PHYSIOLOGICAL INFORMATION MEASUREMENT SYSTEM AND METHOD THEREOF

A physiological information measurement system includes at least one video capture unit, a calculating unit electrical coupled to the video capture unit and a display unit electrical coupled to the calculating unit. The video capture unit captures at least one video provided to the calculating unit. The calculating unit measures physiological information according to the video. The display unit shows the physiological information.

DETAILED DESCRIPTION

Please refer toFIG. 1, a physiological information measurement system of one embodiment of the present disclosure includes at least one video capture unit10, a calculating unit11and a display unit12. The video capture unit10can be a camera, a video file, a universal serial bus web camera (USB web camera), a camera for mobile devices, web information, web video streaming or field depth camera. The video capture unit10can be single one or plural.

Please refer toFIG. 4A, which depicts an embodiment of a video capture unit21of the disclosure. The video capture unit21includes several USB web cameras. Each USB web camera captures video data of a person. As shown inFIG. 4B, the images include a first video23, a second video24and a third video25. The first video23, the second video24and the third video25are displayed in a calculating unit22.

Please refer toFIG. 5A, which depicts an embodiment of a video capture unit310of the disclosure. The video capture unit310is a camera for a mobile device31. As shown inFIG. 5B, the video capture unit310captures at least one video32of a person30. The video32is shown on the mobile device31.

The calculating unit11is electrically coupled to the video capture unit10. The calculating unit11includes a feature extraction module110, a data synchronization module111, an independent component analysis module112, a peak detection module113, a physiological information statistic module114and an information carrier module115.

The feature extraction module110is electrically coupled to the video capture unit10. The feature extraction module110receives video data from the video capture units10and generates a plurality of features.

Please refer toFIG. 4Bagain, the first image23, the second image24and the third image25have regions230,231,240,241,250and251respectively. The regions230,231,240,241,250and251can be treated as the described video data. For example, regions230,240and250can be used to measure a heart rate. The regions231,241and251can be used to measure a respiratory rate. However, they are not limited to measure the heart rate and the respiratory rate.

The feature extraction module110utilizes a temporal differencing method to obtain motion pixels40,41and42in the regions324,325and326ofFIG. 5B, which are treated as video data. As shown inFIG. 6, if the amount of the motion pixels40,41and42is figured out, then the amount can be treated as features for the video data.

The data synchronization module111receives the features from the feature extraction module110and synchronizes the features.

The independent component analysis module112receives the synchronous features and generates a plurality of independent components.

The peak detection module113receives the independent components and generates peak information and several serial peak signals.

The physiological information statistic module114receives and analyzes the serial peak signals to select one of the independent components. The physiological information statistic module114generates a physiological signal based on the selected independent component.

The information carrier module115is informatively connected to the feature extraction module110, the data synchronization module111, the independent component analysis module112, the peak detection module113and the physiological information statistic module114. The information carrier module115can be an inner or outer data base or a fixed or mobile memory.

Please refer toFIG. 2, a physiological information measurement method of the present disclosure includes the steps of:

Step1(S1), providing K groups of video data, and each group of video dada includes sequential image data of physical physiological information regions. For example, the physical physiological information region can be a face region, a neck region, an arm region, a shoulder region, a chest-abdominal region, a left chest region or a right chest region.

The physiological information regions can be obtained by a face detecting process, a skin color detecting process or a manually figuring process. For example the face detecting process can refer to M.-Z. Poh, D. J. McDuff, and R. W. Picard, “Advancements in noncontact, multiparameter physiological measurements using a webcam,” IEEE Trans. Biomedical Engineering, vol. 58, pp. 7-11, January 2011. The skin color detecting process can refer to K.-Z. Lee, P.-C. Hung, and L.-W. Tsai, “Contact-free heart rate measurement using a camera,” in Proc. Ninth Conference on Computer and Robot Vision, 2012, pp. 147-152. The manually figuring process can refer to K. S. Tan, R. Saatchi, H. Elphick, and D. Burke, “Real-time vision based respiration monitoring system,” in Proc. International Symposium on Communication Systems Networks and Digital Signal Processing, 2010, pp. 770-774.

