JP2008217478A - Behavior improvement support device, behavior improvement support method, and program - Google Patents

Behavior improvement support device, behavior improvement support method, and program Download PDF

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JP2008217478A
JP2008217478A JP2007054680A JP2007054680A JP2008217478A JP 2008217478 A JP2008217478 A JP 2008217478A JP 2007054680 A JP2007054680 A JP 2007054680A JP 2007054680 A JP2007054680 A JP 2007054680A JP 2008217478 A JP2008217478 A JP 2008217478A
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action
behavior
plurality
series
living
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Koji Kimura
Hideki Kobayashi
Toshimitsu Kumazawa
英樹 小林
浩二 木村
俊光 熊澤
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Toshiba Corp
株式会社東芝
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Abstract

<P>PROBLEM TO BE SOLVED: To provide a behavior improvement support device for reducing environmental loads generated by life behaviors. <P>SOLUTION: A plurality of different behavior systems showing order of a plurality of various kinds of life behaviors, the number of execution of the respective behavior systems, and an average generation amount of environmental loads generated by the respective behavior systems are stored in a storage means. From among the plurality of behavior systems, a plurality of behavior systems including partial systems equal to ongoing life behavior systems indicating order of a plurality of life behaviors including the current life behaviors are extracted, the extracted plurality of behavior systems are classified into a plurality of groups according to the kind of life behavior next to the partial system among the respective behavior systems. The average generation amount of environmental loads and selection probability to be probability by which a life behavior next to the partial system is selected are calculated every group. When the average generation amount of the environmental loads of a group including the life behavior whose selection probability is the highest among the plurality of groups is larger than a target value, a life behavior next to the partial system of the behavior system which belongs to other groups whose average generation amount of environmental loads is equal to the target value or below is selected as a recommended behavior and a message for recommending the recommended behavior is presented. <P>COPYRIGHT: (C)2008,JPO&INPIT

Description

  The present invention relates to a behavior improvement support apparatus and method for promoting reduction of environmental load caused by living behavior.

  According to the National Life Center survey, the public's interest in energy conservation has exceeded 90%. However, energy consumption in the private sector continues to increase, and it is said that there is a gap between environmental awareness and behavior.

  There are two measures for implementing energy saving in the home, namely, improving the performance of energy-using devices and houses and controlling the amount of energy used. Furthermore, the control of energy usage includes automatic control by hardware and indirect control by information presentation.

  Automatic control by hardware is intended to save energy by making adjustments so as to eliminate useless operation of energy-using devices based on information from sensors (for example, temperature sensors and pyroelectric sensors).

Indirect control based on information presentation is intended to save energy by displaying target values and actual values of energy usage in comparison with others and in a ranking format, etc., to raise consumers' awareness of energy conservation and encourage action. (For example, see Patent Documents 1 and 2).
JP 2001-101292 A JP 2001-344412 A

  The conventional indirect control method based on information presentation is a method that pays attention to an environmental load reduction effect related to a specific device or action. That is, an environmental load generation amount per usage amount is assigned to each device and action, and the environmental load is evaluated.

  However, even if the same action is actually performed for the same time, or even if the same equipment is used for the same time, the amount of environmental load may vary depending on the order of action and equipment use. It was not considered.

  Therefore, in view of the above problems, the present invention provides an action improvement support apparatus capable of reducing the environmental load generated from living behaviors from the viewpoint of the order of a plurality of types of living behaviors including living behaviors involving the use of devices. For the purpose.

  The behavior improvement support device includes (a) a plurality of different behavior sequences indicating the order of a plurality of different types of living behavior, the number of times each behavior sequence is performed, and an average amount of environmental load generated from each behavior sequence. Action sequence storage means for storing data; (b) setting means for setting a target value of the environmental load generation amount; (c) detection means for detecting a living action performed by the user; An extraction means for extracting a plurality of action sequences including a partial series equal to an ongoing action series indicating an order of a plurality of life actions including the current life action detected by the detection means from among the action series; e) classifying the plurality of behavior sequences extracted by the extracting means into a plurality of groups according to the type of living behavior next to the partial sequence in each behavior sequence, and the number of executions of each behavior sequence belonging to each group and Flat A first calculation means for calculating an environmental load average generation amount for each group using the generation amount; and (f) calculating a selection probability that is a probability that the next living action of the partial series is selected for each group. And (g) when the average environmental load generation amount of the group including the living behavior having the highest selection probability among the plurality of groups is larger than the target value, the average environmental load generation amount is Selecting means for selecting the next living action of the partial series of the action series belonging to another group below the target value as a recommended action; and (h) a message for recommending the recommended action selected by the selecting means. Presenting means for presenting.

