JP2001344341A - Daily activity evaluation system and physical condition determining method - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a daily activity evaluation system which can accumulate daily activities of a test subject of an aged person, etc., at a low cost, ensuring privacy, while in the most natural manner, and can accurately evaluate the daily activities of the test subject, and to provide a physical condition evaluation method, which can speedily and easily evaluate the physical conditions of the test subject. SOLUTION: This system is composed of a detection means, which detects the activities of the test subject, an accumulation means which receives and stores a detection signal from the detection means, an analysis and evaluation means which analyzes the detection signal stored in the accumulation means and evaluates the daily activities of the test subject. In addition, entropy is calculated, based on usage frequency of electrical household equipment at each prescribed time, or at least one of the time of the first usage, the time of the last usage and the average time of usage of the household electrical appliances during a day is detected for comparison with the standard entropy of the household electrical appliances, which is detected and calculated beforehand or with the standard time corresponding to the time, so that the physical conditions of the test subject are evaluated and the from difference in entropy or time.

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

The present invention relates to a living activity evaluation system and a physical condition evaluation method, and more particularly to a subject,
In particular, the daily life activities of the elderly are collected in the most natural way while ensuring privacy, and the subject's life activities are evaluated, and the life activities used for nursing, nursing, medical care, and welfare institutions are evaluated. The present invention relates to a system and a physical condition determination method capable of quickly and easily determining a physical condition of a subject.

[0002]

2. Description of the Related Art In Japan, it is said that the proportion of the elderly aged 65 and over in the population will exceed 30% in the next 50 years. Under these circumstances, measures for the elderly in need of care are currently being watched and examined, but it should not be overlooked that there are many healthy elderly. Of the healthy elderly, living alone are often isolated spatially and interpersonally, and it is very important to understand the health of these people. There is an overwhelming shortage of human resources to grasp and manage the health status of people.

For this reason, for example, some municipalities have introduced a pendant-type wireless emergency call system, which is used only in an emergency and ascertains the daily health condition of the elderly. It does not do. In addition, a security system that monitors the elderly's living activities by arranging various types of sensors in their homes is provided by security companies, but the system is expensive to introduce and maintain, and is only widely used by high-income earners. is the current situation. Further, Japanese Patent Application Laid-Open No. Hei 10-248093 proposes a system in which a sensor is attached to household electric appliances to detect the on / off state of a hot water supply switch of an electric pot and monitor the safety of a consumer. The location and amount of information on electrical appliances is limited, and this system does not provide sufficient monitoring for people who rarely use electrical appliances or who unplug electrical appliances from their outlets at night. .

[0004]

SUMMARY OF THE INVENTION The present invention has been made in view of the above-mentioned conventional problems, and has a low introduction cost and a high degree of privacy while ensuring the daily living activities of subjects such as the elderly. It is an object of the present invention to provide a system that can be collected in the most natural form and that can evaluate the living activity of a subject.

It is another object of the present invention to provide a physical condition determining method capable of determining the physical condition of a subject quickly and easily.

[0006]

According to the present invention, detection means for detecting the activity of a subject, integration means for receiving and storing a detection signal from the detection means, and detection signal stored in the integration means And an analysis and evaluation means for analyzing the activity of the subject and evaluating the activity of the subject.

Here, in order to detect the degree of daily activities of a subject such as an elderly person in the most natural manner while ensuring privacy, home electric appliances (hereinafter referred to as "home electric appliances") may be used as detecting means. ) Or a vibration sensor or an acceleration sensor.

[0008] As one of the evaluation methods in the analysis evaluation means, a movement value may be calculated, and evaluation may be performed based on the movement value.

As another evaluation method in the analysis and evaluation means, a storage unit for storing reference data obtained by analyzing the accumulated living activity is provided in the analysis and evaluation means, and the data analysis result and the reference data are stored. May be compared to evaluate the living activity of the subject. Here, as a method of analyzing the degree of living activity, a non-linear analysis method, a method using entropy calculated from the frequency of use of home electric appliances at predetermined time intervals,
It is preferable to use at least one of the following: the first use time, the last use time, and the average use time of the household electric appliance in a day. Among the non-linear analysis methods, it is preferable to use the cows analysis. Furthermore, the chaos attractor is created in the cows analysis, the orbital period of the chaotic attractor is measured, and the activity of life is analyzed from the fluctuation of the orbital period. Is preferred.

