1. A method for assessing the mental health status of college students using machine learning and image analysis, comprising the steps of: collecting facial expression and body language data from a sample of college students; training a machine learning model using the collected data; and using the trained model to predict the mental health status of new college students based on their image data.

2. The method of claim 1, wherein the collected image data includes facial expression and body language data captured through video recordings or image captures.

3. The method of claim 1, wherein the machine learning model uses supervised learning techniques to identify patterns in the image data associated with specific mental health conditions.

4. The method of claim 1, wherein the machine learning model uses feature extraction and selection techniques to identify the most important facial expression and body language features associated with specific mental health conditions.

5. The method of claim 1, wherein the machine learning model uses natural language processing techniques to analyze speech data associated with the captured images.

6. A system for assessing the mental health status of college students using machine learning and image analysis, comprising: a data collection module for collecting facial expression and body language data from college students; a machine learning module for training a machine learning model using the collected data; and a prediction module for using the trained model to predict the mental health status of new college students based on their image data.

7. The system of claim 6, wherein the data collection module includes a camera or a mobile application that captures video or images of college students during their daily activities.

8. The system of claim 6, wherein the machine learning module includes a cloud-based platform that enables the use of scalable machine learning algorithms.

9. The system of claim 6, wherein the prediction module provides personalized feedback and resources to college students based on their predicted mental health status.  DERIVING PSYCHOLOGICAL STATE ASSESSMENT OF STUDENT VIA BEHAVIOUR PATTERNS USING ARTIFICIAL INTELLIGENCE Abstract A college student's mental health may be evaluated with the use of machine learning and image analysis, according to one embodiment of the present disclosure. The technique involves gathering facial expression and body language data from a representative student population. In certain embodiments, the acquired data is used to train a machine learning model. The trained algorithm might potentially be used to make predictions about the mental health of incoming college students using photographs. , Claims:Claims :

1. A method for assessing the mental health status of college students using machine learning and image analysis, comprising the steps of: collecting facial expression and body language data from a sample of college students; training a machine learning model using the collected data; and using the trained model to predict the mental health status of new college students based on their image data.

2. The method of claim 1, wherein the collected image data includes facial expression and body language data captured through video recordings or image captures.

3. The method of claim 1, wherein the machine learning model uses supervised learning techniques to identify patterns in the image data associated with specific mental health conditions.

4. The method of claim 1, wherein the machine learning model uses feature extraction and selection techniques to identify the most important facial expression and body language features associated with specific mental health conditions.

5. The method of claim 1, wherein the machine learning model uses natural language processing techniques to analyze speech data associated with the captured images.

6. A system for assessing the mental health status of college students using machine learning and image analysis, comprising: a data collection module for collecting facial expression and body language data from college students; a machine learning module for training a machine learning model using the collected data; and a prediction module for using the trained model to predict the mental health status of new college students based on their image data.

7. The system of claim 6, wherein the data collection module includes a camera or a mobile application that captures video or images of college students during their daily activities.

8. The system of claim 6, wherein the machine learning module includes a cloud-based platform that enables the use of scalable machine learning algorithms.

9. The system of claim 6, wherein the prediction module provides personalized feedback and resources to college students based on their predicted mental health status.

