Patent Publication Number: US-10768591-B2

Title: Behavior identification device, air conditioner, and robot control device

Description:
TECHNICAL FIELD 
     The present invention relates to a behavior identification device that identifies a behavior of a target, an air conditioner using such a behavior identification device, and a robot control device using such a behavior identification device. 
     BACKGROUND ART 
     Conventional behavior identification devices use sensor values measured by various sensors incorporated in wearable terminals or portable terminals to identify behaviors of a target. For example, acceleration sensors, angular velocity sensors, heart rate sensors, and the like are used for the sensors. There has been conventionally proposed a behavior identification device in which a designer defines in advance component behaviors constituting a behavior and identifies the component behaviors using sensor values so as to identify the behavior. 
     For example, a behavior identification device described in Patent Document 1 identifies a component behavior using an identification device that is configured in advance for each component behavior and then identifies a behavior using a sequence of identification results of component behaviors. A behavior identification device described in Patent Document 2 identifies a component behavior that is selected depending on the performance of the device, resources, or the like so as to efficiently use an identification device that is configured in advance for each component behavior, and then identifies a behavior using combinations of evaluation values, which are identification results of component behaviors. As described above, the behavior identification devices described in Patent Documents 1 and 2 do not identify a behavior as a single behavior but identify a behavior as a combination of component behaviors constituting the behavior using a sequence of identification results of component behaviors or a combination of evaluation values. 
     PRIOR ART DOCUMENTS 
     Patent Documents 
     Patent Document 1: JP 2010-213782 A 
     Patent Document 2: JP 2011-156132 A 
     SUMMARY OF THE INVENTION 
     Problems to be Solved by the Invention 
     A conventional behavior identification device needs to specifically define component behaviors constituting a behavior in advance and to configure an identification device for each of the component behaviors. That is, in the conventional behavior identification device, the behavior, which is an identification target, is limited to a behavior in which component behaviors constituting the behavior are definable in advance. However, it is thought that, for example, behaviors in daily life of people are constituted by complicated combinations of component behaviors, and thus it is difficult for a designer to specifically define component behaviors constituting such a behavior in advance. 
     The present invention has been achieved to solve the above problems, and an object of the invention is to provide a behavior identification device that can identify various behaviors without specifically defining a component constituting a behavior in advance by a designer. 
     Means for Solving the Problems 
     A behavior identification device according to the present invention identifies a behavior of a target using a sensor value measured by a sensor for the behavior of the target. The behavior identification device includes a sensor-value obtaining unit that obtains a sensor value and calculates a sensor value distribution that is a distribution of the sensor value measured within a predetermined time, a component database that stores therein a set of basic distributions that are basic components constituting the sensor value distribution, a ratio calculating unit that calculates a first component ratio that is a ratio of each of the basic distributions included in the sensor value distribution, a component ratio database that stores therein a second component ratio determined in association with a behavior to be identified, and an identification unit that compares the first component ratio to the second component ratio to identify the behavior. The basic distribution is calculated as a sensor value distribution that is a base when each sensor value distribution is assumed to be a vector based on a set of the sensor value distributions obtained in advance for each of a plurality of types of the behavior. 
     Effects of the Invention 
     According to the behavior identification device of the present invention, the basic distribution stored in the component database is calculated as a sensor value distribution that is a base when each sensor value distribution is assumed to be a vector based on a set of the sensor value distributions obtained in advance for each of a plurality of types of the behavior. It is thus unnecessary for a designer to specifically define components constituting a behavior in advance. In addition, it is possible to identify a behavior that cannot be specifically defined by the designer. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  shows an example of a configuration of a behavior identification device according to a first embodiment of the present invention. 
         FIG. 2  shows an example of a sensor value measured by a sensor according to the first embodiment of the present invention. 
         FIGS. 3A and 3B  are diagrams for explaining an operation of a sensor-value obtaining unit in the behavior identification device according to the first embodiment of the present invention. 
         FIG. 4  is a diagram for explaining a data structure of a sensor-value distribution database in the behavior identification device according to the first embodiment of the present invention. 
         FIG. 5  shows a graphical model of a generation process assumed in an analysing unit in the behavior identification device according to the first embodiment of the present invention. 
         FIGS. 6A and 6B  are diagrams for explaining an operation of an evaluating unit in the behavior identification device according to the first embodiment of the present invention. 
         FIG. 7  is a diagram for explaining a data structure of a component database in the behavior identification device according to the first embodiment of the present invention. 
         FIG. 8  is a diagram for explaining a data structure of a component ratio database in the behavior identification device according to the first embodiment of the present invention. 
         FIGS. 9A, 9B, and 9C  are diagrams for explaining an operation of an identification unit in the behavior identification device according to the first embodiment of the present invention. 
         FIG. 10  is a flowchart of an operation of the behavior identification device according to the first embodiment of the present invention in a generation phase. 
         FIG. 11  is a flowchart of an operation of the behavior identification device according to the first embodiment of the present invention in an identification phase. 
         FIG. 12  shows an example of a hardware configuration of the behavior identification device according to the first embodiment of the present invention. 
         FIG. 13  shows another example of the hardware configuration of the behavior identification device according to the first embodiment of the present invention. 
         FIG. 14  shows an example of a configuration of a behavior identification device according to a second embodiment of the present invention. 
         FIG. 15  is a diagram for explaining a data structure of a sensor-value distribution database in the behavior identification device according to the second embodiment of the present invention. 
         FIG. 16  shows an example of a basic distribution calculated by a basic distribution generating unit in the behavior identification device according to the second embodiment of the present invention. 
         FIG. 17  shows an example of similarity calculated by a component integrating unit in the behavior identification device according to the second embodiment of the present invention. 
         FIG. 18  is a diagram for explaining an operation of the component integrating unit in the behavior identification device according to the second embodiment of the present invention. 
         FIG. 19  is a diagram for explaining a data structure of a mode corresponding database in the behavior identification device according to the second embodiment of the present invention. 
         FIG. 20  is a diagram for explaining a data structure of a component database in the behavior identification device according to the second embodiment of the present invention. 
         FIG. 21  is a diagram for explaining a data structure of a component ratio database in the behavior identification device according to the second embodiment of the present invention. 
         FIG. 22  is a flowchart of an operation of the behavior identification device according to the second embodiment of the present invention in a generation phase. 
         FIG. 23  is a flowchart of an operation of the behavior identification device according to the second embodiment of the present invention in an identification phase. 
         FIG. 24  shows an example of a configuration of a behavior identification device according to a third embodiment of the present invention. 
         FIG. 25  shows an example of a configuration of an action identification unit in the behavior identification device according to the third embodiment of the present invention. 
         FIG. 26  is a diagram for explaining a data structure of a combining rule database in the behavior identification device according to the third embodiment of the present invention. 
         FIG. 27  shows an example of a configuration of an air conditioner according to a fourth embodiment of the present invention. 
         FIG. 28  is a diagram for explaining a usage example of the air conditioner according to the fourth embodiment of the present invention. 
         FIG. 29  is a diagram for explaining a data structure of a component ratio database in the air conditioner according to the fourth embodiment of the present invention. 
         FIG. 30  is a diagram for explaining a data structure of a control rule database in the air conditioner according to the fourth embodiment of the present invention. 
         FIG. 31  shows an example of a configuration of a robot control device according to a fifth embodiment of the present invention. 
         FIG. 32  is a diagram for explaining a usage example of the robot control device according to the fifth embodiment of the present invention. 
         FIG. 33  is a diagram for explaining a data structure of a component ratio database in the robot control device according to the fifth embodiment of the present invention. 
         FIG. 34  is a diagram for explaining a data structure of a control rule database in the robot control device according to the fifth embodiment of the present invention. 
     
    
    
     EMBODIMENTS OF THE INVENTION 
     First Embodiment 
       FIG. 1  shows an example of a configuration of a behavior identification device  1  according to a first embodiment of the present invention. The behavior identification device  1  identifies a behavior of a target. For example, the behavior identification device  1  is incorporated in a wearable terminal or a portable terminal together with a sensor  2 , a display unit  3 , and an input unit  4 , and is attached to or carried by the target. “Behavior” collectively means the behavior, action, and posture of a person and the like, and examples of the behavior include “stopping”, “walking”, and “running”. 
     The overall configuration of the behavior identification device  1  is described with reference to FIG.  1 . The behavior identification device  1  includes a sensor-value obtaining unit  10 , a ratio calculating unit  21 , an identification unit  22 , a basic distribution generating unit  30 , a sensor-value distribution database  41 , a component database  42 , and a component ratio database  43 . In addition, the basic distribution generating unit  30  includes an analysing unit  31  and an evaluating unit  32 . The sensor  2 , the display unit  3 , and the input unit  4  are connected to the behavior identification device  1 . The behavior identification device  1  identifies a behavior using a sensor value measured by the sensor  2  and displays an identification result on the display unit  3 . 
     Before an operation of the behavior identification device  1  is described, the sensor  2  is described. In the present embodiment, the sensor  2  is a three-axis acceleration sensor that is attached to, for example, the waist of a target, and measures three-axis acceleration values ax, ay, and az for a behavior of the target. In addition, the sensor  2  calculates a norm |a| of the three-axis acceleration values ax, ay, and az by formula (1), and outputs the norm as a sensor value every 50 milliseconds.
 
