walking acceleration pattern and a walking waveform analysis. The pedometer sensor 120 is a tilt sensor, or one end of the elastic wire is fixed, with a slightly heavy weight on the other end and there is a contact below or above the weight and further shakes according to the wearer's movement to touch the additional switch, one by one It consists of a single pendulum using the law of gravity and inertia that goes up. The data processor 130 receives the filtered acceleration signal and the signal of the pedometer sensor and detects the number of walking or life patterns. The interface unit 160 exchanges the detected number of walking or life pattern data with an external device. The memory unit 150 may store the detected data, and algorithm and other data may be stored. The feedback unit 140 presents the generated information to the user through vision (liquid crystal display), hearing (speaker) or tactile (vibration generator).

2 is a detailed block diagram of a preferred embodiment of the data processing unit 130 of FIG. 1.

Referring to FIG. 2, the data processor 130 may include a primary walking count detector 131, a walking peak candidate detector 132, a walking waveform analyzer 133, a walking pattern detector 134, and a final walking count detector 135. It includes.

The walking peak candidate detector 132 receives a signal filtered by the acceleration signal and detects a walking peak to detect a walking candidate acceleration pattern. At this time, the life pattern corresponding to the walking candidate acceleration pattern includes all patterns such as walking, running, jumping in place, jumping rope, and climbing stairs when the foot of the person falls off the ground and is reattached. The first walking count detector 131 receives a signal input from a pedometer sensor, detects a walking reference point, and verifies the walking reference point with a signal input from the walking peak candidate detector to determine a walking reference point. For example, the number of walks of a pedometer sensor is counted. If there is a walking pattern acceleration signal, the walking number is obviously obvious. If there is no walking count with a signal from the pedometer sensor, and there is no walking pattern acceleration signal, there is no walking number. Here, the number of walking means to recognize as a walking number when the walking reference point is recognized first. However, although the number of steps is counted by the pedometer sensor signal, it is determined that there is no number of steps if there is no walking pattern acceleration signal. This is the case when the measurement device is shaken hard by hand or weak movement when boarding a moving object.

On the contrary, it was found that there was no walking count due to the signal from the pedometer sensor, and if there was a walking pattern acceleration signal, the walking count was counted. This is the case when the back squeezer lifts the pedometer sensor into a non-moving category. That is, the walking candidate peak of the accelerometer is detected and determined based on the walking candidate acceleration pattern. The more walking candidate acceleration patterns, the higher the accuracy can be. However, in order to classify the acceleration pattern of climbing or climbing hills and walking, a reference point is required. When the reference point classification is difficult, the walking reference point detected in the pedometer signal is used as a reference point. That is, since the accuracy of the walking candidate acceleration pattern is not 100%, the walking reference point may be exceptionally determined based on the walking reference point that is output from the pedometer sensor. The walking waveform analysis unit 133 receives a signal filtered by the acceleration sensor signal and analyzes a walking waveform based on a walking reference point output from the first walking number detector, and the walking pattern detection unit 134 is input from the walking waveform analysis unit. Analyze the data to determine the walking pattern based on internal standards. Here, the life pattern is determined by the gravitational and horizontal components of the acceleration signal, i.e., slow walk, walk, fast walk, run, and step up. , Step down, run in place, and the like. The final walking count detector 135 verifies the walking reference point output from the first walking count detector by the determined walking pattern between the reference points to finally determine the number of walking. The number of walks may be finally calculated only when the result of analyzing the pattern of the corresponding period is recognized as a predetermined walk pattern by being set as the walking reference point in the first walking count detection unit, thereby increasing the accuracy of the walking count.

The technique of determining the activity pattern by analyzing the acceleration signal uses the same or similar technology as the technique disclosed in the registered patent No. 601981, but the present invention cross-verifies with a pedometer sensor and an acceleration sensor signal to improve the accuracy of the algorithm. It is easy to classify life patterns and improve the accuracy because it defines the walking cycle based on the standardized walking reference point and classifies very similar life patterns such as stairs and slopes.

