Disclosure of Invention
The invention aims to provide a parkinsonism quantitative early diagnosis system based on behavior feature recognition, which can perform parkinsonism quantitative early diagnosis on a patient based on the behavior feature recognition, can improve the diagnosis effect of the parkinsonism patient and solves the problems in the background technology.
In order to achieve the above purpose, the present invention provides the following technical solutions:
The system comprises an intelligent acquisition device and a quantitative diagnosis platform, wherein the intelligent acquisition device is configured to acquire the behavior characteristics of a patient in the course of the behavior activities of the patient, determine the real-time data of the behavior of the patient, and transmit the acquired real-time data of the behavior of the patient to the quantitative diagnosis platform based on a wireless transmission technology;
the writing photographing device is used for performing writing and photographing according to the writing tremble times and the corresponding writing pressure values;
The quantitative diagnosis platform is configured to construct a model for early diagnosis of parkinsonism quantitative based on the behavior characteristics of the patient, and analyze real-time data of the behavior of the patient and early diagnosis of parkinsonism quantitative based on the constructed model.
Preferably, the intelligent acquisition equipment comprises a motion acquisition unit, a voice acquisition unit, a writing acquisition unit and a face acquisition unit;
The motion acquisition unit is configured to monitor and acquire gait data, balance data and finger knocking data of a patient in real time based on the internet of things technology to acquire motion data of the patient;
The voice acquisition unit is configured to monitor and acquire voice characteristics and voice contents of a patient in real time based on the internet of things technology to acquire voice data of the patient;
The writing acquisition unit is configured to monitor and acquire writing dynamics data and writing graphic data of a patient in real time based on the internet of things technology to acquire writing data of the patient;
The face acquisition unit is configured to monitor and acquire face action data and facial expression data of a patient in real time based on the internet of things technology, and acquire the face data of the patient;
and determining real-time data of patient behaviors based on the Internet of things according to the acquired patient motion data, the patient voice data, the patient writing data and the patient face data.
Preferably, the writing acquisition unit comprises an intelligent writing pen and a writing photographing device, wherein wireless communication connection is established between the intelligent writing pen and the writing photographing device:
The intelligent writing pen collects the writing tremble times and the writing pressure of a patient in real time in a unit time;
comparing the writing tremble times and the writing pressure with a preset writing tremble times threshold and a preset writing pressure threshold respectively;
When the writing tremble times exceeds a preset writing tremble times threshold, but the writing pressure does not exceed the writing pressure threshold, triggering a writing photographing device to photograph through an intelligent writing pen, and obtaining a first writing image;
When the writing tremble times do not exceed a preset writing tremble times threshold, but the writing pressure exceeds a writing pressure threshold, comprehensively judging whether to trigger a writing photographing device to photograph or not according to the writing tremble times and corresponding writing pressure values;
when the writing tremble times and the writing pressure do not exceed the corresponding writing tremble times threshold and writing pressure threshold, controlling a writing photographing device to photograph according to a preset image acquisition frequency;
And setting the evaluation weight value of the first writing image to be higher than the corresponding evaluation weight value of the second writing image.
Preferably, the method for comprehensively judging whether to trigger the writing photographing device to photograph by the corresponding values of the writing tremble times and the writing pressure comprises the following steps:
When the writing pressure exceeds a writing pressure threshold, extracting the writing tremble times corresponding to unit time when the writing tremble times do not exceed a preset writing tremble times threshold, and taking the writing tremble times as a target tremble times data set;
Generating a comprehensive writing state parameter by using the target tremble frequency data set and the writing pressure, wherein the comprehensive writing state parameter is used for evaluating whether the writing state of the current patient has the necessity of image acquisition or not;
Wherein, the comprehensive writing state parameter is obtained by the following formula:
The method comprises the steps of S representing comprehensive writing state parameters, P representing writing pressure corresponding to a writing pressure threshold, Py representing the writing pressure threshold, N representing the number of data contained in a target trembling frequency data set, wherein the number of data is consistent with the number of unit time when the number of the writing trembling frequency does not exceed a preset writing trembling frequency threshold, Ci representing the number of trembling frequencies corresponding to the ith trembling data in the target trembling frequency data set, N representing the number of unit time which a patient has written, N0 representing a preset unit time number reference value, and Cy representing a preset writing trembling frequency threshold;
Comparing the comprehensive writing state parameter with a preset parameter threshold;
when the comprehensive writing state parameter is lower than a preset parameter threshold, judging that the writing photographing device is not required to be triggered to photograph;
When the comprehensive writing state parameter is not lower than a preset parameter threshold, judging that the writing photographing device needs to be triggered to photograph;
Taking a writing image obtained when the writing tremble times do not exceed a preset writing tremble times threshold value but the writing pressure exceeds a writing pressure threshold value as a third writing image;
and setting the evaluation weight value of the third writing image to be higher than the evaluation weight value corresponding to the second writing image, but lower than the evaluation weight value corresponding to the first writing image.