  Reduce the environmental impact of daily activities.

  Embodiments of the present invention will be described below with reference to the drawings.

  FIG. 1 shows an example of the configuration of a behavior improvement support apparatus that presents information for reducing the environmental load generated from a user's daily behavior in consideration of the order of behavior and device usage according to the present embodiment.

  As shown in FIG. 1, this behavior improvement support apparatus has a target value setting unit 1, a behavior detection unit 2, an environmental load generation amount measurement unit 3 having a function of inputting data from the outside to the device, and stores data. The action sequence storage unit 4, the recommended action execution rate storage unit 9, the presentation information storage unit 11, the target value storage unit 14, and the action sequence extraction unit 5 and the selection probability calculation unit 6 having a calculation function. , An average generation amount calculation unit 7, an information presentation determination unit 8, a recommended behavior selection unit 10, a database update unit 13, and an information presentation unit 12 having a function of outputting a message for promoting a recommended behavior.

  The behavior detection unit 2 detects the type of living behavior performed by the user. The behavior detection unit 2 is connected to a plurality of (here, for example, n) detection devices installed in a house such as a pyroelectric sensor, a device that detects opening / closing of a door, and a device that detects ON / OFF of a device. Yes. The behavior detection unit 2 includes, for example, a sensor for detecting opening / closing of a front door, a sensor for detecting opening / closing of a closet door, a kitchen light switch, a dishwasher, a bathroom light switch, a personal computer (PC) ), Connected to a plurality of detection devices 2-1 to 2-n arranged in a house, such as a device for detecting an on / off state of a TV, a bedroom light, etc., and each of the detection devices 2-1 to 2-n The type of the user's living behavior is detected based on the information detected from.

  For example, when detecting the living behavior of the user within the period from when the user comes home to bedtime, the opening and closing of the door is detected by a sensor installed at the door of the entrance, so that the user's “home” Detecting the behavior and detecting the user's "change of clothes" behavior by detecting the opening and closing of the door by a sensor installed in the closet door, and if the kitchen light switch is ON for a certain time or more "Cooking" behavior is detected, the user's "meal cleaning" behavior is detected by turning on the dishwasher, and the user's "bathing" behavior is detected when the bathroom light switch is on for a certain period of time, When the PC is turned on, the user's “PC use” behavior is detected. When the TV is turned on, the user's “TV viewing” behavior is detected. When the light is turned off, the user's “sleep” behavior is detected. To detect.

  The action detection unit 2 constantly measures the human activity line, posture / activity amount / pulse rate, window / door open / closed state, and usage status of home appliances. Sensor information including information such as length may be collected to estimate living behavior.

  The types of living behavior detected by the behavior detecting unit 2 are predetermined based on living behaviors that are expected to have a large influence on the amount of environmental load generated. In the present embodiment, a case will be described in which a period from when a user returns home to going to bed is monitored, and the living behavior of the user within this monitoring target period is detected. In this case, the behavior detection unit The types of living behavior detected in 2 are “return home”, “change of clothes”, “cooking”, “meal cleaning”, “bathing”, “use of personal computer (PC)”, “TV viewing”, “sleep”. There are 8 types.

  In the action sequence storage unit 3, a plurality of action sequences within the monitoring target period that the user has performed in the past are stored together with the number of executions of the action sequence and the average amount of environmental load generated from the action sequence. The action series indicates the order of a plurality of different life actions performed by the user within the monitoring target period.

