Patent Publication Number: US-2010131228-A1

Title: Motion mode determination method and apparatus and storage media using the same

Description:
This Application claims priority of Taiwan Patent Application No. 97145898, filed on Nov. 27, 2008, the entirety of which is incorporated by reference herein. 
     BACKGROUND OF THE INVENTION 
     1. Field of the Invention 
     The invention relates generally to a motion mode determination method and apparatus and storage media using the same, and more particularly, to a motion mode determination method and apparatus and storage media using the same, which is capable of determining the surrounding terrain of a pedestrian. 
     2. Description of the Related Art 
     Electronic devices have become an essential part of every day life for humans. For example, when traveling, a Global Positioning System (GPS) is used to find the most appropriate routes for traveling. However, the GPS is not suitable for indoor usage, and even more is not suitable for a pedestrian. So it is necessary to provide a pedestrian with a motion mode determination method and apparatus for judging the surrounding terrain and helping him by the auxiliary guidance service. 
     BRIEF SUMMARY OF THE INVENTION 
     The invention discloses a motion mode determination apparatus. The motion mode determination apparatus comprises an inertial device, a frequency decomposition module, a characteristic value generator, a training module and a determination module. The inertial device collects at least a first motion signal corresponding to a first motion mode and at least a second motion signal corresponding to a second motion mode, wherein each of the first motion signal and the second motion signal comprises a first signal, a second signal and a third signal. The frequency decomposition module decomposes each of the first signals into a first high-frequency signal and a first low-frequency signal. The characteristic value generator generates a plurality of characteristic values, wherein the characteristic values are the means and variances for each group of the first high-frequency signals, the first low-frequency signals, the second signals and the third signals respectively. The training module generates a first data group corresponding to the first motion mode and a second data group corresponding to the second motion mode, according to the characteristic values. The determination module determines the motion mode of a third motion signal according to the generated first data group and the second data group. 
     Furthermore, the invention discloses a motion mode determination method. The method comprises collecting at least a first motion signal corresponding to a first motion mode and at least a second motion signal corresponding to a second motion mode, wherein each of the first motion signal and the second motion signal comprises a first signal, a second signal and a third signal. The method further comprises decomposing each of the first signals into a first high-frequency signal and a first low-frequency signal. The method further comprises generating a plurality of characteristic values, wherein the characteristic values are the means and variances for each group of the first high-frequency signals, the first low-frequency signals, the second signals and the third signals respectively. The method further comprises generating a first data group corresponding to the first motion mode and a second data group corresponding to the second motion mode, according to the characteristic values. The method further comprises determining the motion mode of a third motion signal according to the generated first data group and the second data group. 
     Furthermore, the invention discloses a storage medium for storing a motion mode determination program. The motion mode determination program comprises a plurality of program codes to be loaded onto a computer system so that a motion mode determination method may be executed by the computer system. The method comprises collecting at least a first motion signal corresponding to a first motion mode and at least a second motion signal corresponding to a second motion mode, wherein each of the first motion signal and the second motion signal comprises a first signal, a second signal and a third signal. The method further comprises decomposing each of the first signals into a first high-frequency signal and a first low-frequency signal. The method further comprises generating a plurality of characteristic values, wherein the characteristic values are the means and variances for each group of the first high-frequency signals, the first low-frequency signals, the second signals and the third signals respectively. The method further comprises generating a first data group corresponding to the first motion mode and a second data group corresponding to the second motion mode, according to the characteristic values. The method further comprises determining the motion mode of a third motion signal according to the generated first data group and the second data group. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
       For fully understanding the of the purpose, the features, and the advantage of the invention, preferred embodiments of the invention are illustrated in the accompanying drawings and described in detail with reference to the following description. In the drawings: 
         FIG. 1  shows a block diagram of the pedestrian motion mode determination apparatus according to an embodiment of the invention; 
         FIG. 2  shows an flowchart of the pedestrian motion mode determination method according to an embodiment of the invention; 