[0010] It is preferable that the living activity evaluation system further includes a transmitting means for transmitting the evaluation result of the living activity by the analyzing and evaluating means, and transmits the evaluation result to a care, nursing, medical and welfare institution.

Further, according to the present invention, entropy is calculated from the frequency of use of the home electric appliance at predetermined time intervals, compared with the standard entropy of the home electric appliance detected and calculated in advance, and the physical condition of the subject is determined from the difference. Is provided.

Further, according to the present invention, at least one of the first use time, the last use time, and the average use time of the household electric appliance in a day is detected, and the home electric appliance detected in advance is detected. There is provided a physical condition determining method for comparing a standard time corresponding to the time of the electric device and determining a physical condition of the subject from the time difference.

[0013]

DETAILED DESCRIPTION OF THE INVENTION The present inventors have found that at low cost of introduction,
In addition, as a result of intensive studies, it was possible to provide a system that can accumulate the daily activities of the subjects such as the elderly in the most natural form while ensuring privacy, and also to provide a system that can evaluate the activities of the subjects. Detects and accumulates the usage status of household appliances, gas and water usage, and the movement of subjects indoors and outdoors, which are widely used without discomfort and widely used, and analyzes such detection results. Thus, the present inventors have found that it is sufficient to evaluate the living activity of the subject, and have accomplished the present invention.

That is, a major feature of the living activity evaluation system of the present invention is that the detection signals obtained by the detection means are accumulated by the accumulation means, the signals are stored in a time series, and the accumulated time series data is used. The point is that the living activity of the subject is evaluated by the analysis evaluation means.

FIG. 1 shows a schematic diagram of a living activity evaluation system according to the present invention. The signals detected by the various detection sensors 1 disposed in the house and the detection sensors 1 carried by the subject are sent to the accumulation means 2, where they are accumulated and stored as time-series data. The time-series data stored in the accumulation means 2 is analyzed and evaluated at predetermined time intervals or when a transmission request is made.
Sent to The analysis and evaluation means analyzes and evaluates the collected time series data. The evaluation result by the analysis / evaluation means 3 is sent to the subject 5 and the nursing / nursing / medical / welfare institution 6 as necessary using the transmission means 4, and is utilized for self-management of the health condition and activities by the institution. Hereinafter, each means will be individually described.

First, as the detecting means, for example, household appliances such as air conditioners, televisions, refrigerators, microwave ovens, and mats, vibration sensors, acceleration sensors, and infrared sensors can be used. It is desirable to use it for more detailed detection of life activity. For example, if a home electric appliance is used as the detection means, it is possible to detect on / off of a switch, opening / closing of a door, setting of temperature, operation mode, compressor rotation speed, remote control operation, power consumption, entry / exit, and the like. In addition, home appliances, doors, furniture, toilets, bathrooms, living rooms, bedrooms, altars, etc.
If a vibration sensor or an acceleration sensor is attached to an indoor member where vibration is transmitted well when moving indoors such as the floor, which sensor detected the vibration or acceleration at what time and when, that is, what kind of action the person performed Can be detected. Further, if a vibration sensor, an acceleration sensor, or a meter is attached to the gas or water pipe, the usage state of the gas or water can be detected. Further, if an infrared sensor is used as a detecting means, it is possible to detect that a person has entered a specific area. If a portable pedometer (registered trademark) incorporating a vibration sensor, an acceleration sensor, a tilt sensor, or the like is used as a detecting means, it is possible to grasp and record the living activity of the subject outdoors.

The detection signals detected by the various detection means are sent to an accumulation means, where they are stored as time-series data. Although there is no particular limitation on the method of transmission from the detection means to the accumulation means, it is preferable to transmit the data by radio or power line transmission because no new wiring work is required.