  FIG. 3 is an example of action sequence data stored in the action sequence storage unit 4. The action series data includes a number for identifying the action series, an action series representing the types of living behaviors in the order of execution, the number of times the action series was executed, and the CO2 generated when the action series was executed. Including the average amount of In the example of FIG. 3, the monitoring target period is from returning home to going to bed, and each behavior series is represented by arranging symbols representing the types of living behavior in the order of implementation from the left.

  The recommended action execution rate storage unit 9 presents a recommended number of times indicating the number of times selected as a recommended action and a message prompting the recommended action for each life action to be detected within the monitoring target period performed by the user in the past. The execution rate (execution probability) indicating the high possibility of being executed when selected as a recommended action, which is calculated from the number of executions indicating the number of times actually executed, the number of recommendations, and the number of times of execution is stored. ing.

  FIG. 4 is an example of recommended action execution rate data stored in the recommended action execution rate storage unit 9. In the example of FIG. 4, the recommended number, the number of executions, and the execution probability of each living action are classified for each selection probability described later, but it is not always necessary to classify in this way.

  FIG. 2 is a flowchart for explaining the processing operation of the behavior support apparatus of FIG. 1, and is an information presentation method for reducing the environmental load generated from the user's daily behavior in consideration of the order of behavior and device usage. The procedure is shown.

  Here, a period from when a predetermined specific user comes home to bedtime (from the time when the behavior detecting unit 2 detects the starting behavior “home”) until the end behavior “sleep” is detected. 2), a specific example of presenting information for reducing the environmental load generated from the living behavior of the specific user will be described with reference to the flowchart of FIG.

  In step S <b> 1, the target value setting unit 1 sets the target value of the environmental load generation amount in the target period. The set target value is stored in the target value storage unit 14. The target value may be arbitrarily set by a person. Alternatively, the target value setting unit 1 may automatically set the target value. For example, the environmental load generation amount in the same month of the previous year may be automatically set as the target value.

Here, the environmental load refers to a cause of hindrance in global environmental conservation depending on human behavior, and refers to water usage, power usage, gas usage, carbon dioxide (CO 2 ) emissions, and the like. In the present embodiment, the CO 2 emission amount is difficult to directly measure the environmental load. Therefore, it is assumed that the CO 2 emission amount is calculated by calculation from the usage amount of electric power or gas measured by a power meter, a flow meter or the like. Details will be described later.

  In step S2, the type of living behavior performed by the specific user during the period targeted by the behavior detection unit 2 is detected. In the present embodiment, the types of living behaviors to be detected are specifically “returning home”, “changing clothes”, “cooking”, “mealing”, “bathing”, “using a personal computer (PC)”, There are eight types, “TV viewing” and “sleep”.

  When the user's “return home” behavior is detected by the behavior detection unit 2 in step S2, the behavior sequence extraction unit 5 stores “return home” as the first ongoing behavior sequence, and monitors the sequence of living behavior. Start. The behavior detection unit 2 detects the behavior detection unit 2 by adding the new behavior to the end of the ongoing behavior sequence each time the behavior detection unit 2 detects a new lifestyle behavior of the user thereafter. The in-progress action series in which the types of the living actions are arranged in the order of detection is stored.

  As will be described later, each time a behavior of the user is detected by the behavior detector 2, whether or not a message has been presented (to encourage a recommended behavior) before the behavior is detected (step S3) It is checked whether or not the monitoring target period has ended, that is, whether or not “sleep” at the end point of the monitoring institution is added to the ongoing action sequence (step S4). If the monitoring target period has just started, there will be no message presentation or end of the monitoring target period, as will be described later. Therefore, in step S2, the behavior detection unit 2 detects a new living behavior of the user. Then, the process proceeds to step S5 as it is through step S3 and step S4.

  In step S <b> 5, the behavior sequence extraction unit 5 adds the new life behavior of the user detected by the behavior detection unit 2 to the end of the ongoing behavior sequence. And the action series containing the partial series which shows the order of the life action equal to an ongoing action series is extracted from the some action series memorize | stored in the action series memory | storage part 4. FIG.

  For example, if the current action sequence in progress is “return home”, “changing clothes”, “cooking”, “meal”, “meal cleanup”, from the action sequence data in FIG. The action sequence of “No. 7” is extracted.