3A shows an acceleration sensor signal and a pedometer sensor signal when walking at slow steps (2 m / s). The first walking cycle of FIG. 3A refers to the number of walking two feet of the left foot. The number of walks may be matched one-to-one with the walking reference point illustrated in the drawing. That is, if it is verified that the walking reference point of the pedometer signal is correct, it can be determined as one walking number. Thus, both the acceleration waveform and the pedometer sensor waveform represent two walking numbers. In the second walking cycle (part A), the acceleration waveform detects two walking numbers, but the oscillator does not detect the walking number in the dotted line portion. In this case, the first walking count detection unit verifies the number of walking by using the walking acceleration pattern input by the walking peak candidate detector by the walking count input from the pedometer sensor to determine the number of walking and improves accuracy. In addition, part B indicates a double counting when contact is made with a contact terminal by a mechanical configuration of a pedometer sensor. This is the case when a person skips a rope or thumps strongly.

In addition, by setting the reference point for each step that allows the acceleration sensor to detect the minute difference of movement, it is easy to distinguish what the pattern of each step is and increase the accuracy. That is, the complexity of the algorithm increases due to the slight change in the accelerometer signal of the flat, stair, and slope. At this time, if the algorithm is implemented by using the walking reference point of the pedometer sensor, the complexity can be considerably reduced, which makes the algorithm easy to implement. In addition, the algorithm can be implemented in a small CPU when configuring the device.

3b shows an acceleration sensor signal and a pedometer sensor signal when walking at a fast pace (5 m / s). Figure 3c also shows an acceleration sensor signal and a pedometer sensor signal when running (8 m / s). In this case, due to the rather fast movement, the shape in which the double counting does not occur. Therefore, in the general step, the number of walking by the existing pedometer sensor shows the measured value exaggerated than the actual value.

When two sensors are used in parallel, the number of walking can be measured more accurately, the life pattern analysis can be easily implemented, and the accuracy is also improved, so that the user can know the exact lifestyle of the user. Can provide. In addition, since the additional pedometer sensor is not an expensive component, it can be inexpensively configured, which can contribute to the improvement of national health by widely distributing more accurate devices at the same price point.

1 is a block diagram of a lifestyle measurement apparatus using a composite sensor according to the present invention.

2 is a detailed block diagram of a preferred embodiment of the data processing unit 130 of FIG. 1.

3A shows an acceleration sensor signal and a pedometer sensor signal when walking at slow steps (2 m / s).

3b shows an acceleration sensor signal and a pedometer sensor signal when walking at a fast pace (5 m / s). .

3C shows the acceleration sensor signal and the pedometer sensor signal when running (8 m / s).

Claims (6)

An acceleration sensor detecting a user's movement; Pedometer sensor for detecting the movement of the user; And a data processor analyzing the signals input from the acceleration sensor and the pedometer sensor to analyze the number of walking of the user and the living pattern of the user. The method of claim 1, wherein the data processing unit A walking peak candidate detector configured to receive a signal filtered by the acceleration sensor signal and detect a walking peak; A first walking count detector for detecting a walking reference point by receiving a signal input from the pedometer sensor and verifying the walking reference point by a walking acceleration pattern input by the walking peak candidate detector; A walking waveform analysis unit for analyzing a walking waveform by receiving a signal filtered through an acceleration sensor signal and receiving a walking reference point from a first walking count detector; A walking pattern detector for determining a walking pattern based on a signal input from the walking waveform analyzer; The first pedestrian detection unit receives a pedestrian reference point and verifies it with a pedestrian pattern. Simultaneously measuring a user's movement with an accelerometer and a pedometer sensor; Filtering a signal output from the accelerometer; Determining the number of walking using an accelerometer and a pedometer signal; Life style measurement method comprising the step of determining the life pattern using the accelerometer and pedometer signal. The method of claim 3, wherein the walking number measuring method Detecting a walking reference point using a signal input from a pedometer sensor; Calculating a walking peak using the filtered acceleration sensor data, and detecting the first walking reference point by verifying the walking reference point as the walking peak; And determining the detected primary walking reference point as the number of walking. The method of claim 3, wherein the walking number measuring method Detecting a walking reference point using a signal input from a pedometer sensor; Calculating a walking peak using the filtered acceleration sensor data, and detecting the first walking reference point by verifying the walking reference point as the walking peak; And determining the number of walks according to the type of walking pattern determined during the walking reference point. The method of claim 3, wherein the living pattern is measured. Detecting a walking reference point using a signal input from a pedometer sensor; Calculating a walking peak using filtered acceleration sensor data, and detecting the first walking reference point by verifying the walking reference point with the walking acceleration pattern; And analyzing a walking waveform based on the signal of the filtered acceleration sensor on the basis of the walking reference point, and determining a walking pattern.

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