Preferably, the gait data comprises step size, step width, step frequency, step speed, swing phase, support phase, gait symmetry, gait variability, torso swing, arm swing and cornering gait;
the balance data comprise standing balance, sitting balance, gravity center swing and posture stability;
the finger knocking data comprise knocking frequency, rhythm, force and finger coordination;
the voice features include fundamental frequency, sound intensity, sound length, speech speed, pause, intonation, pronunciation clarity and voice fluency;
The voice content comprises vocabulary, grammar structure, semantic expression and emotion expression;
writing dynamics data including writing speed, acceleration and pressure, stroke length, width and angle, writing pause and tremble;
The writing graphic data comprises font size, shape and spacing, handwriting integrity and consistency;
Facial motion data includes movements of eyebrows, eyes, nose, and mouth portions;
facial expression data includes expression change frequency, amplitude, and duration.
Preferably, the quantitative diagnosis platform comprises a behavior data preprocessing unit and an early diagnosis evaluation unit;
The behavior data preprocessing unit is configured to clean, transform, integrate and extract characteristics of real-time data of patient behaviors based on the Internet of things, and determine characteristic data of the patient behaviors based on the Internet of things;
the early diagnosis evaluation unit is configured to analyze the patient behavior characteristic data based on the Internet of things, conduct parkinsonism quantitative early diagnosis on the patient, and conduct intelligent management and control treatment on the patient according to the diagnosis result of the patient.
Preferably, the behavior data preprocessing unit comprises a behavior data cleaning module and a behavior data conversion module;
the behavior data cleaning module is configured to clean real-time data of patient behaviors based on the Internet of things;
the method comprises the steps of checking real-time data of patient behaviors based on the Internet of things based on a Python library, identifying repeated values, missing values and abnormal values in the real-time data of the patient behaviors based on the Internet of things, and deleting the repeated values, the missing values and the abnormal values in the real-time data of the patient behaviors based on the Internet of things;
the behavior data transformation module is configured to transform the real-time data of the patient behavior based on the Internet of things;
The method comprises the steps of normalizing real-time data of patient behaviors based on the Internet of things based on Z-Score standardization, converting the real-time data of the patient behaviors based on the Internet of things into standard normal distribution with a mean value of 0 and a standard deviation of 1, and removing dimension differences among the real-time data of the patient behaviors based on the Internet of things.
Preferably, the behavior data preprocessing unit further comprises a behavior data integrating module and a behavior data extracting module;
the behavior data integration module is configured to integrate real-time data of patient behaviors based on the Internet of things;
Integrating the real-time data of the patient behaviors based on the Internet of things based on the Python library, integrating the real-time data of the patient behaviors based on the Internet of things into a unified data view, verifying the integrated real-time data of the patient behaviors based on the Internet of things, and judging whether the integrated real-time data of the patient behaviors based on the Internet of things is absent;
The behavior data extraction module is configured to extract real-time data of patient behaviors based on the Internet of things;
The method comprises the steps of extracting characteristics related to Parkinson's disease from real-time data of patient behaviors based on the Internet of things, carrying out weighted fusion on the extracted characteristic vectors, and determining the patient behavior characteristic data based on the Internet of things.