[Formula 1]
 
| a |=√{square root over ( ax   2   +ay   2   +az   2 )}  (1)
 
       FIG. 2  shows an example of a sensor value measured by the sensor  2  according to the present embodiment. In  FIG. 2 , the vertical axis indicates an acceleration norm, which is a sensor value, and the horizontal axis indicates a sensor-value obtaining time. While the first embodiment describes a case where a three-axis acceleration sensor is used as the sensor  2  and is attached to the waist of a target, the present invention is not limited thereto, and any sensor that can measure a sensor value corresponding to a behavior of the target can be used. In addition to the three-axis acceleration sensor, for example, an angular velocity sensor, a position sensor, an air pressure sensor, and a heart rate sensor can be used. 
     Next, the operation of the behavior identification device  1  is described. The behavior identification device  1  operates in two phases, that is, a generation phase and an identification phase. In  FIG. 1 , broken line arrows indicate the relationships between blocks in the generation phase, whereas solid line arrows indicate the relationships between the blocks in the identification phase. An operation of the behavior identification device  1  in the generation phase is described first. In the generation phase, the sensor-value obtaining unit  10  obtains a sensor value from the sensor  2  and calculates a sensor value distribution h d  that is a distribution of sensor values measured by the sensor  2  within a predetermined time. d denotes a data number for identifying each of a plurality of sensor value distributions. 
       FIGS. 3A and 3B  are diagrams for explaining an operation of the sensor-value obtaining unit  10  in the behavior identification device  1  according to the present embodiment.  FIG. 3A  is a diagram in which sensor values output from the sensor  2  are divided for each predetermined time. In  FIG. 3A , the vertical axis indicates an acceleration norm, which is a sensor value, and the horizontal axis indicates a sensor-value obtaining time. The predetermined time is defined as five seconds in the present embodiment. The sensor-value obtaining unit  10  quantizes the norm of three-axis acceleration values, which is a sensor value output from the sensor  2 , every 0.04 g from 0.04 g to 2.00 g and calculates a histogram (a frequency distribution of occurrence) of sensor values within a predetermined time. The generated histogram is thus a sensor value distribution. That is, each sensor value distribution h d  is a histogram in which a quantized sensor value is set as a class (a bin). 
     The sensor  2  obtains a sensor value every 50 milliseconds in the present embodiment, and thus 100 sensor values are obtained within a predetermined time, that is, five seconds.  FIG. 3B  shows an example of a distribution of obtained sensor values. In  FIG. 3B , the horizontal axis indicates a sensor value and the vertical axis indicates the frequency of occurrence (the measurement frequency) of a sensor value. In  FIG. 3B , a histogram on the left side is a distribution of sensor values obtained from 0 to 5 seconds, whereas a histogram on the right side is a distribution of sensor values obtained from 5 to 10 seconds. 
     Next, the sensor-value distribution database  41  is described. The sensor-value distribution database  41  stores therein a set of sensor value distributions h d  generated by the sensor-value obtaining unit  10 .  FIG. 4  is a diagram for explaining a data structure of the sensor-value distribution database  41  in the behavior identification device  1  according to the present embodiment. In  FIG. 4 , the sensor-value distribution database  41  stores therein D sensor value distributions. As described above, a data number d is given to each of the sensor value distributions. The sensor value distribution is a histogram and thus is represented as a set of frequencies of occurrence (measurement frequencies) of each sensor value. That is, h d ={h d (1), h d (2), h d (3), . . . , h d (V)}. h d (v) denotes the frequency of occurrence (the measurement frequency) of a sensor value corresponding to the vth class in the dth sensor value distribution. V denotes the number of classes (bins) of a histogram. When quantization is performed every 0.04 g from 0.04 g to 2.00 g, V=50. 
     Next, the basic distribution generating unit  30  is described. The basic distribution generating unit  30  includes the analysing unit  31  and the evaluating unit  32 . The analysing unit  31  is described first. The analysing unit  31  estimates a basic distribution and a component ratio based on a set of sensor value distributions stored in the sensor-value distribution database  41 . The basic distribution is a distribution of a basic component that constitutes a sensor value distribution. The component ratio is a ratio of each basic distribution included in a sensor value distribution. 
     In the first embodiment, the analysing unit  31  estimates a basic distribution φ j  constituting a sensor value distribution and a component ratio θ d,j  that is a ratio of each basic distribution included in a sensor value distribution using Latent Dirichlet Allocation (LDA). j denotes a basic distribution number, which is an integer from 1 to T. T denotes the number of basic distributions constituting a sensor value distribution. As described above, d denotes the data number of a sensor value distribution stored in the sensor-value distribution database  41 , which is an integer from 1 to D. As described above, D denotes the number of sensor value distributions stored in the sensor-value distribution database  41 . That is, the component ratio θ d,j  indicates a ratio of the jth basic distribution included in the dth sensor value distribution. A set of component ratios in the dth sensor value distribution is denoted by θ d . That is, θ d ={θ d,1 , θ d,2 , . . . , θ d,T }. 
     In the present embodiment, the analysing unit  31  performs a process assuming that a sensor value is generated by a predetermined modeled generation process. The analysing unit  31  assumes that the basic distribution φ j  is a probability distribution when a sensor value is generated, and estimates the basic distribution φ j  for generating a set of measured sensor values. In the present embodiment, the generation process for generating a set of sensor values is assumed by formulae (2) to (5) wherein Dir denotes a Dirichlet distribution and Mult denotes a multinomial distribution. The basic distribution φ j  is represented as a set of frequencies of generation of each sensor value. That is, φ j ={φ j (1), φ j (2), φ j (3), , φ j (V)}. φ j  (v) denotes the frequency of generation of a sensor value corresponding to the vth class in the jth basic distribution. V denotes the number of classes (bins) of a histogram.
 
[Formula 2]
 
θ d ˜Dir(α)  (2)
 
[Formula 3]
 
ϕ j ˜Dir(β)  (3)
 
[Formula 4]
 
 z   d,i ˜Mult(θ d )  (4)
 
[Formula 5]
 
 w   d,i ˜Mult(ϕ z     d,i   )  (5)
 
     In formula (2), α denotes a parameter for a Dirichlet distribution that generates a set θ d  of component ratios. In formula (3), β denotes a parameter for a Dirichlet distribution that generates the basic distribution φ j . In formulae (4) and (5), i denotes the number of a sensor value included in each sensor value distribution, which is an integer from 1 to N. N denotes the number of sensor values measured within a predetermined time during which a sensor value distribution is calculated, and N=100 in the present embodiment. Each of the sensor value distribution represents a distribution of N sensor values. i denotes what number a sensor value is among N sensor values. The sensor value number i may be different from a number in the time serial order of sensor values measured for a behavior. For example, the sensor value number i may be a number in ascending order. 
     In formula (5), w d,i  denotes the ith sensor value included in the dth sensor value distribution h d  stored in the sensor-value distribution database  41 . In formula (4), z d,i  denotes a value indicating by which basic distribution w d,i  is generated. In the first embodiment, the number T of basic distributions and the parameters α and β are included in predetermined estimation conditions determined in advance by a designer of the behavior identification device  1 .  FIG. 5  shows a graphical model of a generation process assumed in the analysing unit  31  in the behavior identification device  1  according to the present embodiment. In  FIG. 5 , arrows indicate which data is generated by which data, and T, N, and D denote the number of times that data is generated. 
     The shape of each sensor value distribution approximates the shape of a mixture distribution of T basic distributions φ j  estimated by the analysing unit  31  and in the dth sensor value distribution, the mixing ratio of the basic distribution φ j  is the component ratio θ d,j . The sensor value distribution h d  can thus be ideally represented by the following formula (6). From a different point of view, assuming that the sensor value distribution h d  is a vector including the frequency of occurrence of each sensor value as its element, T basic distributions φ j  are basis vectors, and the sensor value distribution is approximated by multiplying a linear sum of T basic distributions φ j , whose coefficient is the component ratio θ d,j , by a proportional coefficient. The proportional coefficient is the number N of sensor values constituting the sensor value distribution h d . That is, T basic distributions φ j  estimated by the analysing unit  31  are components constituting the sensor value distribution h d  stored in the sensor-value distribution database  41 . Additionally, the component ratio θ d,j  estimated by the analysing unit  31  indicates the constituent ratio (the mixing ratio) of the components. Assuming that the sensor value distribution h d  and the basic distribution φ j  are vectors, V, which indicates the number of classes (bins) of a histogram, denotes the number of dimensions of a vector. 
     