The time-series data accumulated and stored in the accumulation means is transmitted to the analysis and evaluation means. There is no particular limitation on the transmission method from the accumulation means to the analysis evaluation means.
A line or the like may be used. In FIG. 1, the accumulating means and the analyzing / evaluating means are separate bodies, but they may of course be formed integrally.

Next, an analysis / evaluation method in the analysis / evaluation means of claim 3 will be described. In this method, mobility is calculated, and evaluation is performed based on this value. Here, the mobility is one index indicating the distance that the subject has moved during a certain period, and indicates, for example, how much the subject has moved in a day. Hereinafter, this method will be described with reference to the drawings.

FIG. 2 is a plan view of the house, in which an entrance, a bedroom, a living room, a kitchen, and a bathroom are provided in series for easy explanation. Entrance: Entrance light,
Entrance mat, bedroom: bedroom light, bedroom air conditioner, living room: living light, TV, video, air conditioner, kitchen:
Kitchen lights, refrigerators, microwave ovens, rice cookers, bathrooms: installed in bathroom lights and washing machines. For example, Table 1 shows a case where these detection sensors respond at least once an hour as “*” marks.

[0021]

[Table 1]

By analyzing this table, it is possible to grasp the movement of the subject between the rooms. That is, according to Table 1, the subject wakes up at about 7:00, moves from the bedroom to the living room, then moves to the kitchen, and so on. Therefore, one point is obtained by moving one room, two points are obtained by moving two rooms, and four points are obtained by moving three rooms. When the movement of the subject in one day is scored as mobility, 18 points are obtained in Table 1. become. If the average value of the mobility of a very active healthy person is 30 points and the mobility of a bedridden person is 1 point, the movement value of 18 points is slightly higher than usual, and the score of the self can be understood. This will allow you to recognize your relative living activity. In addition, the calculated movement value is set to 30 or more points as “5”, 2
Four or more points are rated "4", 18 or more points are "3", 12 or more points are "2", six or more points are "1", and less than six points are "0". Is also good. By utilizing such a movement value, it is also possible to increase the motivation of the subject to increase the movement value or to move to the next higher stage.

Next, an analysis / evaluation method in the analysis / evaluation means according to claim 5 will be described. The analysis and evaluation methods include:
For example, in the simplest case, if the detection means does not detect the activity of the subject for a certain period of time or if the detection of the activity becomes extremely small, it is assumed that the subject has failed and the safety of the subject is evaluated. There is a way to do it.

As another example, time-series data on the degree of living activity of a subject is accumulated to determine a daily life activity pattern, which is stored in a storage unit as reference data, and the detected time-series data is stored. When the pattern shows a pattern different from the reference data, there is a cluster analysis method of evaluating the health condition of the subject as an abnormal condition. For example, create a time chart of each home appliance as shown in Fig. 3, how many times a day the refrigerator door opens and closes a day, how many hours a day the TV is turned on, and what time the living room lights turn on. Average data such as whether the lights are turned off is extracted and stored, and the detected data is compared with the average data to evaluate whether there is any change in the subject's state. If the detected data is significantly different from the average data, for example, the refrigerator door has been opened and closed an average of 10 times a day but has been opened and closed only a few times, or the TV is not turned on at all, or the living room light is off If not,
The subject is evaluated as having some sort of abnormality, and a notification to that effect is made to the outside.

As a further advanced analysis / evaluation method, an analysis result associated with daily living activity is stored in a storage unit as reference data, and the analysis result of the time-series data is compared with the reference data. The living activity level of the subject can be estimated and evaluated in more detail from the matched or approximated reference data. A schematic diagram of this method is shown in FIG. The time series data sent from the accumulation means 2 to the analysis evaluation means 3 is first analyzed by the analysis section 31. A specific analysis method will be described later. The analysis result associated with the living activity level of the subject is stored in the storage unit 32 as reference data.
Is stored. On the other hand, the analysis results of the time-series data sequentially transmitted are compared with the reference data, the living activity of the subject is estimated and evaluated from the matched or approximated reference data, and output by the output unit 34.