Preferably, the early diagnosis evaluation unit comprises a diagnosis model training module, a diagnosis model analysis module and a diagnosis model analysis module, wherein the diagnosis model training module is configured to train a parkinsonism quantitative early diagnosis model;
Collecting patient behavior historical data based on the Internet of things according to parkinsonism quantification early diagnosis requirements based on behavior feature recognition, dividing the collected patient behavior historical data based on the Internet of things, and dividing the patient behavior historical data based on the Internet of things into a training set and a testing set;
based on a deep learning technology, training a deep learning model by adopting a training set, enabling the deep learning model to autonomously learn a parkinsonism quantitative early diagnosis process based on behavior feature recognition, and determining a parkinsonism quantitative early diagnosis model based on the behavior feature recognition;
Based on a test set, testing the parkinsonism quantitative early diagnosis model based on behavior feature recognition, evaluating the performance of the parkinsonism quantitative early diagnosis model based on the behavior feature recognition based on the accuracy, recall rate and F1 value index, and judging whether the parkinsonism quantitative early diagnosis model based on the behavior feature recognition can achieve the expected effect;
And adjusting parameters and structures of the parkinsonism quantitative early diagnosis model based on behavior feature recognition according to the test evaluation result, and determining an optimal parkinsonism quantitative early diagnosis model through continuous iterative optimization.
Preferably, the early diagnosis and evaluation unit further comprises a diagnosis and evaluation management and control module, wherein the diagnosis and evaluation management and control module is configured to conduct parkinsonism quantitative early diagnosis and evaluation management and control on a patient;
acquiring an optimal parkinsonism quantitative early diagnosis model, and disposing the optimal parkinsonism quantitative early diagnosis model in an actual parkinsonism quantitative early diagnosis environment based on behavior feature recognition;
Inputting the patient behavior characteristic data based on the Internet of things into an optimal parkinsonism quantitative early diagnosis model, analyzing the patient behavior characteristic data based on the Internet of things based on the optimal parkinsonism quantitative early diagnosis model, performing parkinsonism quantitative early diagnosis on a patient, and determining a patient diagnosis result;
the system comprises a patient diagnosis module, a patient monitoring module and a patient monitoring module, wherein the patient behavior characteristic data are analyzed based on the patient diagnosis result, a patient condition evaluation report is determined, intelligent management and control treatment is carried out on the patient based on the patient condition evaluation report, the treatment effect is monitored in real time, and the management and control treatment scheme of the patient is dynamically adjusted according to the monitoring condition.
Compared with the prior art, the invention has the beneficial effects that:
According to the invention, the real-time data of the patient behaviors based on the Internet of things are determined by collecting the patient motion data, the patient voice data, the patient writing data and the patient facial data, the real-time data of the patient behaviors based on the Internet of things are cleaned, transformed, integrated and extracted, the characteristic data of the patient behaviors based on the Internet of things are determined, the parkinsonism quantitative early diagnosis model is trained based on the deep learning technology, the characteristic data of the patient behaviors based on the Internet of things are analyzed based on the parkinsonism quantitative early diagnosis model, the parkinsonism quantitative early diagnosis is carried out on the patient, the patient diagnosis result is determined, the characteristic data of the patient behaviors are analyzed based on the patient diagnosis result, the condition evaluation report of the patient is determined, the intelligent management and control treatment is carried out on the patient based on the condition evaluation report of the patient, the real-time monitoring is carried out on the treatment effect, the management and control treatment scheme of the patient is dynamically adjusted according to the monitoring condition, the parkinsonism quantitative early diagnosis is carried out on the patient based on the behavior characteristic identification, and the parkinsonism patient diagnosis effect can be improved.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In order to solve the problem that the existing method cannot perform parkinsonian quantitative early diagnosis on a patient based on behavior feature recognition, resulting in poor diagnosis effect on parkinsonian patients, referring to fig. 1, the present embodiment provides the following technical scheme:
The parkinsonism quantitative early diagnosis system based on behavior feature recognition comprises intelligent acquisition equipment and a quantitative diagnosis platform.
The intelligent acquisition equipment is used for acquiring patient motion data, patient voice data, patient writing data and patient face data, determining patient behavior real-time data based on the Internet of things, and transmitting the acquired patient behavior real-time data based on the Internet of things to the quantitative diagnosis platform based on a wireless transmission technology;
in this embodiment, the intelligent acquisition device includes:
The motion acquisition unit is used for monitoring and acquiring gait data, balance data and finger knocking data of a patient in real time based on the internet of things technology to acquire motion data of the patient;
The gait data include step size, step width, step frequency, step speed, swing phase, support phase, gait symmetry, gait variability, torso swing, arm swing and cornering gait.