       
         
           
             
               
                 
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     In a behavior such as “stopping”, “walking”, or “running”, a component behavior constituting the behavior is regarded as an operation of each part of a body. However, it is difficult to specifically define in advance such an operation of each part of a body as the component behavior and to configure an identification device for each component behavior. In contrast, the behavior identification device  1  according to the present embodiment obtains a sensor value distribution in advance for each of various behaviors and calculates the basis for a set of obtained sensor value distributions. In this way, a component constituting a behavior can be calculated without being defined by a designer. 
     The basic distribution φ j  and the component ratio θ d,j  can be estimated based on an LDA generation process by a repetitive process such as variational Bayes or Gibbs sampling (for example, David M. Blei, Andrew Y. Ng, and Michael I. Jordan, “Latent Dirichlet allocation”, Journal of Machine Learning Research, vol. 3, pp. 993-1022, 2003, and Thomas L. Griffiths and Mark Steyvers, “Finding scientific topics”, in Proceedings of the National Academy of Sciences of the United States of America, vol. 101, pp. 5228-5235, 2004). Detailed descriptions of these processes are omitted. 
     The parameters α and β can be automatically estimated by Minka&#39;s fixed-point iteration (Thomas P. Minka, “Estimating a Dirichlet distribution”, Technical report, Massachusetts Institute of Technology, vol. 2000, pp. 1-13, 2000.). The analysing unit  31  operates as described above. 
     Next, the evaluating unit  32  is described. The evaluating unit  32  evaluates a basic distribution and a component ratio that are estimated by the analysing unit  31 . If an evaluation result does not satisfy predetermined evaluation criteria, the analysing unit  31  changes predetermined estimation conditions and then estimates again the basic distribution and the component ratio. In the present embodiment, for a set of sensor value distributions stored in the sensor-value distribution database  41 , the evaluating unit  32  calculates an average of component ratios θ d,j  for each basic distribution φ j , and sets the calculated average as the evaluation criteria.  FIGS. 6A and 6B  are diagrams for explaining an operation of the evaluating unit  32  in the behavior identification device  1  according to the present embodiment. In  FIGS. 6A and 6B , the horizontal axis indicates a basic distribution number j and the vertical axis indicates a component ratio.  FIG. 6A  shows an example of a set θ d  of component ratios estimated by the analysing unit  31  for each sensor value distribution. The set θ d  of component ratios is constituted by the component ratio θ d,j  of each basic distribution in each sensor value distribution. The evaluating unit  32  calculates first an average component ratio θa j , which is an average of the component ratios θ d,j  for each basic distribution by the following formula (7). 
     
       
         
           
             
               
                 
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       FIG. 6B  shows an example of θa, which is a set of calculated average component ratios θa j . The average component ratio indicates in what ratio each basic distribution is included, on average, in a set of sensor value distributions stored in the sensor-value distribution database  41 . A basic distribution having an extremely small average component ratio is hardly included in sensor value distributions stored in the sensor-value distribution database  41 . That is, the number T of basic distributions set in advance might be too large. To perform such an evaluation, the evaluating unit  32  compares each average component ratio θa j  to a predetermined threshold th. 
     If all the average component ratios θa j  are larger than or equal to the threshold th, the evaluating unit  32  stores a basic distribution in the component database  42  and a component ratio in the component ratio database  43 . On the other hand, if one of the average component ratios θa j  is less than the threshold th, the evaluating unit  32  subtracts the number T of basic distributions set in advance by 1. The analysing unit  31  then estimates again a basic distribution and a component ratio based on a set of sensor value distributions stored in the sensor-value distribution database  41  by using the updated number T of basic distributions as a new condition. According to the example of  FIG. 6B , in a set θa of average component ratios, a ratio θa 4  of the fourth basic distribution is less than the threshold th. Consequently, the analysing unit  31  estimates again a basic distribution and a component ratio under conditions that the number T of basic distributions is subtracted by 1. 
     The evaluation criteria of the evaluating unit  32  is not limited to the criteria described above. Alternatively, a sensor value distribution may be reproduced using a basic distribution and a component ratio that are estimated by the analysing unit  31 , the reproduced sensor value distribution may be compared to a sensor value distribution stored in the sensor-value distribution database  41 , and differences between these sensor value distributions may be used as the evaluation criteria. The evaluating unit  32  operates as described above. Next, the component database  42  is described. The component database  42  stores therein a basic distribution φ j  calculated by the basic distribution generating unit  30 .  FIG. 7  is a diagram for explaining a data structure of the component database  42  in the behavior identification device  1  according to the present embodiment. The component database  42  stores therein the basic distribution φ j  in association with the basic distribution number j. 
     Next, the component ratio database  43  and the input unit  4  are described. The component ratio database  43  stores therein a set θ d  of component ratios calculated by the basic distribution generating unit  30  for each sensor value distribution. In addition, the component ratio database  43  stores therein a behavior label of a target when each sensor value distribution is measured in association with a component ratio.  FIG. 8  is a diagram for explaining a data structure of the component ratio database  43  in the behavior identification device  1  according to the present embodiment. The component ratio database  43  stores therein a set θ d  of component ratios and a behavior label in association with the data number d of a sensor value distribution. Each component ratio θ d,j  constituting the set θ d  of component ratios is stored in association with the basic distribution number j. 
     In  FIG. 8 , for a first sensor value distribution stored in the sensor-value distribution database  41 , for example, the component ratio database  43  stores therein the behavior label “stopping” together with a set θ 1  of component ratios included in the first sensor value distribution. The behavior label is input by the input unit  4 . The input unit  4  is configured by a device that can externally input information, such as a keyboard, a touch panel, or a memory card read device. The behavior identification device  1  according to the present embodiment generates a basic distribution φ j  and a set θ d  of component ratios based on a sensor value distribution calculated for a behavior of a target in a generation phase. However, the operation of the behavior identification device  1  is not limited thereto. For example, the behavior identification device  1  may generate a basic distribution φ j  and a set θ d  of component ratios based on a sensor value distribution calculated in advance for a behavior of a person, who is not a target. The behavior identification device  1  according to the present embodiment operates in the generation phase as described above. 
     Next, an operation of the behavior identification device  1  in an identification phase is described. The sensor-value obtaining unit  10  is described first. The sensor-value obtaining unit  10  obtains a sensor value from the sensor  2  and calculates a sensor value distribution h2 in the same manner as in the generation phase. The sensor value distribution h2 is a histogram and thus is represented as a set of measurement frequencies of each sensor value. That is, h2={h2(1), h2(2), h2(3), . . . , h2(V)}. h2(v) denotes the measurement frequency of a sensor value corresponding to the vth class in the sensor value distribution h2. Next, the ratio calculating unit  21  is described. For the sensor value distribution h2 calculated in the identification phase, the ratio calculating unit  21  calculates a component ratio θ2 j , which is a ratio of each basic distribution included in the sensor value distribution h2, using the basic distribution φ j  stored in the component database  42 . As described above, j denotes a basic distribution number. That is, the component ratio θ2 j  indicates a ratio of the jth basic distribution included in the sensor value distribution h2. A set of component ratios in the sensor value distribution h2 is denoted by θ2. That is, θ2={θ2 1 , θ2 2 , . . . , θ2 T }. Specifically, the ratio calculating unit  21  calculates the set θ2 of component ratios of the basic distribution φ j  included in the sensor value distribution h2 using the EM algorithm. The EM algorithm estimates parameters of a probability model based on the maximum likelihood method. 
     It is assumed that a sensor value that constitutes the sensor value distribution h2 calculated by the sensor-value obtaining unit  10  is denoted by w2 i . As described above, i denotes the number of a sensor value included in the sensor value distribution h2, which is an integer from 1 to N. The ratio calculating unit  21  performs the following first procedure and then alternately repeats a second procedure and a third procedure for predetermined times based on the EM algorithm. In the first procedure, the ratio calculating unit  21  sets an initial value of the component ratio θ2 j  included in a sensor value distribution. Next, in the second procedure, the ratio calculating unit  21  calculates a probability ψ i,j  that the ith sensor value w2 i  included in the sensor value distribution h2 is generated by the jth basic distribution φ j  using the following formula (8). In formula (8), v2 i  indicates which class in a histogram the ith sensor value w2 i  belongs to. Next, in the third procedure, the ratio calculating unit  21  calculates the component ratio θ2 j  using the following formula (9). The second procedure is referred to as M-step and the third procedure is referred to as E-step. The ratio calculating unit  21  operates as described above. 
     
       
         
           
             
               
                 
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     Next, the identification unit  22  is described. The identification unit  22  calculates the similarity between the set θ2 of component ratios calculated by the ratio calculating unit  21  and a set θ d  of component ratios for each sensor value distribution stored in the component ratio database  43 . The identification unit  22  then obtains, as an identification result, a behavior label corresponding to a set of component ratios having the highest similarity among sets θ d  of component ratios stored in the component ratio database  43 .  FIGS. 9A, 9B, and 9C  are diagrams for explaining an operation of the identification unit  22  in the behavior identification device  1  according to the present embodiment. The operation of the identification unit  22  is specifically described with reference to  FIGS. 9A, 9B, and 9C .  FIG. 9A  shows an example of the set θ2 of component ratios calculated by the ratio calculating unit  21 .  FIG. 9B  shows an example of the set θ d  of component ratios stored in the component ratio database  43 . 
     The identification unit  22  calculates first the similarity between the set θ2 of component ratios calculated by the ratio calculating unit  21  and the set θ d  of component ratios for each sensor value distribution stored in the component ratio database  43 . The identification unit  22  calculates the similarity between component ratios using Histogram Intersection. Histogram Intersection is an index indicating the similarity between two histograms, and becomes larger as the similarity becomes higher. Additionally, Histogram Intersection has a maximum value of 1 and a minimum value of 0. The identification unit  22  calculates D similarities HI d  by formula (10). In formula (10), min(A1, A2) means a calculation of the minimum value of A1 and A2.  FIG. 9C  shows an example of a calculated similarity HI d . 
     