First, a case where a non-linear analysis method is used as a method of analyzing time-series data will be described. Since information on a living body such as behavior is generally nonlinear data, a nonlinear analysis method can be used as an analysis method. Among them, chaos analysis, which is widely used as a method for analyzing non-linear data, is most suitably used for the method for analyzing time-series data. The chaos analysis includes various analysis methods such as a fractal dimension analysis, a Lyapunov exponent analysis, and a chaos attractor analysis. The chaos attractor analysis is suitable as an analysis method for biological information. Above all, a method of measuring the orbital period of the chaos attractor and analyzing the degree of life activity from the fluctuation of the orbital period is preferable.

Hereinafter, this analysis method will be specifically described. FIG. 5 shows actually measured acceleration data of walking by the subject. FIG. 5 is a diagram showing a temporal change in acceleration due to walking with the horizontal axis representing time and the vertical axis representing acceleration. FIG. 5 (a) shows acceleration data in a healthy state, and FIG. This is acceleration data. As can be understood from these figures, the difference is often not clearly understood by comparing the waveforms of the acceleration data.

Next, a chaotic attractor drawn in a two-dimensional number space based on this data is shown in FIG. FIG. 6A shows a chaotic attractor in a healthy state, and FIG. 6B shows a chaotic attractor in a poor physical condition. From the shape of the chaos attractor,
If the evaluation can be performed using the pattern matching method, the evaluation may be performed at this stage, but the difference between the two may be unclear as in the case of this figure. In such a case, the pattern matching method may be used. Can not be applied.

Then, the average value of the components of the chaos attractor shown in FIG. 6 was obtained, and the chaos attractor was subjected to polar coordinate transformation using the average coordinate point as the origin to measure the orbital period of the chaos attractor. Specifically, a horizontal line S is drawn rightward from the point O of the chaos attractor, and the time required for the attractor trajectory to rotate 360 ° counterclockwise and return to the reference line using the horizontal line S as a reference line is defined as an orbit cycle. did.

FIG. 7 shows the measurement results of the attractor orbital period. FIG. 7A shows an attractor trajectory cycle in a healthy state, and FIG. 7B shows an attractor trajectory cycle in a poor physical condition. Although acceleration data and a chaos attractor were not shown, the orbital cycle of the chaos attractor on the day before the subject fell ill is shown in FIG. As can be seen by comparing (a), (b) of FIG. 7, and FIG. 8, a difference in walking data due to a difference in physical condition, which was not known by the pattern matching method of the chaos attractor, can be found from the fluctuation of the attractor trajectory cycle. It is. Therefore, if the fluctuation of the attractor trajectory period in various states is stored in the storage unit as reference data, the fluctuation characteristics of the reference data and the measured fluctuation characteristics are sequentially compared to detect the coincidence, thereby detecting the coincidence. Condition, health in the example above, a precursor to poor health,
Poor physical condition can be determined.

As a method of analyzing the fluctuation of the attractor trajectory, a detrended fluctuation analysis (DFA) is used.
tuation Analysis), frequency conversion such as Fourier transform,
Conventionally known analysis methods such as wavelet analysis and multifractal analysis can be used, but DFA is preferable because it can objectively and accurately analyze even large fluctuations.

An outline of the DFA analysis method is as follows. First, a fluctuation system is divided by a predetermined window size, and a waveform is linearly approximated for each window. Then, the absolute value of the deviation from the linear approximation is integrated, and this integrated value is used as the magnitude of the fluctuation. The magnitude of the fluctuation is plotted on the vertical axis, the window size is plotted on the vertical axis, and the magnitude of the fluctuation with respect to each window size is plotted, and the slope and y-intercept are used as indices for state determination / prediction.

FIG. 9 shows the result of analyzing the fluctuation of the attractor orbital period shown in FIGS. 7A and 7B by DFA.
In FIG. 9, the abscissa represents the window size, and the ordinate represents the magnitude of the fluctuation, and the healthy state and the poor physical condition are represented by a solid line and a broken line, respectively. The slope of the broken line when the physical condition is poor is smaller than that of the solid line when healthy. Therefore, in this case, it is possible to evaluate the health and physical condition mainly from the change in the slope of the line.