  Here, the action series data will be described. “No. 4” and “No. 8” in the action series data in FIG. 3 are action series in which the order of actions is reversed by one place, but the average amount of CO2 generated in each action series is greatly different. Is open. There are various causes for this. For example, when the order of actions changes, the action time with high environmental load increases (eg, when watching TV just before going to bed, it takes a long time), and extra environmental load occurs (requiring cooking when bathing is delayed) The environmental load due to timing is reduced (if it is performed immediately before going to bed, the late-night power is used and the environmental load is reduced).

  Understanding these causes and considering the order of efficient actions can be a daunting task. In addition, it is thought that the tendency varies depending on the individual. As shown in this embodiment, the more personal action series data is stored, the more information can be presented to each individual.

In step S6, the average generation amount calculation unit 7 first classifies the plurality of extracted action sequences into a plurality of groups according to the type of the next living action in the partial series equal to the ongoing action sequence. Further, the average generation amount calculation unit 7 calculates the average generation amount E of the environmental load for each group according to the equation (1).

here,
E: Average amount of environmental load in a group n i : Number of executions of action series i belonging to the group ei: Average amount of environmental load of action series i belonging to the group The current ongoing action series is “return home” In the case of “changing clothes”, “cooking”, “meal”, and “meal cleaning”, the action sequences of “No. 1” to “No. 7” are extracted from the action sequence data of FIG. In the extracted action sequences of “No. 1” to “No. 7”, the next living behavior of the partial sequence equal to the ongoing action sequence is four types of “bathing”, “TV viewing”, “PC use”, and “sleep”. is there. That is, the extracted seven action sequences are the first group including “No. 1” and “No. 2”, and the second group including “No. 3”, “No. 4”, and “No. 5”. The group is classified into four groups: a third group including “No. 6” and a fourth group including “No. 7”.

Based on the formula (1), the average CO 2 generation amount of the first group (“No. 1” and “No. 2”) is {(31 × 3823) + (15 × 3797)} ÷ (31 + 15) = 3814.8 g. Similarly, the average CO2 generation amount of the second group (“No. 3”, “No. 4”, and “No. 5”) is {(19 × 3961) + (33 × 4025) + (5 × 4116). )} ÷ (19 + 33 + 5) = 4011.6 g. Since the third group (“No. 6”) and the fourth group (“No. 7”) each belong to only one action series, the average CO2 generation amount for each group and each action series match. That is, the average CO2 generation amount of the third group is 4087 g, and the average CO2 generation amount of the fourth group is 3760 g.

In step S7, the selection probability calculation unit 6 calculates the probability (selection probability) P that the next living action in the partial series equal to the ongoing action series is selected (by the user) for each group, as in step S6. Calculate according to (2).

here,
P: Probability of selection for a group
n i : Number of executions of action series i belonging to the group
n j : Number of executions of the plurality of action sequences j extracted in step S5 When the ongoing action sequence is “going home”, “changing clothes”, “cooking”, “meal”, “meal cleanup”, the action sequence data of FIG. Action sequences “No. 1” to “No. 7” are extracted. As described above, the extracted seven behavior sequences from “No. 1” to “No. 7” are the next life behavior “bathing”, “TV watching”, “PC” of the partial sequence equal to the ongoing behavior sequence. It is classified into the above four groups according to “use” and “sleep”.

  In the first group (“No. 1” and “No. 2”), the selection probability of the next living action “bathing” in the partial series equal to the ongoing action series is (31 + 15) based on the equation (2). ) ÷ (31 + 15 + 19 + 33 + 5 + 3 + 9) = 46 ÷ 115 = 0.4. Similarly, in the second group (“No. 3”, “No. 4” and “No. 5”), the selection probability of the next living action “TV viewing” in the partial series equal to the ongoing action series is (19 + 33 + 5) ÷ 115 = 0.5, and in the third group (“No. 6”), the selection probability of the next living action “PC use” in the partial series equal to the ongoing action series is 3 ÷ 115 = 0.03, In the fourth group (“No. 7”), the selection probability of the next living action “PC use” in the partial series equal to the ongoing action series is 9 ÷ 115 = 0.08.