       
         
           
             
               
                 
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     As described above, the component ratio database  43  stores therein a set θ d  of component ratios and a behavior label in association with a sensor value distribution number d. The identification unit  22  searches for a component ratio having the highest similarity HI d  to the set θ2 of component ratios among D sets θ d  of component ratios stored in the component ratio database  43 . The identification unit  22  then outputs a behavior label corresponding to the searched set of component ratios as an identification result. In the example shown in  FIG. 9C , the similarity to the second set θ 2  of component ratios stored in the component ratio database  43  is 0.959, which is the maximum value, and thus the identification unit  22  identifies the label “walking” corresponding to the second set θ 2  of component ratios. The identification unit  22  outputs the identification result to the display unit  3  that is externally provided to the behavior identification device  1 . The display unit  3  is an image device that displays identification results, such as a liquid crystal display. Instead of the display unit  3 , a storage device that stores therein identification results or a communication device that transmits identification results may be provided. The behavior identification device  1  according to the present embodiment operates as described above. 
     The operation of the behavior identification device  1  according to the present embodiment is further described with reference to a flowchart. The behavior identification device  1  operates in a generation phase in advance and then operates in an identification phase. If the behavior identification device  1  has already operated in the generation phase, the behavior identification device  1  may successively operate in the identification phase. An operation of the behavior identification device  1  in the generation phase is described first.  FIG. 10  is a flowchart of the operation of the behavior identification device  1  according to the present embodiment in the generation phase. In the generation phase, the sensor-value obtaining unit  10  obtains first a sensor value from the sensor  2  and calculates a sensor value distribution (Step S 101 ). Next, the sensor-value distribution database  41  stores therein the sensor value distribution calculated at Step S 101  (Step S 102 ). 
     Next, the analysing unit  31  estimates a basic distribution that is a component constituting a sensor value distribution and a component ratio that is a ratio of each basic distribution included in a sensor value distribution (Step S 103 ). At Step S 103 , the analysing unit  31  estimates a basic distribution and a component ratio based on the sensor value distribution stored at Step S 102 . Next, the evaluating unit  32  evaluates the basic distribution and the component ratio estimated at Step S 103  (Step S 104 ). At Step S 104 , the evaluating unit  32  determines whether the component ratio calculated for each basic distribution satisfies predetermined evaluation criteria to evaluate the basic distribution and the component ratio. If the component ratio does not satisfy the evaluation criteria, the operation of the behavior identification device  1  proceeds to Step S 105 . At Step S 105 , the evaluating unit  32  changes an estimation condition used at Step S 103 . When Step S 105  ends, the operation of the behavior identification device  1  returns to Step S 103 . 
     On the other hand, if the component ratio satisfies the evaluation criteria, the operation of the behavior identification device  1  proceeds to Step S 106 . At Step S 106 , the component database  42  stores therein the basic distribution in association with a basic distribution number, whereas the component ratio database  43  stores therein the component ratio in association with a data number of the sensor value distribution. At Step S 106 , the component database  42  stores therein the basic distribution and the component ratio database  43  stores therein the component ratio. Next, the component ratio database  43  stores therein a behavior label of a target when the sensor value distribution is calculated in association with the data number of the sensor value distribution (Step S 107 ). As a result, the component ratio database  43  stores therein the component ratio in association with the behavior label. The behavior identification device  1  may perform the operation at Step S 107  at any time after Step S 102 . When the operation at Step S 107  ends, the operation of the behavior identification device  1  in the generation phase ends. 
     Next, an operation of the behavior identification device  1  in the identification phase is described.  FIG. 11  is a flowchart of the operation of the behavior identification device  1  according to the present embodiment in the identification phase. In the identification phase, the sensor-value obtaining unit  10  obtains first a sensor value from the sensor  2  and calculates a sensor value distribution (Step S 201 ). Next, the ratio calculating unit  21  calculates a component ratio in the sensor value distribution calculated at Step S 201  using a basic distribution stored in the component database  42  (Step S 202 ). The component ratio calculated at Step S 202  is a ratio of each basic distribution included in the sensor value distribution. 
     Next, the identification unit  22  calculates the similarity between a set of component ratio calculated at Step S 202  and each set of component ratios stored at Step S 106  in the generation phase (Step S 203 ). Next, the identification unit  22  selects a set of component ratio having the highest similarity among the sets of component ratios stored at Step S 106  in the generation phase (Step S 204 ). The identification unit  22  then outputs a behavior label corresponding to the set of component ratios selected at Step S 204  as an identification result (Step S 205 ). When the operation at Step S 205  ends, the operation of the behavior identification device  1  in the identification phase ends. The behavior identification device  1  operates as described above. 
     Next, the hardware configuration that achieves the behavior identification device  1  according to the present embodiment is described. Functions of the sensor-value obtaining unit  10 , the ratio calculating unit  21 , the identification unit  22 , and the basic distribution generating unit  30  in the behavior identification device  1  are achieved by processing circuits. The processing circuit may be dedicated hardware or may be a CPU that executes programs stored in memories (Central Processing Unit, which is also referred to as a processing unit, a computing unit, a microprocessor, a microcomputer, a processor, or a DSP). Additionally, functions of the sensor-value distribution database  41 , the component database  42 , and the component ratio database  43  are achieved by memories. 
     When the processing circuit is dedicated hardware, examples of the processing circuit include a single circuit, a composite circuit, a programmed processor, a parallel-programmed processor, an ASIC, an FPGA, and any combination thereof. The functions of the sensor-value obtaining unit  10 , the ratio calculating unit  21 , the identification unit  22 , and the basic distribution generating unit  30  may be achieved by processing circuits, respectively, or may be achieved by a single processing circuit. 
     When the processing circuit is a CPU, the functions of the sensor-value obtaining unit  10 , the ratio calculating unit  21 , the identification unit  22 , and the basic distribution generating unit  30  are achieved by software, firmware, or a combination of software and firmware. The software and the firmware are described as programs and stored in memories. The processing circuit achieves the functions of units by reading the programs stored in the memories and executing the programs. These programs are for causing a computer to perform procedures or methods of operating the sensor-value obtaining unit  10 , the ratio calculating unit  21 , the identification unit  22 , and the basic distribution generating unit  30 . Examples of the memory include a non-volatile or volatile semiconductor memory such as a RAM, a ROM, a flash memory, an EPROM, and an EEPROM, a magnetic disk, a flexible disk, an optical disk, a compact disk, a mini disk, and a DVD. 
     A part of the functions of the sensor-value obtaining unit  10 , the ratio calculating unit  21 , the identification unit  22 , and the basic distribution generating unit  30  may be achieved by dedicated hardware and another part thereof may be achieved by software or firmware. For example, a processing circuit, which is dedicated hardware, may achieve the function of the sensor-value obtaining unit  10 , and a processing circuit may achieve the functions of the ratio calculating unit  21 , the identification unit  22 , and the basic distribution generating unit  30  by reading programs stored in memories and executing the programs. 
       FIG. 12  shows an example of the hardware configuration of the behavior identification device  1  according to the present embodiment.  FIG. 12  shows an example in a case where a processing circuit  1001  is dedicated hardware. In the example of  FIG. 12 , the processing circuit  1001  achieves functions of the sensor-value obtaining unit  10 , the ratio calculating unit  21 , the identification unit  22 , and the basic distribution generating unit  30 . A memory  1002  achieves functions of the sensor-value distribution database  41 , the component database  42 , and the component ratio database  43 . The processing circuit  1001  is connected via a data bus  1003  to the memory  1002 . 
       FIG. 13  shows another example of the hardware configuration of the behavior identification device  1  according to the present embodiment.  FIG. 13  shows an example of the hardware configuration in a case where a processing circuit is a CPU. In the example of  FIG. 13 , functions of the sensor-value obtaining unit  10 , the ratio calculating unit  21 , the identification unit  22 , and the basic distribution generating unit  30  are achieved by a processor  1004  executing programs stored in a memory  1005 . Functions of the sensor-value distribution database  41 , the component database  42 , and the component ratio database  43  are achieved by the memory  1002 . The processor  1004  is connected via the data bus  1003  to the memory  1002  and the memory  1005 . Behavior identification devices according to subsequent embodiments can be achieved by the same hardware configuration as in the behavior identification device  1  according to the present embodiment. 