Referring toFIG. 1, the format of the video data is one of three primary color format (red, green and blue, RGB format), true-color space format (luminance, chrominance and chrome, YUV format) or color attribute format (hue, saturation and value, HSV format). The video data captured by the video capture unit10are saved in the information carrier module115based on time sequence for later access and calculation.

For example, the K groups of video data are obtained by shooting a person with the video capture units10. The K groups of video data are provided to the calculating unit11.

The K groups of video data can also be obtained by shooting a person with the video capture units10built in a mobile device such as a mobile phone.

As described above, Iffkis the image data, where k=1, 2, 3, . . . , K. Ifkis the fthframe in the kthvideo. T(Ifk) is the time for capturing image Ifk. Unit of the time can be ms, μs, s, minute or hour.

S2, the feature extraction module110obtains features including physiological information from each image Ifkto analyze physiological information.

For example, if the physiological information is a heart rate, then the heart rate is obtained by the average color of skin region accompany with a weighted statistical method. The weighted statistical method can refer to K.-Z. Lee, P.-C. Hung, and L.-W. Tsai, “Contact-free heart rate measurement using a camera,” in Proc. Ninth Conference on Computer and Robot Vision, 2012, pp. 147-152. Therefore, when heart rate is measured, the feature ufkof the fthframe in the kthvideo can be a weighting value for color average.

If the physiological information is a respiratory rate, then the respiratory rate is obtained by measuring the movement of chest. The movement is obtained by a temporal differencing method. The temporal differencing method can refer to K. S. Tan, R. Saatchi, H. Elphick, and D. Burke, “Real-time vision based respiration monitoring system,” in Proc. International Symposium on Communication Systems Networks and Digital Signal Processing, 2010, pp. 770-774. Therefore, when respiratory rate is measured, the feature ufkof the fthframe in the kthvideo can be an amount of motion pixels.

S3, since the frame rate of each video data is not static, frame rate is defined as the number of frames captured in a specific period. For example, the video capture units10has a frame rate N fames/sec, where N is a constant such as 10, 20, 30, 60, 120, 150, 180 or 300.

As described above, the time points of the video data is not synchronous due to unstable frame rate of each video data. A common frequency H fps is provided for each video data to obtain a synchronous feature νtkat time t by interpolation method, where T(νtk)=1000×t/H is the time index of the synchronous feature νtk, t=1, 2, 3, . . .

The synchronous feature vtkof each video data has the same time index T(νtk) at time t after synchronization.

If the feature ufkof a known image Ifkhas a time index T (Ifk), the synchronous feature νtkat time t can be obtained by an interpolation method. The interpolation method can be a linear interpolation method, a bilinear interpolation method or a bicubic interpolation method. These interpolation methods refer to J. G. Proakis and D. K. Manolakis, Digital Signal Processing (4th Edition): Prentice Hall, 2006.

For example, the synchronous features are obtained by a linear interpolation method, which is measured by the following equation:

where T(Ifk)≦T(νtk)≦T(If+1k)
the synchronous features are obtained by a data synchronization module111.

Please refer toFIG. 7, features of the regions230,240and250inFIG. 4Bare shown. The features can be a series of heart rate feature under the frame rate of each video capture unit is 30 fps and the measurement is performed for 5 seconds.

Supposed that the three video data have unstable frame rate, only 129 frames, 150 frames and 140 frames are captured. In addition, since each video capture unit has different characteristics, the captured features are different. Three average values of feature series are 138.43, 64.38 and 90.42 respectively.

A common frequency H fps is therefore defined and provided to each video data to obtain the synchronous feature νtkat time t by the interpolation method.

FIG. 8shows the synchronous features for heart rate (after step S3). All features νtkat time t of different groups have the same time index T(νtk) .

S4, in addition to the physiological information, the video data also implicitly includes periodical variation of environment light (blinking lamp), periodical regulation of camera (automatic light compensation) and other variations caused by movement or facial expression change. If multiple groups of video data are measured simultaneously, since each video data includes the same physiological information, an independent component analysis method is utilized to extract stable signals from the video data. The independent component analysis method utilizes a linear transformation process to transform signals to a combination of non-Gaussian distributed signals which are statistically independent. The independent component analysis refers to A. Hyvärinen, J. Karhunen, and a. E. Oja, Independent Component Analysis. New York: John Wiley & Sons., 2001.