  In step S <b> 8, the information presentation determination unit 8 determines whether to present a message that encourages a recommended action based on the average amount of environmental load calculated for each group and the selection probability.

  The information presentation determination unit 8 presents a message when the average environmental load generated in step S6 is smaller than the target value set in step S1 for the group including the living action with the highest selection probability. Absent. In this case, the process returns to step S2.

  On the other hand, if the environmental load average generation amount calculated in step S6 for the group including the living action with the highest selection probability is equal to or greater than the target value set in step S1, the process proceeds to step S9 to present a message. .

For example, if the target value set in step S1 is 4000 g, in the case of the above example, the group including the living action with the highest selection probability is the second group (“No. 3”, “No. 4”, “No. 5”). "TV viewing" (50%). Since the average CO 2 generation amount of this second group is 4011 g, it is necessary to present a message that exceeds the target value and prompts the recommended action.

  In step S <b> 9, the recommended action selection unit 10 selects, as a recommended action, the next living action in the partial series that is equal to the ongoing action series from other groups whose average environmental load is equal to or less than the target value. Here, the recommended action is determined based on the recommended action execution rate data as shown in FIG. 4 stored in the recommended action execution rate storage unit 9.

  In the above-described example, the action sequences “No. 1” to “No. 7” extracted from the action sequence data of FIG. 3 are classified from the first group to the fourth group, and the environmental load average generation for each group occurs. The amount is calculated to be 381.8 g for the first group, 4011.6 g for the second group, 4087 g for the third group, and 3760 g for the fourth group. The selection probability of the next living action of the same partial series was 40% in the first group, 50% in the second group, 3% in the third group, and 8% in the fourth group.

  Here, it is assumed that the target value of the environmental load generation amount is 4000 g. The group including the living action with the highest selection probability is the second group, and the average generation amount of the environmental load is 4011.6 g, which exceeds the target value. Therefore, the recommended action selection unit 10 from the group whose average environmental load is less than the target value of 4000 g has the highest possibility of being implemented (implementation probability) and is the next life in the partial series equal to the ongoing action series. Select an action.

  In the above example, it is the first group and the fourth group whose average environmental load is less than or equal to the target value. From the recommended action execution rate data of FIG. 4, when the next living action “bathing” of the partial group of the first group is recommended with a selection probability of 40%, it is executed with a probability of 29.6%. In addition, when the next living action “sleep” of the partial group of the fourth group is recommended when the selection probability is 8%, it is implemented with a probability of 6.3%. In this case, the recommended action selecting unit 10 determines the next living action “bathing” of the partial series of the first group as the recommended action.

  In step S10, the information presentation unit 12 reads a message corresponding to the life behavior selected as the recommended behavior from the presentation information storage unit 11, and sets the message to a mobile terminal owned by the user, a predetermined location. It is presented by voice or text on a video monitor or a predetermined terminal. The message may be transmitted according to the type of vibration or odor. Moreover, you may make it show the content of a message by the method of combining these methods.

  As shown in FIG. 5, the presentation information storage unit 11 stores a message to be presented when the living behavior is selected as the recommended behavior for each type of living behavior. For example, when “bathing” is selected as the recommended action, the information presentation unit 12 outputs a voice message “Take a bath and refresh your mind and body.”

  After presenting the message prompting the recommended action, the process returns to step S2 again, and if a new life action of the user is detected, the process proceeds to step S3. Since the life behavior detected this time is detected after presenting the message prompting the recommended behavior in steps S9 to S10, the process proceeds from step S3 to step S11.