     As described above, the behavior identification device  1  according to the present embodiment can calculate a basic distribution constituting a sensor value distribution based on a sensor value distribution obtained for a behavior such as “stopping”, “walking”, or “running”. Consequently, it is possible to identify a behavior more flexibly as a combination of component constituting the behavior without defining components constituting the behavior by a designer. 
     In addition, the behavior identification device  1  according to the present embodiment can identify more flexibly even a behavior that cannot be specifically defined by a designer as a combination of components constituting the behavior. For example, it is difficult for a designer to define a component constituting a behavior that is vaguely defined by the designer, such as “behavior indicating that a person seems to be in a hurry”. That is to say, the behavior that is vaguely defined is a behavior that is defined conceptually. However, the behavior identification device according to the first embodiment can identify the behavior that is vaguely defined if a behavior label stored in the component ratio database  43  is associated with the behavior that is vaguely defined. 
     While “behavior” collectively means the behavior, action, and posture of a target and the like in the present embodiment, the behavior is not limited thereto. The present invention can also be applied to an operation of a target other than a human being (for example, an action of an animal or an operation of a machine with unknown control contents). For the operation of a target other than a human being, in most cases, it is difficult for a designer of a behavior identification device to define a component constituting a behavior, and thus it is effective to apply the present invention to such a case. 
     Second Embodiment 
     There are some modes in a behavior in a target&#39;s daily life. The mode collectively means a scene that affects a behavior of a target, a physical state, and the like. That is to say, the mode is a condition that affects a behavior of a target. The mode is thus a clue for identifying a behavior of a target. Examples of the mode include “at home”, “in factory”, “in park”, “good physical condition”, and “bad physical condition”. If the mode of a behavior of a target differs, it is thought that a set of basic distributions constituting a sensor value distribution differs. For example, even in the same behavior “walking”, the mode “at home” and the mode “in factory” have different sets of basic distributions constituting a sensor value distribution. 
       FIG. 14  shows an example of a configuration of a behavior identification device  1   b  according to a second embodiment of the present invention. The behavior identification device  1   b  according to the present embodiment is different from the behavior identification device  1  according to the first embodiment in that the behavior identification device  1   b  detects the mode of a target to calculate a set of basic distributions that is appropriate for each mode. As a result, the behavior identification device  1   b  according to the present embodiment can achieve behavior identification with higher accuracy. The difference between the behavior identification device  1   b  according to the present embodiment and the behavior identification device  1  according to the first embodiment is mainly described below. 
     Similarly to  FIG. 1 , in  FIG. 14 , broken line arrows indicate the relationships between blocks in a generation phase, whereas solid line arrows indicate the relationships between the blocks in an identification phase. Comparing to the behavior identification device  1  according to the first embodiment, the behavior identification device  1   b  according to the present embodiment newly includes a mode corresponding database  44 , a component integrating unit  61 , a component select unit  62 , and a ratio select unit  63 . The behavior identification device  1   b  according to the present embodiment is different from the behavior identification device  1  according to the first embodiment in a part of an operation of a basic distribution generating unit  30   b . In addition, the data structure of a sensor-value distribution database  41   b  and a component ratio database  43   b  in the behavior identification device  1   b  according to the present embodiment is different from that in the behavior identification device  1  according to the first embodiment. A mode detecting unit  5  is connected to the behavior identification device  1   b.    
     The mode detecting unit  5  is described first. In the present embodiment, the mode detecting unit  5  detects a mode of a target. The mode detecting unit  5  detects, as a mode, a place where the target is present such as “at home” or “in factory”. The mode detecting unit  5  is configured by, for example, a device that is capable of detecting an absolute position on the earth such as GPS (Global Positioning System), a positioning system in which radio waves from a transmitter are received by a plurality of receivers and a position is detected using the radio wave arrival time, the intensity of an electric field, and the like, or a distance measuring system in which the distance from a transmitter is estimated by receiving radio waves from the transmitter. As another example, the mode detecting unit  5  may obtain physical information such as heart rates, body temperature, brain waves, or blood oxygen saturation levels to detect, as the mode of a target, “good physical condition” and “bad physical condition”. As yet another example, the mode detecting unit  5  may receive a vertical acceleration value measured by the sensor  2  as an input and detect, as a mode, the posture of a target such as “lying” and “standing”. 
     Next, the behavior identification device  1   b  according to the present embodiment is described. The sensor-value distribution database  41   b  is described first. The sensor-value distribution database  41   b  stores therein a set of sensor value distributions calculated by a sensor-value obtaining unit  10  in a generation mode for each mode of a target detected by the mode detecting unit  5 .  FIG. 15  is a diagram for explaining a data structure of the sensor-value distribution database  41   b  in the behavior identification device  1   b  according to the present embodiment. In the behavior identification device  1   b  according to the present embodiment, a mode A is an “at home” mode and a mode B is an “in factory” mode. In the example of  FIG. 15 , the sensor-value distribution database  41   b  stores therein a set of Da sensor value distributions h d  for a mode A and a set of Db sensor value distributions h d  for a mode B. 
     Next, the basic distribution generating unit  30   b  is described. The basic distribution generating unit  30   b  calculates a basic distribution and a component ratio for each mode based on a set of sensor value distributions stored for each mode. Except that the basic distribution and the component ratio are calculated for each mode, the basic distribution generating unit  30   b  operates in the same manner as in the behavior identification device  1  according to the first embodiment. Next, the component integrating unit  61  is described. The component integrating unit  61  successively selects a basic distribution of each mode from basic distributions calculated for different modes and generates a combination of basic distributions. The component integrating unit  61  compares combined basic distributions in all combinations, determines that basic distributions satisfying a predetermined comparison condition are identical, and integrates these basic distributions. An operation of the component integrating unit  61  is specifically described with reference to the drawings. 
       FIG. 16  shows an example of a basic distribution calculated by the basic distribution generating unit  30   b  in the behavior identification device  1   b  according to the present embodiment. The basic distribution generating unit  30   b  calculates five basic distribution φa 1 , φa 2 , φa 3 , φa 4 , and φa 5  in the mode A, and five basic distribution φb 1 , φb 2 , φb 3 , φb 4 , and φb 5  in the mode B. While the number of basic distributions calculated in the mode A is equal to the number of basic distributions calculated in the mode B in the example of  FIG. 16 , these numbers do not always need to be equal to each other. The component integrating unit  61  successively combines one of the basic distributions calculated in the mode A with one of the basic distributions calculated in the mode B. In addition, the component integrating unit  61  compares combined basic distributions in all the combinations. The component integrating unit  61  compares basic distributions by using the similarity employing Histogram Intersection as an index. 
       FIG. 17  shows an example of similarity calculated by the component integrating unit  61  in the behavior identification device  1   b  according to the present embodiment.  FIG. 17  shows the similarity between basic distributions shown in  FIG. 16 , which is calculated by the component integrating unit  61 . When the calculated similarity exceeds 0.7, the component integrating unit  61  determines that combined basic distributions are identical and integrates these basic distributions.  FIG. 18  is a diagram for explaining an operation of the component integrating unit  61  in the behavior identification device  1   b  according to the present embodiment. In  FIG. 17 , since the basic distribution pair with similarity exceeding 0.7 is a φa 1  and φb 2  pair, a φa 2  and φb 3  pair, and a φa 4  and φb 4  pair, the component integrating unit  61  integrates basic distributions of each pair, so that new basic distributions φc 1 , φc 2 , and φc 3  are obtained. 
     The component integrating unit  61  integrates basic distributions by averaging frequencies in the same class. When assuming that the basic distribution is a vector, the component integrating unit  61  integrates basic distributions by averaging elements of vectors. When basic distributions combined by the component integrating unit  61  are denoted by φp and φq, an integrated basic distribution φr is represented by the following formula (11). In formula (11), φp(v) denotes the frequency of a sensor value corresponding to the vth class in the basic distribution φp. φq(v) denotes the frequency of a sensor value corresponding to the vth class in the basic distribution φq. φr(v) denotes the frequency of a sensor value corresponding to the vth class in the basic distribution φr.
 