The balance data includes standing balance, sitting balance, center of gravity swing, and posture stability.
It should be noted that the finger knocking data includes knocking frequency, rhythm, dynamics and finger coordination.
The voice acquisition unit is used for monitoring and acquiring voice characteristics and voice contents of a patient in real time based on the internet of things technology to acquire voice data of the patient;
It should be noted that the speech features include fundamental frequency, intensity, duration, speech speed, pause, intonation, articulation clarity, and speech fluency.
It should be noted that, the voice content includes vocabulary, grammar structure, semantic expression and emotion expression;
the writing acquisition unit is used for monitoring and acquiring writing dynamics data and writing graphic data of a patient in real time based on the internet of things technology to acquire writing data of the patient;
the writing dynamics data includes writing speed, acceleration and pressure, stroke length, width and angle, writing pause and tremble.
The writing graphic data comprises font size, shape and interval, handwriting integrity and consistency;
The facial acquisition unit is used for monitoring and acquiring facial action data and facial expression data of a patient in real time based on the internet of things technology to acquire the facial data of the patient;
The facial motion data includes movements of eyebrows, eyes, nose, and mouth.
It should be noted that the facial expression data includes expression change frequency, amplitude and duration.
And determining real-time data of the patient behavior based on the Internet of things according to the patient motion data, the patient voice data, the patient writing data and the patient face data.
Specifically, by collecting patient motion data, patient voice data, patient writing data and patient face data, real-time patient behavior data based on the Internet of things is determined, and data support can be provided for early diagnosis of parkinsonism quantification of a patient.
The quantitative diagnosis platform is used for preprocessing and analyzing real-time data of patient behaviors based on the Internet of things, performing parkinsonism quantitative early diagnosis on a patient, and performing intelligent management and control treatment on the patient according to a patient diagnosis result.
Specifically, write the collection unit, write the pen including intelligence and write and shoot the device, wherein, the intelligence write the pen with write and shoot and establish wireless communication connection between the device:
The intelligent writing pen collects the writing tremble times and the writing pressure of a patient in real time in a unit time;
comparing the writing tremble times and the writing pressure with a preset writing tremble times threshold and a preset writing pressure threshold respectively;
When the writing tremble times exceeds a preset writing tremble times threshold, but the writing pressure does not exceed the writing pressure threshold, triggering a writing photographing device to photograph through an intelligent writing pen, and obtaining a first writing image;
When the writing tremble times do not exceed a preset writing tremble times threshold, but the writing pressure exceeds a writing pressure threshold, comprehensively judging whether to trigger a writing photographing device to photograph or not according to the writing tremble times and corresponding writing pressure values;
when the writing tremble times and the writing pressure do not exceed the corresponding writing tremble times threshold and writing pressure threshold, controlling a writing photographing device to photograph according to a preset image acquisition frequency;
And setting the evaluation weight value of the first writing image to be higher than the corresponding evaluation weight value of the second writing image.
The technical effect of the technical scheme is that the intelligent writing pen can be used for collecting the writing tremble times and the writing pressure of a patient in real time, so that the collection rate of the writing state information of the patient and the timeliness of data acquisition can be effectively improved. Meanwhile, whether the writing photographing device is triggered to photograph or not is judged according to the preset writing tremble frequency threshold and the writing pressure threshold, so that the accuracy of judging abnormal conditions in the writing process of a captured patient can be effectively improved, the necessity of triggering is improved, and the problem that important and key writing images cannot be successfully captured when the image photographing is carried out according to the fixed photographing frequency is prevented.
When one of the number of writing tremors or writing pressure exceeds a threshold, the system may take a different photographing strategy. And when the number of the writing judder exceeds the threshold value, shooting is carried out according to the preset image acquisition frequency. The shooting triggering strategy can effectively improve shooting pertinence, and further improve accuracy of subsequent parkinsonism assessment. On the other hand, the system sets the evaluation weight value of the first written image (i.e., the photographing triggered under the specific condition) to be higher than the corresponding evaluation weight value of the second written image (i.e., the image photographed at the preset frequency). The weight assignment method can be used for focusing on key image data in the analysis process, so that the accuracy and the effectiveness of evaluation are improved.