  In step S11, the database update unit 13 updates the recommended action execution rate storage unit 9 based on whether the living action detected immediately after the message presentation is the recommended action selected in step S9. That is, in the case of “bathing” in which the life behavior detected immediately after the message is presented is selected as the recommended behavior, the database update unit 13 selects the selection probability “20” of the life behavior “bathing” in the recommended behavior implementation rate storage unit 9. The recommended number of times and the number of times of implementation in “greater than 40” are incremented by one each, and a new execution probability (= updated number of times of execution / recommended number of times of update) is calculated from the updated number of times of recommendation and the number of times of execution. The execution probability stored in the recommended action execution rate storage unit 9 is updated with the value of the calculation result. If the life behavior detected immediately after the message presentation is not “bathing” selected as the recommended behavior, the database update unit 13 determines that the selection probability “20” of the life behavior “bathing” in the recommended behavior implementation rate storage unit 9 is larger. The number of recommendations in “40 or less” is incremented by 1, and a new execution probability (= current execution number / recommended number after update) is calculated from the updated recommendation number and the current execution number. The execution probability stored in the recommended action execution rate storage unit 9 is updated.

  After updating the recommended action execution rate storage unit 9 as described above, the process proceeds to step S4. In step S4, when the action detection unit 2 detects the user's "sleep" action, which is the end point of the ongoing action series, that is, the living action defined as the end point of the monitoring target period (for example, "sleep" here) ") Is detected, the process proceeds to step S12. When the behavior detecting unit 2 detects a living behavior other than “sleep”, the process proceeds to step S5, and the same processing operation as described above is repeated.

  In step S12, the database update unit 13 receives from the behavior sequence extraction unit 5 a new behavior sequence with “sleep” added to the end of the ongoing behavior sequence, and based on this new behavior sequence, the behavior sequence storage unit 4 is updated. Specifically, when there is an action sequence in the action sequence data stored in the action sequence storage unit 4 that matches the order of the new action sequence and the living action, the number of executions of the action sequence is set to 1. Increment by one. If there is no action series in the action series data stored in the action series storage unit 4 that matches the order of the new action series and the living action, the new action series is added and the number of times of execution is added. Is “1”.

  Furthermore, based on the environmental load generation amount measured by the environmental load generation amount measuring unit 3 for the new action sequence, the environmental load average generation amount of the action sequence data is also updated.

Here, since it is difficult to directly measure the CO 2 emission amount measured as the environmental load in the example of the action series data in FIG. 3, the usage amount of the electric power and gas measured by a wattmeter, a flow meter, etc. It shall be calculated based on the calculation.

The environmental load generation amount measuring unit 3 stores basic unit data for calculating the CO 2 emission amount from the usage amount of electric power and gas as shown in FIG. The environmental load generation amount measuring unit 3 calculates the product of the power consumption measured from the action sequence of the specific user from returning home to sleeping within the monitoring target period and the CO 2 emission basic unit per unit of power. The CO 2 emission amount (CO 2 generation amount) accompanying use is obtained. Similarly, the product of the amount of gas used and the CO 2 emission basic unit per unit of gas is calculated to obtain the CO 2 emission amount (CO 2 generation amount) accompanying the gas use. The sum of the CO 2 emission amount that accompanies the power usage amount and the gas usage amount becomes the CO 2 emission amount generated from the action sequence from the return of the specific user to sleep. Furthermore, the amount of water used may be measured, and the CO 2 emission accompanying water use may be included as the CO 2 emission to be evaluated. Also, for CO 2 emission intensity that varies depending on the time period used, such as electric power, prepare CO 2 emission intensity for each time period, measure the usage amount for each time period, and measure CO 2 emissions. May be calculated.

  The database updating unit 13 calculates the environmental load generation amount calculated for the new behavior sequence by the environmental load generation amount measurement unit 3 as described above, and the past behavior in which the order of the new behavior sequence and the living behavior matches. The environmental load average generation amount is recalculated from the environmental load generation amount of the series, and the environmental load average generation amount of the action sequence storage unit 4 is updated with the obtained new value.

  As described above, according to the above embodiment, by presenting information for energy saving in consideration of the order of a plurality of living activities including living activities involving the use of equipment, that is, the environmental load is reduced and implemented. Environment that is generated from living behaviors by improving the order of multiple types of living behaviors, including living behaviors that involve the use of devices, by presenting messages that encourage lifestyle behaviors that have a high probability of being performed (high probability of implementation) as recommended behaviors The load can be reduced.