Description:DERIVING PSYCHOLOGICAL STATE ASSESSMENT OF STUDENT VIA BEHAVIOUR PATTERNS USING ARTIFICIAL INTELLIGENCE
Field of the Invention
[0001] The present invention relates generally to mental health analysis, more particularly to a system and method to evaluate a mental health of a student.
Background
[0002] The background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[0003] Psychological state assessment of students is an important area of research in the field of psychology and education. It involves evaluating the mental and emotional state of students in order to better understand their needs and support their academic and personal growth.
[0004] There are several approaches to psychological state assessment, including self-report measures, behavioral observation, and psychological testing. Self-report measures involve asking students to provide information about their thoughts, feelings, and behaviors through questionnaires or interviews. Behavioral observation involves directly observing the student in their natural environment, such as the classroom or playground, and noting their behaviors and interactions. Psychological testing involves administering standardized tests to assess various aspects of the student's psychological functioning, such as intelligence, personality, and emotional well-being.
[0005] Research on psychological state assessment of students has focused on several key areas, including identifying risk factors for mental health problems, evaluating the effectiveness of interventions for students with mental health concerns, and developing tools and strategies for early detection and prevention of mental health issues. Other important areas of research include understanding the relationship between psychological state and academic performance, as well as the impact of stress and trauma on students' psychological well-being.
[0006] In recent years, there has been increasing interest in using technology-based approaches to psychological state assessment, such as mobile apps and wearable devices, which offer the potential for more frequent and accurate monitoring of students' mental and emotional states. However, there are also concerns about the privacy and ethical implications of these technologies, and further research is needed to fully understand their potential benefits and drawbacks.
[0007] Overall, the field of psychological state assessment of students is a dynamic and rapidly evolving area of research, with significant implications for both education and mental health.
[0008] Various technological solutions (e.g., self-report measures, behavioral assessments, discerning psychological state from correlated user behaviour and contextual information, system for determining psychological state using sensing device and method thereof, etc.) are disclosed in patent literature. Few of the exemplary documents are discussed below.
[0009] US10623431B2 (By: FORCEPOINT) relates to method, system and computer-usable medium for performing a psychological profile operation. The psychological profile operation includes: monitoring user interactions between a user and an information handling system; converting the user interactions into electronic information representing the user interactions; determining when the user interactions are associated with generation of an electronic communication; associating the user interactions with the electronic communication; and, generating a psychological profile of the user based upon the user interactions and the electronic communication, the psychological profile comprising information regarding a psychological state of the user.
[00010] KR101689021B1 (By: INFOSHARE) relates to a system for determining a psychological state using a sensing device, and to a method thereof. According to the present invention, the system for determining a psychological state using a sensing device comprises: at least one sensing device for measuring at least one among motion information, bio-signal information, and voice information of a subject, with respect to the subject who is counseling or interviewing; a state type analysis unit for filtering at least one state type information corresponding to a current state of the subject by using measured data of the at least one sensing device, based on multiple state type information which can be classified by the measured data for each factor of the measurement; a database unit for storing a psychology classification table in which with respect to multiple heterologous psychological states, state type information corresponding to each of the psychological states from the multiple state type information is individually mapped; and a psychological state extraction unit for extracting at least one psychological state of the subject and providing the same by matching the filtered at least one state type information with the psychology classification table. According to the system for determining a psychological state using a sensing device and a method thereof, behavior patterns of the subject during counseling or interview can be monitored and analyzed in real-time to deduce a psychological state corresponding to the behavior patterns of the subject more objectively, and to improve accuracy of determining the psychological state.
[00011] CN112086169A (By: BEIJING XINLINGLILIANG TECHNOLOGY) provides an interactive psychological counseling system adopting psychological data labeling modeling. The system comprises a multi-source data acquisition port, a psychological state detection engine, a psychological state feature extraction module, a psychological state detection model, a psychological counseling model training data construction subsystem, a psychological counseling model construction subsystem, a psychological state threshold adjustment subsystem and a psychological counseling result output subsystem, wherein the multi-source data acquisition port acquires psychological state information of the user in unit time from a plurality of data sources; the psychological state detection engine obtains a psychological state detection level of the current user; thepsychological state detection model outputs vectorized index vectors of the psychological state characteristics of the user; the psychological counseling model construction subsystem is used for training the psychological counseling model based on the training data of the psychological counseling model; the psychological state threshold adjustment subsystem is used for adjusting the psychologicalstate threshold of the psychological state detection engine; and the psychological counseling result output subsystem is used for outputting psychological counseling results.
[00012] CN110729049A (By: GUANGZHOU CLOUD BUTTERFLY TECHNOLOGY) provides an early warning method of mental health, including the following steps: acquiring classroom video and audio data of multiple students in different subjects within a preset first duration, wherein the classroom video and audio data include emotional information and body movements; obtaining a first curve of each student's emotion changing with the time according to the emotional information of each student; obtaining a second curve of each student's body movements changing with the time according to the body movements of each student; judging whether the student has psychological abnormality according to the first curve and/or the second curve of each student; generating a first warning prompt message when the student has psychological abnormality; and sending the first warning prompt message to a terminal. Therefore, the early warning of abnormal psychology is realized, so that parents and teachers can pay attention to abnormal conditions in advance so as to promote the healthy development of students and improving the teaching effect.
[00013] However, the technological solutions for trait detection suffers from various limitations such as, expensive, inaccuracy, etc. Therefore, more advancement in this field of technology is required. More specifically, to a system and method to evaluate behavior based mental health of a college student.
[00014] All references, including publications, patent applications, and patents, cited herein are hereby incorporated by reference to the same extent as if each reference were individually and specifically indicated to be incorporated by reference and were set forth in its entirety herein.