If N is the number of features which are intended to be analyzed. The value of N depends on the common frequency H fps and a reasonable value of the measured physiological information. For example, if N for the heart rate is defined as 5H, and N for the respiratory rate is defined as 30H, that means the heart rate and the respiratory rate use 5 seconds and 30 seconds as their input features respectively.

ztis a matrix of all features at time t

ztis transformed to a matrix of statistically non-Gaussian independent components. zt=Axi, where A is a mixing matrix. Since A and xiis unknown, ztcan be rewritten as

where W is a demixing matrix similar to matrix A. If a demixing matrix W satisfies W≈A−1, the independent component matrix yt≈xt, and ytkis the value of the kthindependent component at time t.

The independent conponents are obtained by the independent component analysis module112.

Referring toFIG. 9, the three independent components are analyzed, wherein N=5H and H=30 fps. As shown inFIG. 9, peaks are indicated by small circles. Each independent component has four peaks. The peak detection method is described in the step S5.

In step S5, the peak of the independent component ytis detected to obtain the signal period.

In the peak detection step, noise of signals is filtered out by a low pass filter or a median filter. Afterwards, local extreme values are searched to determine peaks' location. The signals are the described independent components. The peak detection method can refer to J. G. Proakis and D. K. Manolakis, Digital Signal Processing (4th Edition): Prentice Hall, 2006.

Referring toFIG. 3, peak detection method for each independent component is described as follows.

In step S8, low frequency signals of each independent component are filtered out by a filter to obtain a denoised signal matrix ot, where otkis the value of the kthgroup of denoised signal at time t.

In step S9, each denoised signal otkis given a corresponding signal direction Dtkwhich can be up, down or none.

Dtkis given an initial value which is none, i.e. Dtk=NONE.

When otk−ot−1k>0, the signal direction is up, and when otk−ot−1k<0, the signal direction is down. The signal direction Dtkis therefore determined

In step510, determine whether the signal direction changes from up to down at current time t. If the kthgroup of the denoised signal has down direction at time t, and has up direction at time t−1, i.e. Dtk=DOWN, Dt−1k=UP, then a time point pikis obtained (S11), where pikis the time point of the ithpeak of the kthgroup of the denoised signals otk, i=1, 2, 3, . . . nk, nkis the peak number of the kthgroup of the denoised signals.

In step S12, if the time t is not the point where the signal direction changes from UP to DOWN or a new peak is obtained, then determine whether the signal is the last one of the signal series. If the signal is the last one of the signal series, the peak detection ends (S13); if the signal is not the last one, then return to step S9to determine the signal direction of next time point.

The peak detection is performed by the peak detection module113.

In step S6, peak-peak interval (PPI) between two adjacent peaks is calculated and analyzed to select a stable independent component to be the representative component.

The qjkrepresents the jthPPI of the kthgroup of the independent components, where j=1, 2, 3, . . . , nk−1. the value of qjkis obtained by the following equation:

The Skrepresents the variance of the PPI of the kthgroup of the independent components. The independent component with the minimal variance (the most stable one) is selected as the representative component. The variance of PPI is calculated by the following equation:

Theqkrepresents average of PPI of the kthgroup of the independent components. The average is calculated by the following equation:

The averageqkis selected to calculate the physiological information value R. R is calculated by the following equation:

The independent component with the minimal variance is selected as the representative component. The average PPIqkof the representative component can be utilized to obtain the physiological information.

The physiological information is obtained by the physiological information statistic module114.

In step S7, the physiological information obtained in step S6is displayed on the display unit12.

Information or data obtained by the feature extraction module110, the data synchronization module111, the independent component analysis module112, the peak detection module113and the physiological information statistic module114can be saved in the information carrier module115or loaded from the information carrier module115.

In the present disclosure, several video capture units are utilized to capture several video data. The video capture units can be various kinds of camera or information from internet.

In the present disclosure, measurement of the physiological information is automatic and noncontact based, which can reduce uncomfortable feeling. Besides, the influence caused by unstable signal is also reduced.