  The behavior detection unit 2 detects a behavior sequence from the user's return home to sleep, and repeats the operation of calculating the environmental load generation amount by the environmental load generation amount measurement unit 3 for the behavior sequence. The database update unit 13 collects and creates action sequence data including the number of executions of each action sequence and the average amount of environmental load as shown in FIG. 3, and then performs the processing operation shown in FIG. Also good.

  The method of the present invention described in the embodiment of the present invention (functions of each component in FIG. 1) is a program that can be executed by a computer as a magnetic disk (flexible disk, hard disk, etc.), optical disk (CD-ROM). , DVD, etc.) and storage media such as semiconductor memory can also be distributed.

  Note that the present invention is not limited to the above-described embodiment as it is, and can be embodied by modifying the constituent elements without departing from the scope of the invention in the implementation stage. In addition, various inventions can be formed by appropriately combining a plurality of constituent elements disclosed in the embodiment. For example, some components may be deleted from all the components shown in the embodiment. Furthermore, constituent elements over different embodiments may be appropriately combined.

The figure which showed the structural example of the action assistance apparatus which concerns on one Embodiment of this invention. The flowchart for demonstrating the processing operation of a life assistance apparatus. The figure which shows an example of the action sequence data memorize | stored in an action sequence memory | storage part. The figure which shows an example of the recommendation action implementation rate data memorize | stored in a recommendation action implementation rate memory | storage part. The figure which shows an example of the presentation information data (message) memorize | stored in a presentation information storage part. In environmental load measuring unit, illustrates an example of a basic unit data for calculating the CO 2 emissions from the use of electricity or gas.

Explanation of symbols

DESCRIPTION OF SYMBOLS 1 ... Target value setting part 2 ... Action detection part 3 ... Environmental load generation amount measurement part 4 ... Action series memory | storage part 5 ... Action series extraction part 6 ... Selection probability calculation part 7 ... Average generation amount calculation part 8 ... Information presentation determination part DESCRIPTION OF SYMBOLS 9 ... Recommended action implementation rate memory | storage part 10 ... Recommended action selection part 11 ... Presentation information memory | storage part 12 ... Information presentation part 13 ... Database update part 14 ... Target value memory | storage part

Claims (7)