FIG. 10 shows the result of analyzing the fluctuation of the attractor trajectory cycle by DFA using the vibration data during walking as the time series data. The vibration during walking was measured using a vibration switch (manufactured by Koshin). Since the output of the vibration switch is a digital signal of "1" and "0", the signal is added and averaged, smoothing is performed, and a chaos attractor is drawn. Similarly to FIG. 9, FIG. 10 shows a healthy state and a poor physical condition by a solid line and a broken line, respectively, with the horizontal axis representing the window size and the vertical axis representing the magnitude of the fluctuation. Since the y-intercept and the slope are different between the solid line at the time of health and the broken line at the time of poor physical condition, it is possible to determine the health and poor physical condition from these differences.

As a method of analyzing the degree of living activity, a case will be described below in which entropy calculated from the frequency of use of home electric appliances at predetermined time intervals is used. The entropy is a value that quantifies the degree of variation of the sample in the statistical distribution, and indicates here whether the home electric appliance has been used in a diffused or concentrated manner. The present inventors arranged detection means on various home electric appliances in a house of 100 test subjects (single resident), and investigated the use situation for one month. As a result, we obtained a new finding that there is a close correlation between the entropy for using home appliances and the physical condition of the subject, and found that entropy can be used as one index indicating the health status of the subject. Hereinafter, a case where a refrigerator is used as a home appliance will be described as an example.

A door open / close detector is provided in the refrigerator, and when the door of the refrigerator is opened / closed, the date and time of opening / closing and a signal of "open" or "closed" are sent to the analyzing unit 31 (shown in FIG. 4). . The door open / close detector and the analysis unit are connected by, for example, a power line, and data is sent from the door open / close detector to the analysis unit based on a network constructed on the power line. FIG. 11 shows an example of time-series data sent to the analysis unit. In FIG. 11, for example, in the first line,
It indicates that the refrigerator door was opened at 06:09:14 on July 31, 2000, and the next line indicates that the door was closed at 6:09:18 on the same day.

Next, FIG. 12 shows a histogram obtained by integrating the number of times the refrigerator door is opened and closed at predetermined time intervals. FIG.
Shows the cumulative number of times the door is opened and closed every hour, with the horizontal axis representing time and the vertical axis representing the cumulative number of times the door is opened and closed. From this door opening / closing frequency data, entropy for door opening / closing is obtained. The number of times the door is opened and closed at each time is n _i (i = 1, 2,.
Entropy E is calculated from the following equation, where N is the total number of times of opening and closing the doors per day. In the calculation of the entropy, the sum of the number of times of opening and closing every hour is calculated in the above example, but it is needless to say that various time intervals such as 1 minute, 15 minutes, and 3 hours may be used.

[0038]

(Equation 1)

FIG. 13 shows an example of the change in entropy for each day calculated in this way. FIG. 13 is a diagram showing a daily change in entropy with the vertical axis being entropy and the horizontal axis being date, and it can be seen that the entropy changes daily.

On the other hand, the subject's daily physical condition during the survey period was simultaneously investigated using a diary-type questionnaire shown in FIG. 14, and the relationship between entropy and the subject's physical condition was examined. Table 2 shows an example in which entropy of each day is entered for each subject's physical condition (good, normal, bad) obtained from the questionnaire, and FIG. 15 shows a graph thereof. In FIG. 15, the upper and lower sides of a square having a triangular notch on the left and right sides of the center are two points from the top of the mother data.
The values are 5% and 25% from the bottom.

[0041]

[Table 2]

From Table 2 and FIG. 15, it can be seen that the entropy tends to be small when the appetite is good and normal, and the entropy increases when the appetite is absent (bad). Therefore, based on the data in Table 2, the correlation between entropy and physical condition was verified by means of a hypothesis test in statistics. Table 3 shows the results. The value in Table 3 is a value indicating a probability generally called “p value”,
This is the probability of obtaining a survey result that is actually obtained assuming that there is no correlation between each subject's physical condition such as complexion and mood and entropy. In other words, if the p-value is sufficiently small, the hypothesis that there is no correlation is rejected, and there is a correlation between the two. Therefore, the smaller the value in Table 3, the stronger the correlation between the physical condition of the subject and the entropy.