Specifically, whether the writing photographing device is triggered to photograph or not is comprehensively judged through corresponding values of the writing tremble times and the writing pressure, and the method comprises the following steps:
When the writing pressure exceeds a writing pressure threshold, extracting the writing tremble times corresponding to unit time when the writing tremble times do not exceed a preset writing tremble times threshold, and taking the writing tremble times as a target tremble times data set;
Generating a comprehensive writing state parameter by using the target tremble frequency data set and the writing pressure, wherein the comprehensive writing state parameter is used for evaluating whether the writing state of the current patient has the image acquisition necessity or not;
Wherein, the comprehensive writing state parameter is obtained by the following formula:
The method comprises the steps of S representing comprehensive writing state parameters, P representing writing pressure corresponding to a writing pressure threshold, Py representing the writing pressure threshold, N representing the number of data contained in a target trembling frequency data set, wherein the number of data is consistent with the number of unit time when the number of the writing trembling frequency does not exceed a preset writing trembling frequency threshold, Ci representing the number of trembling frequencies corresponding to the ith trembling data in the target trembling frequency data set, N representing the number of unit time which a patient has written, N0 representing a preset unit time number reference value, and Cy representing a preset writing trembling frequency threshold;
Comparing the comprehensive writing state parameter with a preset parameter threshold;
when the comprehensive writing state parameter is lower than a preset parameter threshold, judging that the writing photographing device is not required to be triggered to photograph;
When the comprehensive writing state parameter is not lower than a preset parameter threshold, judging that the writing photographing device needs to be triggered to photograph;
Taking a writing image obtained when the writing tremble times do not exceed a preset writing tremble times threshold value but the writing pressure exceeds a writing pressure threshold value as a third writing image;
and setting the evaluation weight value of the third writing image to be higher than the evaluation weight value corresponding to the second writing image, but lower than the evaluation weight value corresponding to the first writing image.
The technical effect of the technical scheme is that the technical scheme can evaluate the writing state of the patient more finely by introducing the comprehensive writing state parameter S. The accuracy of the evaluation of the real writing situation of the patient can be effectively improved by comprehensively evaluating the writing judder times and the writing pressure. Meanwhile, the judder times and pressure changes in different unit time in the writing process of the patient are utilized in the technical scheme, the comprehensive writing state parameters are obtained by extracting the target judder times data set, the following performance between the comprehensive writing state parameters and the fine changes of the writing state of the patient can be effectively improved, and further the capturing sensitivity and the capturing accuracy of the fine changes of the writing state of the patient are effectively improved. Meanwhile, in the technical scheme, weight assignment is carried out on the writing images acquired under different conditions. The evaluation weight value of the third written image is higher than that of the second written image but lower than that of the first written image, the weight assignment mode not only considers the importance difference of the images under different conditions, but also keeps the rationality and continuity of weight assignment, and the importance distinction degree of the first written image, the second written image and the third written image can be effectively improved, so that the accuracy of early judgment of subsequent Parkinson is improved.
In the embodiment, the quantitative diagnosis platform comprises a behavior data preprocessing unit and an early diagnosis evaluation unit.
The behavior data preprocessing unit is used for cleaning, transforming, integrating and extracting characteristics of the patient behavior real-time data based on the Internet of things, and determining patient behavior characteristic data based on the Internet of things;
it should be noted that, the behavior data preprocessing unit includes:
The behavior data cleaning module is used for cleaning the real-time data of the patient behaviors based on the Internet of things;
the method comprises the steps of checking real-time data of patient behaviors based on the Internet of things based on a Python library, identifying repeated values, missing values and abnormal values in the real-time data of the patient behaviors based on the Internet of things, and deleting the repeated values, the missing values and the abnormal values in the real-time data of the patient behaviors based on the Internet of things;
the behavior data transformation module is used for transforming the real-time data of the patient behavior based on the Internet of things;
the method comprises the steps of normalizing real-time data of patient behaviors based on the Internet of things based on Z-Score standardization, converting the real-time data of the patient behaviors based on the Internet of things into standard normal distribution with a mean value of 0 and a standard deviation of 1, and removing dimension differences among the real-time data of the patient behaviors based on the Internet of things;
the behavior data integration module is used for integrating the real-time data of the patient behaviors based on the Internet of things;
Integrating the real-time data of the patient behaviors based on the Internet of things based on the Python library, integrating the real-time data of the patient behaviors based on the Internet of things into a unified data view, verifying the integrated real-time data of the patient behaviors based on the Internet of things, and judging whether the integrated real-time data of the patient behaviors based on the Internet of things is absent;
the behavior data extraction module is used for extracting real-time data of patient behaviors based on the Internet of things;
The method comprises the steps of extracting characteristics related to Parkinson's disease from real-time data of patient behaviors based on the Internet of things, carrying out weighted fusion on the extracted characteristic vectors, and determining the patient behavior characteristic data based on the Internet of things.