  1. Action sequence storage means for storing action sequence data including a plurality of different action sequences indicating the order of a plurality of different types of life actions, the number of executions of the action sequence, and an average amount of environmental load generated from the action sequence; ,
    A setting means for setting a target value of the environmental load generation amount;
    A detecting means for detecting a living behavior performed by the user;
    Extracting means for extracting from the plurality of action series a plurality of action series including a partial series equal to the ongoing action series indicating the order of the plurality of living actions including the current living action detected by the detecting means. When,
    The plurality of action sequences extracted by the extracting means are classified into a plurality of groups according to the type of living action next to the partial series in each action series, and the number of executions and the average of each action series belonging to each group A first calculation means for calculating an environmental load average generation amount for each group using the generation amount;
    A second calculating means for calculating a selection probability, which is a probability that the next living action of the partial series is selected for each group;
    When the average environmental load occurrence amount of the group including the living action having the highest selection probability among the plurality of groups is larger than the target value, the behavior belonging to another group having the average environmental load occurrence amount equal to or less than the target value Selection means for selecting the next living action of the partial series of the series as a recommended action;
    Presenting means for presenting a message for recommending the recommended action selected by the selecting means;
    A behavior improvement support apparatus comprising:
  2. For each of a plurality of different types of living behaviors, selected as a recommended behavior, calculated from the recommended number indicating the number of times selected as the recommended behavior and the number of times performed when the message is presented. An implementation rate storage means for storing an implementation rate indicating a high possibility of being implemented in the case of
    The selection means selects, as the recommended action, the living action having the highest implementation rate among the living actions next to the partial series of the action series belonging to the group whose average environmental load is equal to or less than the target value. The behavior improvement support apparatus according to claim 1.
  3.   Based on whether or not a new living action detected by the detecting means after presenting the message by the presenting means is the recommended action, the recommended number of times of the living action selected as the recommended action, the implementation The behavior improvement support apparatus according to claim 1, further comprising first update means for updating the number of times and the execution rate.
  4. A third calculation means for calculating an environmental load generation amount from at least one usage amount of electricity, gas, and water used during the progression of the behavior sequence from the starting lifestyle behavior to the ending lifestyle behavior;
    When the detecting means detects a living action at the end of the ongoing action sequence, the action sequence up to the living action at the end point and the occurrence of an environmental load calculated by the third calculating means for the action series A second further means for updating the action series data based on the quantity;
    The behavior improvement support apparatus according to claim 1, further comprising:
  5.   For each group, the second calculation means divides the sum of the number of executions of the action series belonging to the group by the sum of the number of executions of the plurality of action sequences extracted by the extraction means. The behavior improvement support apparatus according to claim 1, wherein a probability is calculated.
  6. Action sequence data including a plurality of different action sequences indicating the order of a plurality of different types of living activities, the number of times of execution of the action sequences, and the average amount of environmental load generated from the action sequences is stored in the first storage means And steps to
    A setting step for setting a target value for the amount of environmental load generated;
    A first detection step of detecting a living activity performed by the user;
    Extracting from the plurality of behavior sequences a plurality of behavior sequences including a partial sequence equal to an ongoing behavior sequence indicating the order of the plurality of life behaviors including the current living behavior detected in the first detection step. An extraction step to
    The plurality of action sequences extracted in the extraction step are classified into a plurality of groups according to the type of living action next to the partial series in each action series, and the number of executions and the average of each action series belonging to each group A first calculation step of calculating an environmental load average generation amount for each group using the generation amount;
    A second calculation step of calculating a selection probability, which is a probability that the next living action of the partial series is selected for each group;
    When the average environmental load occurrence amount of the group including the living action having the highest selection probability among the plurality of groups is larger than the target value, the behavior belonging to another group having the average environmental load occurrence amount equal to or less than the target value A selection step of selecting the next living action of the partial series of the series as a recommended action;
    A presenting step of presenting a message for recommending the recommended action selected in the selecting step;
    Behavior improvement support method including
  7. Computer
    Action sequence storage means for storing action sequence data including a plurality of different action sequences indicating the order of a plurality of different types of life actions, the number of executions of each action series, and the average amount of environmental load generated from each action series,
    Setting means for setting the target value of environmental load generation amount,
    Detecting means for detecting a living behavior performed by the user;
    Extracting means for extracting from the plurality of action series a plurality of action series including a partial series equal to the ongoing action series indicating the order of the plurality of living actions including the current living action detected by the detecting means. ,
    The plurality of action sequences extracted by the extracting means are classified into a plurality of groups according to the type of living action next to the partial series in each action series, and the number of executions and the average of each action series belonging to each group A first calculation means for calculating an environmental load average generation amount for each group using the generation amount;
    A second calculating means for calculating a selection probability, which is a probability that the next living action of the partial series is selected for each group;
    When the average environmental load occurrence amount of the group including the living action having the highest selection probability among the plurality of groups is larger than the target value, the behavior belonging to another group having the average environmental load occurrence amount equal to or less than the target value Selecting means for selecting the next living action of the partial series of the series as a recommended action;
    Presenting means for presenting a message for recommending the recommended action selected by the selecting means;
    Program to function as.
JP2007054680A 2007-03-05 2007-03-05 Behavior improvement support device, behavior improvement support method, and program Pending JP2008217478A (en)

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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2010170419A (en) * 2009-01-23 2010-08-05 Toshiba Corp Behavior time ratio calculation device
JP2011100382A (en) * 2009-11-09 2011-05-19 Fuji Xerox Co Ltd Information processing system and program
WO2019087851A1 (en) * 2017-11-01 2019-05-09 パナソニックIpマネジメント株式会社 Behavior inducement system, behavior inducement method and program

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2010170419A (en) * 2009-01-23 2010-08-05 Toshiba Corp Behavior time ratio calculation device
JP2011100382A (en) * 2009-11-09 2011-05-19 Fuji Xerox Co Ltd Information processing system and program
WO2019087851A1 (en) * 2017-11-01 2019-05-09 パナソニックIpマネジメント株式会社 Behavior inducement system, behavior inducement method and program

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