[0043]

[Table 3]

Table 3 shows that there is a strong correlation between the entropy of the frequency of opening and closing the refrigerator door and appetite and sleep. For example, when the time interval is set to 15 minutes, an increase in entropy and a decrease in appetite have a p-value of 5.67 × 10 ⁻⁶ , indicating a high correlation. Similarly, increased entropy and decreased sleep are p
The value is 4.79 × 10 ⁻⁹ , indicating that there is an extremely high correlation. Table 3 (same for Tables 4, 6, and 7)
Here is the result of the variance analysis of the p-values of the whole category such as “good”, “normal”, and “bad”, but shows a strong correlation even when the correlation between “normal” and “bad” is seen There is. For example, there is a strong correlation between the increase in entropy and the appetite / sleep when the time interval is 1 minute, 15 minutes and 1 hour, and the relationship between the increase in entropy and sleep when the time interval is 3 hours. Show.

It was also found that there was a correlation between entropy and a specific physical condition when a television was used as a home appliance, as in the case where a refrigerator was used. Instead of the number of times the door was opened and closed in the case of a refrigerator, the number of signal transmissions from a remote controller (hereinafter sometimes referred to as “remote control”) was used in the case of a television. That is, the number of transmissions of a signal transmitted from the remote controller to the television, such as power on / off, channel switching, and volume increase / decrease, was used.
The calculation of the entropy and the statistical processing are the same as those in the case of the refrigerator, so they are omitted here, and the correlation between the obtained entropy and the physical condition (body pain) is shown in FIG. Table 4 shows the results of verification of the correlation between entropy and physical condition by means of a hypothesis test in statistics.

[0046]

[Table 4]

According to Table 4, when the time interval is 1 minute, the p-value between entropy and pain for the number of times of television signal transmission is 4.93 × 10 ^-7 , indicating a high correlation. I understand.

Further, the daily entropy is accumulated for a predetermined period, and the average during the period is obtained and compared with the reference entropy. You can also grasp changes in physical condition. As an example, Table 5 shows “POMS test” after 29 days of experiment on 78 subjects.
The results of examining the relationship between the two physical condition items “depression” and “fatigue” and the entropy of the number of times the refrigerator door is opened and closed are shown.

[0049]

[Table 5]

According to Table 5, "POMS inspection"
For "depression", 43 of the subjects were less likely and 34 were more likely, and the mean entropy of subjects with less "depression" was 5.32, while " The average entropy of subjects with a high probability of depression was 5.57. That is, it is considered that the higher the average entropy, the higher the probability of being in a “depression” state. When the correlation with “depression” was verified, the p-value was 0.3.
0152 showed a small value, and it was recognized that there was a correlation between both.

Regarding “fatigue”, 44 subjects among the subjects were less likely to be tired by the “POMS test”, and 33 subjects were more likely to be fatigued. The average entropy of the subject with a low possibility of being tired was 5.33, whereas the average entropy of the subject with a high possibility of being tired was 5.55. From this, it is considered that if the average entropy is high, the possibility of fatigue is high. As described above, when the correlation between entropy and “fatigue” was verified by statistical processing, the p-value showed a relatively small value of 0.0344,
It was recognized that there was a correlation between the two.

As is apparent from the above, the entropy of each subject in the normal state (normal) is calculated in advance and stored as the reference entropy, and the detection data sent daily or the data within the predetermined period are stored. The physical condition of the subject can be determined by comparing the entropy calculated from the detection data with the reference entropy. In the above-described embodiment, a refrigerator and a television are used as home appliances, but the invention is not limited to these.
Conventionally known home electric appliances can be used. In addition, by calculating a plurality of entropies from the use frequencies of a plurality of home appliances, the physical condition of the subject can be more specifically determined.

Next, a case where at least one of the first use time, the last use time, and the average use time of the household appliances in a day is used as a method of analyzing the degree of living activity will be described. As in the case where entropy was used as an analysis method, detection means were provided for various home appliances in a house of 100 test subjects (single person), and as a result of investigating the usage over a month, the usage of the home appliances was determined. New findings have been obtained that there is a close correlation between the time and the physical condition of the subject, and it has been found that this use time can be used as one index indicating the health condition of the subject. Hereinafter, a case where a refrigerator and a television are used as home appliances will be described as an example.