[Formula 11]
 
ϕ r ( v )=(ϕ p ( v )+ϕ q ( v ))/2  (11)
 
     Next, the mode corresponding database  44  is described. The mode corresponding database  44  stores therein a basic distribution corresponding to each mode.  FIG. 19  is a diagram for explaining a data structure of the mode corresponding database  44  in the behavior identification device  1   b  according to the present embodiment. The mode corresponding database  44  stores therein, for each mode, a basic distribution number j used for each mode and information for identifying a basic distribution corresponding to a basic distribution number j among basic distributions stored in the component database  42 . For example, the mode corresponding database  44  uses a distribution name as the information for identifying a basic distribution. The component database  42  stores therein a basic distribution that is not integrated by the component integrating unit  61  and a basic distribution obtained by integration.  FIG. 20  is a diagram for explaining a data structure of the component database  42  in the behavior identification device  1   b  according to the present embodiment. The component database  42  stores therein a basic distribution in association with a distribution name. 
     Next, the component ratio database  43   b  is described. The component ratio database  43   b  stores therein a set θ d  of component ratios calculated by the basic distribution generating unit  30   b  for each mode. In addition, the component ratio database  43   b  stores therein a behavior label of a target when a sensor value distribution stored in the sensor-value distribution database  41   b  is calculated in association with a set of component ratios.  FIG. 21  is a diagram for explaining a data structure of the component ratio database  43   b  in the behavior identification device  1   b  according to the present embodiment. The component ratio database  43   b  stores therein, for each mode, a set θ d  of component ratios and a behavior label in association with a data number d of a sensor value distribution. Each component ratio θ d,j  constituting the set θ d  of component ratios is stored in association with the basic distribution number j. 
     Next, the component select unit  62  and the ratio select unit  63  are described. The component select unit  62  refers to the mode corresponding database  44  to select a basic distribution corresponding to a mode of a target detected by the mode detecting unit  5  from the component database  42  and outputs the basic distribution to the ratio calculating unit  21 . For example, when the mode B of a target is detected by the mode detecting unit  5 , the selection unit  62  refers to the mode corresponding database  44  to select basic distributions φb 1 , φc 1 , φc 2 , φc 3 , and φb 5  from the component database  42  and outputs these basic distributions to the ratio calculating unit  21 . The ratio select unit  63  selects a component ratio corresponding to a mode of a target from the component ratio database  43   b  and outputs the component ratio to the identification unit  22 . 
     The operation of the behavior identification device  1   b  according to the present embodiment is described with reference to a flowchart. The behavior identification device  1   b  operates in a generation phase in advance and then operates in an identification phase. An operation of the behavior identification device  1   b  in the generation phase is described first.  FIG. 22  is a flowchart of the operation of the behavior identification device  1   b  according to the present embodiment in the generation phase. In the generation phase, the behavior identification device  1   b  performs processes from Steps S 301  to S 305  for each mode detected by the mode detecting unit  5 . The sensor-value obtaining unit  10  obtains first a sensor value from the sensor  2  and calculates a sensor value distribution (Step S 301 ). Next, the sensor-value distribution database  41   b  stores therein the sensor value distribution calculated at Step S 301  for each mode (Step S 302 ). 
     Next, the basic distribution generating unit  30   b  estimates a basic distribution and a component ratio (Step S 303 ). Next, the basic distribution generating unit  30   b  evaluates the basic distribution and the component ratio estimated at Step S 303  (Step S 304 ). At Step S 304 , the basic distribution generating unit  30   b  determines whether the component ratio satisfies predetermined evaluation criteria to evaluate the basic distribution and the component ratio. If the component ratio does not satisfy the evaluation criteria, the operation of the behavior identification device  1   b  proceeds to Step S 305 . At Step S 305 , the basic distribution generating unit  30   b  changes an estimation condition used at Step S 303 . When Step S 305  ends, the operation of the behavior identification device  1   b  returns to Step S 303 . The behavior identification device  1   b  performs processes from Steps S 301  to S 305  for each mode. 
     On the other hand, if the component ratio satisfies the evaluation criteria, the operation of the behavior identification device  1   b  proceeds to Step S 306 . At Step S 306 , the component integrating unit  61  compares basic distributions calculated in different modes, determines that similar basic distributions are identical, and integrates these basic distributions. Next, at step S 307 , the component database  42  stores therein the basic distribution in association with a basic distribution number, whereas the mode corresponding database  44  stores therein information of the basic distribution used in each mode. Such an operation is thus equivalent to an operation in which the component database  42  stores therein a basic distribution for each mode, and it is possible to reduce required storage capacity. At Step S 307 , the component ratio database  43   b  stores therein, for each mode, the component ratio in association with the data number of a sensor value distribution. Next, at Step S 308 , the component ratio database  43  stores therein a behavior label of a target when the sensor value distribution is calculated in association with the data number of a sensor value distribution. When the operation at Step S 308  ends, the operation of the behavior identification device  1  in the generation phase ends. 
     Next, an operation of the behavior identification device  1   b  in the identification phase is described.  FIG. 23  is a flowchart of the operation of the behavior identification device  1   b  according to the present embodiment in the identification phase. In the identification phase, the sensor-value obtaining unit  10  obtains first a sensor value from the sensor  2  and calculates a sensor value distribution (Step S 401 ). Next, the component select unit  62  and the ratio select unit  63  select a basic distribution and a component ratio corresponding to a mode detected by the mode detecting unit  5  (Step S 402 ). Next, the ratio calculating unit  21  calculates a component ratio in the sensor value distribution calculated at Step S 401  using the basic distribution selected at Step S 402  (Step S 403 ). 
     Next, the identification unit  22  calculates the similarity between a set of component ratios calculated at Step S 403  and each set of component ratios stored at Step S 307  in the generation phase (Step S 404 ). Next, the identification unit  22  selects a set of component ratio having the highest similarity among the sets of component ratios stored at Step S 307  in the generation phase (Step S 405 ). The identification unit  22  then outputs a behavior label corresponding to the set of component ratios selected at Step S 405  as an identification result (Step S 406 ). When the operation at Step S 406  ends, the operation of the behavior identification device  1   b  in the identification phase ends. The behavior identification device  1   b  operates as described above. 
     The behavior identification device  1   b  according to the present embodiment detects a mode that affects a behavior of a target such as “at home” or “in factory”, selects a set of basic distributions appropriate for each mode, and uses the set of basic distributions. The behavior identification device  1   b  according to the present embodiment can thus achieve behavior identification with higher accuracy. If the mode of behavior of a target differs, a behavior to be identified by a behavior identification device also differs in most cases. For example, in the “at home” mode, the target is likely to take a behavior such as “lying” or “watching TV”. On the other hand, in the “in factory” mode, the target is less likely to take such behaviors. 
     The behavior identification device  1   b  according to the present embodiment sets, for each mode of a target, only a label of an appropriate behavior as an identification candidate, and thus achieves behavior identification with higher accuracy. In addition, the behavior identification device  1   b  according to the present embodiment compares basic distributions calculated in different modes and integrates similar basic distributions, and thus it is possible to prevent excessive use of memories. The behavior identification device  1   b  according to the present embodiment also has effects identical to those of the behavior identification device  1  according to the first embodiment. 
     Third Embodiment 
     The behavior identification device  1  according to the first embodiment identifies a behavior of a target using a sensor value measured by a sensor. It is assumed that the sensor is, for example, a three-axis acceleration sensor that is attached to the waist of the target. In this case, the sensor measures a sensor value that relates to an action of the core of the target&#39;s body. The behavior identification device  1  uses a measured sensor value to identify the behavior of the target (for example, “stopping”, “walking”, or “running”). However, there is no large difference in sensor values measured by the three-axis acceleration sensor attached to the waist of the target between a behavior “going up stairs” and a behavior “climbing ladder”. Consequently, the accuracy of identifying a behavior in the behavior identification device  1  may degrade. 
     The behavior identification device according to the present embodiment achieves behavior identification with higher accuracy based on sensor values measured by a sensor attached to the wrist of a target, in addition to, for example, a sensor attached to the waist of the target. That is, the behavior identification device according to the present embodiment uses sensor values measured by a plurality of sensors. Differences between the behavior identification device according to the present embodiment and the behavior identification device  1  according to the first embodiment are mainly described below.  FIG. 24  shows an example of a configuration of a behavior identification device  1   c  according to a third embodiment of the present invention. The behavior identification device  1   c  according to the present embodiment includes operation identifying units  100   a  and  100   b , an identification result combining unit  70 , and a combining rule database  45 . Similarly to  FIG. 1 , in  FIG. 24 , broken line arrows indicate the relationships between blocks in a generation phase, whereas solid line arrows indicate the relationships between the blocks in an identification phase. 
       FIG. 25  shows an example of a configuration of the operation identifying units  100   a  and  100   b  in the behavior identification device  1   c  according to the present embodiment. The operation identifying units  100   a  and  100   b  have an identical configuration to the behavior identification device  1  according to the first embodiment shown in  FIG. 1 . Similarly to  FIG. 1 , in  FIG. 25 , broken line arrows indicate the relationships between blocks in the generation phase, whereas solid line arrows indicate the relationships between the blocks in the identification phase. The operation identifying units  100   a  and  100   b  may have an identical configuration to the behavior identification device  1   b  according to the second embodiment shown in  FIG. 14 . 
     As shown in  FIG. 24 , the operation identifying unit  100   a  is connected to a sensor  2   a  and the operation identifying unit  100   b  is connected to a sensor  2   b . The behavior identification device  1   c  uses sensor values measured by the sensor  2   a  and the sensor  2   b  to identify a behavior of a target. The sensor  2   a  is attached to the waist of the target whereas the sensor  2   b  is attached to the wrist of the target. The sensor  2   a  and the sensor  2   b  are three-axis acceleration sensors that output a sensor value, which is the norm of three-axis acceleration values, every 50 milliseconds. The sensor  2   a  can measure a sensor value relating to an identification result of a behavior of the core of the target&#39;s body (for example, “going up” or “going down”). The sensor  2   b  can measure a sensor value relating to a behavior of the target&#39;s hands (for example, “raising hands”, “lowering hands”, or “waving hands”). 