         FIG. 3A  shows an exemplary diagram for the first signal according to an embodiment of the invention; 
         FIG. 3B  shows a diagram of frequency decomposition for signal samples divided from accelerator signals, according to an embodiment of the invention; and 
         FIG. 4  shows a diagram of a training result according to an embodiment of the invention. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     The following description is the preferred embodiment for carrying out the invention. This description is made for the purpose of illustrating the general principles of the invention and should not be taken in a limiting sense. The scope of the invention is best determined by reference to the appended claims. 
       FIG. 1  depicts a block diagram of a pedestrian motion mode determination apparatus  10  according to an embodiment of the invention. The pedestrian motion mode determination apparatus  10  comprises an inertial device  11 , a frequency decomposition module  12 , a characteristic value generator  13 , an amplifier  14 , a training module  15  and a determination module  16 . The details will be illustrated below. 
       FIG. 2  depicts a flowchart of the pedestrian motion mode determination method according to an embodiment of the invention. When beginning operation, the pedestrian motion mode determination apparatus  10  collects various motion signals corresponding to various motion modes. The motion signals for the motion modes are trained and categorized. Then the signals after trained and categorized can be used to determine the surrounding terrain of a user and helping him by the auxiliary guidance service. 
     In the embodiment, the invention assumes that the inertial device  11  initially receives a pedestrian motion signal “walking” corresponding to a pedestrian motion mode “walking”, as well as another pedestrian motion signal “walking upstairs” corresponding to the pedestrian motion mode “walking upstairs” (step S 20 ). In some embodiments, the inertial device  11  comprises an accelerator, a gyro and a compass. Each of the pedestrian motion signals comprises a first signal collected by the accelerator, a second signal collected by the gyro, and a third signal collected by the compass. 
     After the pedestrian motion signals “walking” and “walking upstairs” are collected, the next step is to extract a plurality of characteristic values from the collected signals, such as the first signals, second signals and third signals collected by the accelerator, the gyro and the compass. For the collected first signals by the accelerator, the characteristic values are obtained by frequency decomposition. Referring to  FIG. 3A  which shows an exemplary diagram for the first signal, the frequency decomposition module  12  divides the first signal into a plurality of signal samples, wherein each sample has a time length of 2 seconds and the interval time of 0.5 seconds (one signal sample extracted/per 0.5 seconds) for example. Thus, numerous continuous signal samples are extracted from the first signal. The purpose of signal dividing is to reflect a continuous pedestrian motion mode. If the first signal is not divided into signal samples, the data analysis would not be accurate since there could be several motion modes contained in the first signal. 
     Next, the frequency decomposition module  12  decomposes each signal sample into a high-frequency signal and a low-frequency signal using wavelet transform (step S 21 ), as shown in  FIG. 3B . Referring to  FIG. 3B , the frequency decomposition module  12  firstly decomposes each signal sample into a first level high-frequency signal (H) and a first level low-frequency signal (L). Next, the frequency decomposition module  12  decomposes each first level low-frequency signal (L) into a second level high-frequency signal (LH) and a second level low-frequency signal (LL). Following, the frequency decomposition module  12  decomposes each second level low-frequency signal (LL) into a third level high-frequency signal (LLH) and a third level low-frequency signal (LLL). In this embodiment, the frequency decomposition procedure is performed for three levels, however, more levels may perform the frequency decomposition procedure as desired. Next, the four signals: the first level high-frequency signal (H), the second level high-frequency signal (LH), the third level high-frequency signal (LLH) and the third level low-frequency signal (LLL), are used as the representative signals for the first signal. 
     Based on the four representative signals, the second and the third signals for each motion signal, the characteristic value generator  13  generates the means and variances for each group of the six signals (step S 22 ) respectively, so that 12 characteristic values are obtained. In some embodiments, the 12 characteristic values are not yet appropriate for signal analysis since they are somewhat weak in signal strength. Thus, the amplifier  14  is provided to amplify the characteristic values in an exponential manner (step S 23 ). The amplified characteristic values are later sent to the training module  15  for pedestrian motion mode training (step S 24 ). A Support Vector Machine (SVM) algorithm is provided by the training module  15  for training of the pedestrian motion mode. 
     In some embodiments, the following formula is provided for data training by the training module  15 : 
     
       
         
           
             
               
                 
                   
                     