The refrigerator is provided with a door open / close detector, and when the door of the refrigerator is opened / closed, the time when the door is opened / closed and “open” or “closed”.
Signal is detected. In the case of a television, a signal transmission detector is provided in the remote controller, and the time when the remote controller transmits a signal to the television is detected. Then, the time when the signal transmission of the refrigerator door and the remote controller is first activated in a day, the time when the signal is finally activated, and the average time T calculated from the following equation
Were recorded, and the relationship between the physical condition of the subject examined by the diary-type questionnaire shown in FIG. 14 and the detected time was examined. The results are shown in Tables 6 and 7. Table 6
Table 7 shows the p-values calculated by the statistical processing, as in Table 3.

[0055]

(Equation 2)

[0056]

[Table 6]

[0057]

[Table 7]

From Table 6, it can be seen that, for example, the time when the refrigerator door was first opened, the time when it was last closed, and the average time of the day have a strong correlation with appetite. Also, from Table 7, it can be seen that there is a strong correlation between the time when the remote controller of the television first transmits a signal, the time when the signal is transmitted last, and the average time of the day, and appetite, for example. Therefore, each reference time in the normal state (normal) of each subject is stored, and the physical condition of the subject can be determined by comparing the time detected every day or the time averaged within a predetermined period with the reference time. . Although a refrigerator and a television are used as home appliances in the above embodiment, the home appliances are not limited to these, and conventionally known home appliances can be used. Further, by calculating the use times of the plurality of home electric appliances, the physical condition of the subject can be more specifically determined.

When entropy or the use time is used as an analysis method of the living activity evaluation system of the present invention, specifically, in FIG. 4, the normal state (normal The entropy or use time at the time of ()) is calculated by the analysis unit 31 as necessary and stored in the storage unit 32, and the entropy or use time calculated by the analysis unit 32 from the sent detection data is calculated as follows.
The reference entropy or reference time of the storage unit 32 and the evaluation unit 3
The subject's physical condition is evaluated by comparing in step 3.

The living activity of the subject evaluated by the analysis and evaluation means in this way is transmitted to the subject and the care / nursing / medical / welfare institution via communication means.
It is preferable to use the information to improve the daily life of the subject and the activities of the institution. In this case, the evaluated degree of living activity may be transmitted to the requester via the communication means also in response to a request from the subject, his or her family, the institution, or the like.

[0061]

According to the living activity evaluation system of the present invention,
Detecting means for detecting the activity of the subject, accumulating means for receiving and storing the detection signal from the detecting means, and analyzing and evaluating means for analyzing the detection signal stored in the accumulating means and evaluating the degree of living activity of the subject; With a low cost of introduction, the daily life activities of subjects such as the elderly can be accumulated in the most natural way while ensuring privacy.
Furthermore, the living activity of the subject can be accurately evaluated.

According to the physical condition judging method of the present invention, the entropy is calculated from the frequency of use of the home electric appliance at predetermined time intervals, and compared with the standard entropy of the home electric appliance detected and calculated in advance. Is determined, the physical condition of the subject can be determined quickly and easily.

In another physical condition judging method according to the present invention, at least one of a first use time, a last use time, and an average use time of the household electric appliance in a day is detected, The physical condition of the subject is compared with a standard time corresponding to the time of the home electric appliance detected in advance, and the physical condition of the subject is determined from the time difference. Therefore, the physical condition of the subject can be determined quickly and easily as described above.

[Brief description of the drawings]

FIG. 1 is a schematic configuration diagram of a living activity evaluation system of the present invention.

FIG. 2 is a plan view of a house provided with various sensors.

FIG. 3 is a time chart showing the usage status of each home appliance.

FIG. 4 is a schematic diagram of an analysis evaluation unit according to claim 5;

FIG. 5 is a diagram showing a change in acceleration during walking.

FIG. 6 is a chaotic attractor diagram drawn in a two-dimensional number space from a change in acceleration during walking.