     In the generation phase, the operation identifying unit  100   a  performs an identical process to the behavior identification device  1  according to the first embodiment on a sensor value measured by the sensor  2   a . As a result, a basic distribution for the action of the core of the target&#39;s body is stored in the operation identifying unit  100   a . Next, in the identification phase, the operation identifying unit  100   a  performs an identical process to the behavior identification device  1  according to the first embodiment on a sensor value measured by the sensor  2   a . As a result, the operation identifying unit  100   a  outputs an identification result of the action of the core of the target&#39;s body. 
     On the other hand, in the generation phase, the operation identifying unit  100   b  performs an identical process to the behavior identification device  1  according to the first embodiment on a sensor value measured by the sensor  2   b . As a result, a basic distribution for the action of the target&#39;s hands is stored in the operation identifying unit  100   b . Next, in the identification phase, the operation identifying unit  100   b  performs an identical process to the behavior identification device  1  according to the first embodiment on a sensor value measured by the sensor  2   b . As a result, the operation identifying unit  100   b  outputs an identification result of the action of the target&#39;s hands. 
     In the identification phase, the identification result combining unit  70  combines the identification result of the action of the core of the target&#39;s body that is output from the operation identifying unit  100   a  with the identification result of the action of the target&#39;s hands that is output from the operation identifying unit  100   b , identifies the behavior of the target, and outputs an identification result. The combining rule database  45  stores therein rules for combining an identification result of an action of the core of the body with an identification result of an action of hands.  FIG. 26  is a diagram for explaining a data structure of the combining rule database  45  in the behavior identification device  1   c  according to the present embodiment. As shown in  FIG. 26 , data stored in the combining rule database  45  is represented in a matrix format. The identification result combining unit  70  refers to the combining rule database  45  and identifies the behavior of the whole body of the target using combinations of identification results of the action of the core of the body and identification results of the action of hands. 
     The behavior identification device  1   c  according to the present embodiment operates as described above. The behavior identification device  1   c  according to the present embodiment can identify the behavior of the whole body of a target with higher accuracy by combining an identification result of an action of the core of the target&#39;s body with an identification result of an action of the target&#39;s hands. 
     Fourth Embodiment 
     A fourth embodiment of the present invention relates to an air conditioner that identifies a biological index corresponding to a behavior of a person by using a sensor such as an acceleration sensor, an angular velocity sensor, or a heart rate sensor so as to control an operation of an outdoor unit or an indoor unit, thus achieving more comfortable control. Examples of the biological index include an exercise intensity index and a stress index.  FIG. 27  shows an example of a configuration of an air conditioner  200  according to the fourth embodiment of the present invention. As shown in  FIG. 27 , the air conditioner  200  includes a behavior identification device  1   d , an indoor unit  7 , an outdoor unit  8 , a control rule database  46   a , and a control information output unit  80 . A sensor  2  and an input unit  4  are connected to the air conditioner  200 . 
     The behavior identification device  1   d  is substantially the same as the behavior identification device  1  according to the first embodiment shown in  FIG. 1  except for the data structure of a component ratio database  43   c . In the behavior identification device  1  according to the first embodiment, the component ratio database  43  stores therein a behavior label of a target when each sensor value distribution is measured in association with a component ratio. On the other hand, in the behavior identification device  1   d  according to the present embodiment, the component ratio database  43   c  stores therein a biological index of a target when each sensor value distribution is measured in association with a component ratio. Details thereof are described later. 
       FIG. 28  is a diagram for explaining a usage example of the air conditioner  200  according to the present embodiment. The sensor  2  is attached to a target and wirelessly transmits a measured sensor value to the behavior identification device  1   d . The behavior identification device  1   d  is built or incorporated in the indoor unit  7 . While the control rule database  46   a  and the control information output unit  80  are not shown in  FIG. 28 , the control rule database  46   a  and the control information output unit  80  may be incorporated in selected appropriate locations. For example, the control rule database  46   a  and the control information output unit  80  may be built in the behavior identification device  1   d  or in the indoor unit  7 . 
     The sensor  2  is a three-axis acceleration sensor. As shown in  FIG. 28 , the sensor  2  is attached to the waist of a target  400  and measures three-axis acceleration values ax, ay, and az for a behavior of the target  400 . In addition, the sensor  2  calculates a norm |a| of the three-axis acceleration values ax, ay, and az by the above formula (1), and outputs the norm every 50 milliseconds. While the present embodiment describes a case where a three-axis acceleration sensor functioning as the sensor  2  is attached to the waist of the target  400 , the present invention is not limited thereto. The sensor  2  may be any sensor that can measure a certain change amount relating to a behavior of a target as a sensor value. In addition to the three-axis acceleration sensor, for example, an angular velocity sensor, a position sensor, an air pressure sensor, or a heart rate sensor can be used as the sensor  2 . 
     Next, the behavior identification device  1   d  according to the present embodiment is described. In particular, differences between the behavior identification device  1   d  according to the present embodiment and the behavior identification device  1  according to the first embodiment are mainly described. As described above, in the behavior identification device  1   d  according to the present embodiment, the component ratio database  43   c  stores therein a biological index corresponding to a behavior of the target  400  when a sensor value distribution is calculated in a generation phase.  FIG. 29  is a diagram for explaining a data structure of the component ratio database  43   c  in the air conditioner  200  according to the present embodiment. The component ratio database  43   c  stores therein a set θ d  of component ratios and a biological index in association with a data number d of a sensor value distribution. In the air conditioner  200  according to the present embodiment, the component ratio database  43   c  uses a biological index as a behavior label. 
     In the example of  FIG. 29 , the component ratio database  43   c  stores therein a METs (Metabolic equivalents) value, which is an exercise intensity index, as a biological index. The METs indicates the exercise intensity as the ratio of metabolic rate (rate of energy consumption) to the reference, which is 1.0 METs (a state where a target is at rest such as lying or sitting). For example, the exercise intensity during walking is approximately 2.5 METs, which is 2.5 times higher rate of metabolism (rate of energy consumption) than that at rest. In addition thereto, METs values for various behaviors are known. For example, the exercise intensity during running at 8.0 km/h is approximately 8.0 METs, and the exercise intensity during doing the first radio exercise is 4.0 METs. An identification unit  22  calculates the similarity between a set of component ratios calculated by a ratio calculating unit  21  and each set of component ratios stored in the component ratio database  43   c . In addition, the identification unit  22  outputs, as an identification result, a biological index corresponding to a set of component ratios with the highest similarity among sets of component ratios stored in the component ratio database  43   c . The differences between the behavior identification device  1  according to the first embodiment and the behavior identification device  1   d  according to the present embodiment are as described above. 
     Next, the control rule database  46   a  is described. The control rule database  46   a  stores therein a rule for controlling the indoor unit  7  or the outdoor unit  8  in association with a biological index output from the behavior identification device  1   d .  FIG. 30  is a diagram for explaining a data structure of the control rule database  46   a  in the air conditioner  200  according to the present embodiment. For example, the behavior with an exercise intensity of less than 1.0 METs includes lying at rest or sleeping. In this case, it is highly possible that the target  400  is sleeping. The control rule database  46   a  thus stores therein “not directly blowing on target” as the control rule for an exercise intensity of less than 1.0 METs. For example, the behavior with an exercise intensity of 8.0 METs or more includes a hard exercise such as running at 8.0 km/h. In this case, it is highly possible that the target  400  is doing a hard exercise. The control rule database  46   a  thus stores therein “directly blowing on target” as the control rule for an exercise intensity of 8.0 METs or more. In addition, the control rule database  46   a  stores therein “same as normal state” as the control rule for an exercise intensity of 1.0 METs or more and less than 8.0 METs. 
     Next, the control information output unit  80  is described. The control information output unit  80  receives a biological index output from the identification unit  22  as an input and refers to the control rule database  46   a  to determine a control rule, and outputs the control rule to the indoor unit  7  or the outdoor unit  8  as control information. For example, in the example of the control rule shown in  FIG. 30 , when the identification unit  22  identifies the exercise intensity to be 3.0 METs, the control information output unit  80  refers to the control rule database  46   a  and outputs the control rule “same as normal state”. When the identification unit  22  identifies the exercise intensity to be 9.0 METs, the control information output unit  80  refers to the control rule database  46   a  and outputs the control rule “directly blowing on target”. The indoor unit  7  or the outdoor unit  8  operates according to the control information output from the control information output unit  80 . The air conditioner  200  according to the present embodiment operates as described above. 
     The air conditioner  200  according to the present embodiment identifies a behavior of a person and outputs a biological index corresponding to an identified behavior. It is thus possible to achieve a more comfortable air conditioner control system that directly blows on a person doing a hard exercise or that does not directly blow on a sleeping person. 
     While the present embodiment describes a case where, for example, a three-axis acceleration sensor is attached to the waist of a target and METs, which is an exercise intensity index, is used as a biological index, the present invention is not limited thereto. For example, a heart rate/pulse sensor may be attached to the chest or wrist of a target and LF/HF, which is a stress index, may be used as a biological index, so that it is possible to achieve a more comfortable air conditioner. LF/HF is a stress index for measuring the balance of an autonomic nervous function, that is, the balance between a Low Frequency (LF) component and a High Frequency (HF) component in heart rate variability. The LF/HF decreases when a person is relaxed and increases when the person gets stressed. Consequently, when the LF/HF is increased, for example, an air conditioner blows air in a fluctuation mode or emits an aromatic fragrance with relaxing effect, so that more comfortable control is achieved. 