Specifically, patient behavior characteristic data based on the Internet of things is determined by cleaning, transforming, integrating and extracting characteristics of the patient behavior real-time data based on the Internet of things, wherein the patient behavior characteristic data comprises gait parameters, voice characteristics, writing dynamics characteristics, facial expression characteristics and the like.
The early diagnosis evaluation unit is used for analyzing the patient behavior characteristic data based on the Internet of things, carrying out parkinsonism quantitative early diagnosis on the patient, and carrying out intelligent management and control treatment on the patient according to the diagnosis result of the patient.
The early diagnosis evaluation unit includes:
The diagnosis model training module is used for training an early diagnosis model of parkinsonism quantification;
Collecting patient behavior historical data based on the Internet of things according to parkinsonism quantification early diagnosis requirements based on behavior feature recognition, dividing the collected patient behavior historical data based on the Internet of things, and dividing the patient behavior historical data based on the Internet of things into a training set and a testing set;
based on a deep learning technology, training a deep learning model by adopting a training set, enabling the deep learning model to autonomously learn a parkinsonism quantitative early diagnosis process based on behavior feature recognition, and determining a parkinsonism quantitative early diagnosis model based on the behavior feature recognition;
Based on a test set, testing the parkinsonism quantitative early diagnosis model based on behavior feature recognition, evaluating the performance of the parkinsonism quantitative early diagnosis model based on the behavior feature recognition based on the accuracy, recall rate and F1 value index, and judging whether the parkinsonism quantitative early diagnosis model based on the behavior feature recognition can achieve the expected effect;
Parameters and structures of the parkinsonism quantitative early diagnosis model based on behavior feature recognition are adjusted according to the test evaluation result, and the optimal parkinsonism quantitative early diagnosis model is determined through continuous iterative optimization;
the diagnosis and evaluation management and control module is used for carrying out parkinsonism quantitative early diagnosis and evaluation management and control on patients;
acquiring an optimal parkinsonism quantitative early diagnosis model, and disposing the optimal parkinsonism quantitative early diagnosis model in an actual parkinsonism quantitative early diagnosis environment based on behavior feature recognition;
Inputting the patient behavior characteristic data based on the Internet of things into an optimal parkinsonism quantitative early diagnosis model, analyzing the patient behavior characteristic data based on the Internet of things based on the optimal parkinsonism quantitative early diagnosis model, performing parkinsonism quantitative early diagnosis on a patient, and determining a patient diagnosis result;
the system comprises a patient diagnosis module, a patient monitoring module and a patient monitoring module, wherein the patient behavior characteristic data are analyzed based on the patient diagnosis result, a patient condition evaluation report is determined, intelligent management and control treatment is carried out on the patient based on the patient condition evaluation report, the treatment effect is monitored in real time, and the management and control treatment scheme of the patient is dynamically adjusted according to the monitoring condition.
Therefore, based on the parkinsonism quantitative early diagnosis model, the patient behavior feature data based on the Internet of things are analyzed, parkinsonism quantitative early diagnosis is carried out on the patient, the patient diagnosis result is determined, based on the patient diagnosis result, the patient behavior feature data is analyzed, the patient condition evaluation report is determined, intelligent management and control treatment is carried out on the patient based on the patient condition evaluation report, the treatment effect is monitored in real time, the management and control treatment scheme of the patient is dynamically adjusted according to the monitoring condition, parkinsonism quantitative early diagnosis can be carried out on the patient based on the behavior feature identification, and the parkinsonism patient diagnosis effect can be improved.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.