FIG. 7 is a diagram showing a change in an attractor orbit cycle.

FIG. 8 is a diagram showing a change in an attractor orbit cycle.

FIG. 9 is a diagram showing the result of analyzing the fluctuation of the attractor orbital period by DFA.

FIG. 10 is a diagram showing the result of analyzing the fluctuation of the attractor orbital period by DFA.

FIG. 11 is an example of time-series data on the opening and closing of a refrigerator door.

FIG. 12 is a histogram showing the number of times the door is opened and closed per hour.

FIG. 13 is a diagram showing an example of daily change in entropy.

FIG. 14 is an example of a diary-type questionnaire.

FIG. 15 is a diagram showing the relationship between physical condition (appetite) and entropy.

FIG. 16 is a diagram showing the relationship between physical condition (body pain) and entropy.

[Explanation of symbols]

 DESCRIPTION OF SYMBOLS 1 Detection sensor (detection means) 2 Accumulation means 3 Analysis evaluation means 4 Transmission means 31 Analysis part 32 Storage part 33 Evaluation part 34 Output part

──────────────────────────────────────────────────の Continued on the front page (51) Int.Cl. ⁷ Identification code FI Theme coat ゛ (Reference) A61B 5/11 A61B 5/10 310Z (72) Inventor Takeshi Ogawa 22-22 Nagaikecho, Abeno-ku, Osaka-shi, Osaka Inside Sharp Corporation (72) Inventor Hiroaki Niwamoto 22-22 Nagaikecho, Abeno-ku, Osaka City, Osaka Prefecture Inside Sharp Corporation (72) Tomohiro Ishikawa 22-22 Nagaikecho, Abeno-ku, Osaka City, Osaka Sharp Stock In company

Claims (13)

[Claims] A detecting means for detecting an activity of a subject; an accumulating means for receiving and storing a detection signal from the detecting means;
A living activity evaluation system comprising: an analysis and evaluation means for analyzing the detection signal stored in the accumulating means and evaluating the living activity of the subject. 2. The living activity evaluation system according to claim 1, wherein a household electric appliance is used as said detecting means. 3. The living activity evaluation system according to claim 1, wherein at least one of a vibration sensor and an acceleration sensor is used as said detecting means. 4. The living activity evaluation system according to claim 1, wherein the analysis and evaluation means calculates a movement value and evaluates the movement value based on the movement value. 5. The analysis / evaluation means has a storage unit for storing reference data obtained by analyzing the accumulated degree of living activity, and compares an analysis result of a detection signal with the reference data, and The living activity evaluation system according to claim 1, wherein the living activity is evaluated. 6. The living activity evaluation system according to claim 5, wherein a non-linear analysis method is used as the analysis method of the living activity. 7. The living activity evaluation system according to claim 6, wherein a Cows analysis is used as the nonlinear analysis method. 8. The living activity evaluation system according to claim 7, wherein a chaotic attractor is created, the orbital period of the chaotic attractor is measured, and the degree of living activity is analyzed based on the fluctuation of the orbital period. 9. The living activity evaluation system according to claim 5, wherein the entropy calculated from the frequency of use of the home electric appliance at predetermined time intervals is used as a method of analyzing the living activity. 10. The method for analyzing the degree of living activity, wherein at least one of a time when a household electric appliance is used first, a time when it is last used, and an average use time in a day is used. Life activity evaluation system. 11. A transmitting means for transmitting an evaluation result of a living activity degree by said analysis and evaluation means, further comprising transmitting said evaluation result to a nursing care / nursing / medical / welfare institution.
The living activity evaluation system according to any one of 10 above. 12. A physical condition determination for calculating entropy from the frequency of use of the household electrical appliance at predetermined time intervals, comparing the entropy with the standard entropy of the household electrical appliance detected and calculated in advance, and determining the physical condition of the subject from the difference. Method. 13. The time of the home electric device, wherein at least one of a first use time, a last use time, and an average use time of the home electric device is detected in a day, and the pre-detected time of the home electric device is detected. A physical condition determination method for comparing a standard time corresponding to the above, and determining the physical condition of the subject from the time difference.