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     wherein, X is characteristic value vector for unanalyzed data, α i  and b are constants which are generated during the training of the SVM algorithm, K is a Kernel Function, which is used to project data from a current dimension to a higher dimension, x i  is a support vector, which is generated during the training of SVM algorithm, and y i  is the corresponding label with respect to x i , such as a level group or a stairway. 
     Next, after all characteristic values are trained by the SVM algorithm, categorized motion mode data are generated (step S 25 ). Following, the categorized motion mode data is stored in a pedestrian navigator, such that a motion mode and surrounding terrain of a pedestrian can be detected using the trained data (step S 26 ), thus further providing auxiliary guidance services. 
       FIG. 4  shows a diagram of a training result according to an embodiment of the invention. For example in  FIG. 4 , the training dimension is 2 (2D), and the training result shown in  FIG. 4  is generated by the SVM algorithm training the extracted characteristic values. In  FIG. 4 , each white or black dot represents a signal sample. Note that the signal samples distribution for the same motion mode appears congregated. As an example, the data group of black dots may represent the motion mode “walking”, whereas the data group of white dots may represent the motion mode “walking upstairs”. As a result, the black dots represent a category of motion mode “walking”, and the white dots represent another category of motion mode “walking-upstairs”. 
     Following, how the trained data is used to determine an on-going motion mode of a pedestrian is described. 
     When a pedestrian is moving (walking, running, etc.), the pedestrian motion mode determination apparatus  10  receives a motion signal through the inertial device  11 . Then, the characteristic value generator  13  generates characteristic values thereof. The amplifier  14  next amplifies the characteristic values, and the determination module  16 , according to the amplified characteristic values, determines which data group is located closest to the signal sample of the motion signal. If the signal sample of the motion signal is located closer to the black dots group, then the pedestrian motion mode determination apparatus  10  is determined to be under the motion mode “walking”. Therefore, it is determined that the surrounding terrain is a level group. On the contrary, if the signal sample of the motion signal is located closer to the white dots group, then the pedestrian motion mode determination apparatus  10  is determined to be under the motion mode “walking-upstairs”. Therefore, it is determined that the surrounding terrain of the pedestrian is a stairway. 
     A separate line determined by the previously described Formula (A) can be used to determine which data group the pedestrian motion mode is close to. As shown in  FIG. 4 , the training module  15  is required to generate a line which can separate the black and white dot groups, with substantially the same distance to each data group, and line H 1 , H 2 , and H 3  are drawn for illustration. Referring to the line H 1  in  FIG. 4 , even though it lies between the black and white data groups, it is not considered a qualified line since not every portion of the line is substantially the same distance to each data group. Note that using a non-qualified line for determining a motion mode will lead to an erroneous analysis. As an example, assume a signal sample of a current pedestrian motion signal located on point A, as shown in  FIG. 4 , is considered as the same motion mode represented by the white dots data group since the signal sample is located closer to the white dots data group. However, according to the line H 1 , the signal sample should be instead categorized as the same motion mode represented by the black dots data group since the signal sample is located on the same side with the black dots data group. Additionally, the line H 3  seems non-qualified since it does not separate the black and white data group. Thus, the most qualified line is H 2 , since every portion of the line is substantially the same distance to each data group. Therefore, the line H 2  is the best solution for determining an unknown motion mode of a pedestrian. 
     Note that in  FIG. 4 , the exemplary data dimension is 2. However, more than 2 data dimensions may be applied. In addition, the trained motion modes are not limited to “walking” and “walking upstairs”. 
     Finally, the pedestrian motion mode determination method can be recorded as a program in a storage medium for performing the above procedures, such as an optical disk, floppy disk and portable hard drive and so on. It is to be emphasized that the program of the pedestrian motion mode determination method is formed by a plurality of program codes corresponding to the procedures described above. 
     While the invention has been described by way of example and in terms of the preferred embodiments, it is to be understood that the invention is not limited to the disclosed embodiments. To the contrary, it is intended to cover various modifications and similar arrangements (as would be apparent to those skilled in the art). Therefore, the scope of the appended claims should be accorded the broadest interpretation so as to encompass all such modifications and similar arrangements.