     Fifth Embodiment 
     A fifth embodiment of the present invention relates to a robot control device that uses a sensor such as an acceleration sensor, an angular velocity sensor, or a heart rate sensor to identify a behavior of an operator around a robot in a factory, and controls an operation of the robot based on an identification result. The robot control device according to the present embodiment identifies a safe behavior that is set in advance as a behavior of a target, a dangerous behavior that is set in advance, and a deviant behavior that is not set in advance to control the operation of the robot. The robot control device according to the present embodiment thus enables a robot system that improves the safety of an operator and prevents a robot from unnecessarily stopping to be established. 
       FIG. 31  shows an example of a configuration of a robot control device  300  according to the fifth embodiment of the present invention. As shown in  FIG. 31 , the robot control device  300  includes a behavior identification device  1   e , a control rule database  46   b , and control information output unit  80 . A sensor  2 , an input unit  4 , a robot  9 , and a check device  90  are connected to the robot control device  300 . A robot system is constituted by the robot control device  300 , the sensor  2 , the input unit  4 , the robot  9 , and the check device  90 . The behavior identification device  1   e  is substantially the same as the behavior identification device  1  according to the first embodiment shown in  FIG. 1  except that the check device  90  can update data in a component ratio database  43   d . Details thereof are described later. 
       FIG. 32  is a diagram for explaining a usage example of the robot control device  300  according to the present embodiment. The sensor  2  is attached to an operator  401  and wirelessly transmits a measured sensor value to the behavior identification device  1   e . The behavior identification device  1   e  is built or incorporated in the robot  9 . The check device  90  is constituted by a camera  91  and a display device  92 . The camera  91  captures images around the robot  9 . The display device  92  includes a liquid crystal display device or the like, and displays images captured by the camera  91  in a real-time manner. A third person  402  uses the display device  92  to check the robot  9  and the operator  401 . The display device  92  also includes a function of updating data in the component ratio database  43   d  by an operation of the third person  402 . While the control rule database  46   b  and the control information output unit  80  are not shown in  FIG. 32 , the control rule database  46   b  and the control information output unit  80  may be incorporated in selected appropriate locations. 
     The sensor  2  is a three-axis acceleration sensor in the present embodiment. As shown in  FIG. 32 , the sensor  2  is attached to the waist of the operator  401  and measures three-axis acceleration values ax, ay, and az for a behavior of the operator  401 . In addition, the sensor  2  calculates a norm |a| of the three-axis acceleration values ax, ay, and az by the above formula (1), and outputs the norm every 50 milliseconds. While the present embodiment describes a case where a three-axis acceleration sensor functioning as the sensor  2  is attached to the waist of the operator  401 , the present invention is not limited thereto. The sensor  2  may be any sensor that can measure a certain change amount relating to a behavior of a target as a sensor value. In addition to the three-axis acceleration sensor, for example, an angular velocity sensor, a position sensor, an air pressure sensor, or a heart rate sensor can be used as the sensor  2 . 
     Next, the behavior identification device  1   e  according to the present embodiment is described. In particular, differences between the behavior identification device  1   e  according to the present embodiment and the behavior identification device  1  according to the first embodiment are mainly described. A sensor-value distribution database  41  stores therein a sensor value distribution measured when the operator  401  takes a safe behavior and a sensor value distribution measured when the operator  401  takes a dangerous behavior. The safe behavior is a behavior that is performed as a normal process operation by the operator  401  around the robot  9 . The dangerous behavior is a behavior that is defined in advance to be apparently dangerous to the operator  401  when performed around the robot  9 . The component ratio database  43   d  stores therein “safe behavior” or “dangerous behavior” as a behavior label of the operator  401  when a sensor value distribution is measured.  FIG. 33  is a diagram for explaining a data structure of the component ratio database  43   d  in the robot control device  300  according to the present embodiment. 
     An identification unit  22  calculates the similarity between a set of component ratios calculated by a ratio calculating unit  21  and each set of component ratios stored in the component ratio database  43   d . In addition, the identification unit  22  outputs, as an identification result, a behavior label corresponding to a set of component ratios with the highest similarity among sets of component ratios stored in the component ratio database  43   d . However, if the calculated highest similarity is less than a threshold defined in advance, the identification unit  22  outputs “deviant behavior” as an identification result. Therefore, the identification unit  22  outputs any one of “safe behavior”, “dangerous behavior”, or “deviant behavior” as an identification result. The behavior identification device  1   e  operates as described above. 
     Next, the control rule database  46   b  is described. The control rule database  46   b  stores therein a rule for controlling the robot  9  in association with an identification result output from the behavior identification device  1   e .  FIG. 34  is a diagram for explaining a data structure of the control rule database  46   b  in the robot control device according to the present embodiment. As described above, the safe behavior is a behavior that is performed as a normal process operation by the operator  401  around the robot  9 . Consequently, when the identification result is “safe behavior”, it is less possible that an operation of the robot  9  endangers the operator  401 . The control rule database  46   b  thus stores therein “normal operation” as the control rule for “safe behavior”. 
     On the other hand, the dangerous behavior is a behavior that is defined in advance to be apparently dangerous to the operator  401  when performed around the robot  9 . Consequently, the control rule database  46   b  stores therein “emergency stop” as the control rule for “dangerous behavior”. When the identification result is “deviant behavior”, it is highly possible that a behavior that is not classified in advance into a safety behavior or a dangerous behavior is performed. The behavior of the operator  401  in such a case is different from the behavior that is performed as a normal process operation, and is not the behavior that is defined in advance to be apparently dangerous to the operator  401 . Consequently, the control rule database  46   b  stores therein “reduce operating speed” and “ask for check-up” as the control rule for “deviant behavior”. 
     Next, the control information output unit  80  is described. The control information output unit  80  receives a behavior label identified by the identification unit  22  as an input and refers to the control rule database  46   a  to output a control rule to the robot  9  as control information. For example, when “deviant behavior” is identified by the identification unit  22 , the control information output unit  80  outputs the control rule “reduce operating speed” and “ask for check-up”. The robot  9  operates according to input control information. When “ask for check-up” is input as the control information, the robot  9  outputs a signal asking for checking the state of the operator  401  and the robot  9  through a wireless or wired communication unit to the check device  90 . 
     Next, the check device  90  is described. The check device  90  includes the camera  91  and the display device  92 . When a signal asking for checking the state is input from the robot  9  to the check device  90 , the check device  90  captures images around the robot  9  using the camera  91  and displays the images on the display device  92  in a real-time manner. The third person  402  can thus check a deviant behavior of the operator  401  around the robot  9  and handle the deviant behavior as needed. When the third person  402  checks that the deviant behavior of the operator  401  is apparently dangerous to the operator  401 , the third person  402  can emergently stop the robot  9 . Alternatively, when the third person  402  checks that the deviant behavior of the operator  401  is not dangerous to the operator  401 , the third person  402  can reduce an operating speed and cause the operating robot  9  to return to its normal operation. 
     Additionally, when the third person  402  checks that the deviant behavior of the operator  401  is apparently dangerous to the operator  401 , the check device  90  associates a set of component ratios calculated by the ratio calculating unit  21  with the label “dangerous behavior” and adds the set of component ratios to the component ratio database  43   d . Moreover, when the third person  402  checks that the behavior that is identified to be the deviant behavior of the operator  401  is misdetected as the behavior performed as a normal process operation by the operator  401 , the check device  90  associates a set of component ratios calculated by the ratio calculating unit  21  with the “safe behavior” label and adds the set of component ratios to the component ratio database  43   d.    
     While the present embodiment describes a case where for example, a three-axis acceleration sensor is attached to the waist of the operator  401 , the present invention is not limited thereto. A heart rate/pulse sensor may be attached to the chest or wrist of the operator  401  to directly identify physiological abnormality of the operator  401 , so that it is possible to achieve a safer robot system. The robot control device  300  according to the present embodiment identifies a behavior of the operator  401  operating around the robot  9  in a factory and controls an operation of the robot based on an identification result, and thus it is possible to improve the safety of an operator. For example, when a dangerous behavior of the operator  401  is identified, the robot  9  emergently stops. Consequently, it is possible to reduce the probability that the operator  401  is endangered. In addition, when a deviant behavior of the operator  401  is identified, the third person  402  checks the danger to the operator  401  and handles the danger. Consequently, it is possible to prevent the robot from unnecessarily stopping and improve the safety of the operator  401 . Moreover, when the deviant behavior of the operator  401  is identified, an identification result and an actual behavior of the operator  401  are checked and additionally stored in a database. Consequently, it is possible to improve the identification performance. 
     DESCRIPTION OF REFERENCE SYMBOLS 
     
         
           1 ,  1   b ,  1   c ,  1   d ,  1   e  behavior identification device 
           2 ,  2   a ,  2   b  sensor 
           3  display unit 
           4  input unit 
           5  mode detecting unit 
           7  indoor unit 
           8  outdoor unit 
           9  robot 
           10  sensor-value obtaining unit 
           21  ratio calculating unit 
           22  identification unit 
           30 ,  30   b  basic distribution generating unit 
           31  analysing unit 
           32  evaluating unit 
           41 ,  41   b  sensor-value distribution database 
           42  component database 
           43 ,  43   b ,  43   c ,  43   d  component ratio database 
           44  mode corresponding database 
           45  combining rule database 
           46   a ,  46   b  control rule database 
           61  component integrating unit 
           62  component select unit 
           63  ratio select unit 
           70  identification result combining unit 
           80  control information output unit 
           90  check device 
           91  camera 
           92  display device 
           100   a ,  100   b  operation identifying unit 
           200  air conditioner 
           300  robot control device 
           400  target 
           401  operator 
           402  third person 
           1001  processing circuit 
           1002 ,  1005  memory 
           1003  data bus 
           1004  processor