Summary
[00015] The present invention relates generally to mental health analysis, more particularly to a system and method to evaluate a mental health of a student.
[00016] This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter. Furthermore, the claimed subject matter is not limited to implementations that solve any or all disadvantages noted in any part of this disclosure.
[00017] The following paragraphs provide additional support for the claims of the subject application.
[00018] Embodiments of the present disclosure may include a method for assessing the mental health status of college students using machine learning and image analysis, including the steps of collecting facial expression and body language data from a sample of college students. Embodiments may also include training a machine learning model using the collected data. Embodiments may also include using the trained model to predict the mental health status of new college students based on their image data.
[00019] In some embodiments, the collected image data includes facial expression and body language data captured through video recordings or image captures. In some embodiments, the machine learning model uses supervised learning techniques to identify patterns in the image data associated with specific mental health conditions. In some embodiments, the machine learning model uses feature extraction and selection techniques to identify the most important facial expression and body language features associated with specific mental health conditions. In some embodiments, the machine learning model uses natural language processing techniques to analyze speech data associated with the captured images.
[00020] Embodiments of the present disclosure may also include a system for assessing the mental health status of college students using machine learning and image analysis, including a data collection module for collecting facial expression and body language data from college students. Embodiments may also include a machine learning module for training a machine learning model using the collected data. Embodiments may also include a prediction module for using the trained model to predict the mental health status of new college students based on their image data.
[00021] In some embodiments, the data collection module includes a camera or a mobile application that captures video or images of college students during their daily activities. In some embodiments, the machine learning module includes a cloud-based platform that enables the use of scalable machine learning algorithms. In some embodiments, the prediction module provides personalized feedback and resources to college students based on their predicted mental health status.
Brief Description of the Drawings
[00022] Embodiments will now be described in more detail in relation to the enclosed drawings, in which:
[00023] FIG. 1 is a flowchart illustrating a method for assessing the behavior based mental health status of college students, according to some embodiments of the present disclosure.
[00024] FIG. 2 is a block diagram illustrating a system for behavior based mental health status analysis, according to some embodiments of the present disclosure.
[00025] FIG. 3 is a block diagram further illustrating the detailed system of FIG. 2, according to some embodiments of the present disclosure.

Detailed Description
[00026] The following is a detailed description of exemplary embodiments to illustrate the principles of the invention. The embodiments are provided to illustrate aspects of the invention, but the invention is not limited to any embodiment. The scope of the invention encompasses numerous alternatives, modifications and equivalent; it is limited only by the claims.
[00027] Numerous specific details are set forth in the following description in order to provide a thorough understanding of the invention. However, the invention may be practiced according to the claims without some or all of these specific details. For the purpose of clarity, technical material that is known in the technical fields related to the invention has not been described in detail so that the invention is not unnecessarily obscured.
[00028] The present invention relates generally to mental health analysis, more particularly to a system and method to evaluate a mental health of a college student.
[00029] FIG. 1 is a flowchart that describes a method for assessing the mental health status of college students, according to some embodiments of the present disclosure. In some embodiments, at 110, the method may include collecting facial expression and body language data from a sample of college students. At 120, the method may include training a machine learning model using the collected data. At 130, the method may include using the trained model to predict the mental health status of new college students based on their image data.
[00030] In some embodiments, the collected image data may include facial expression and body language data captured through video recordings or image captures. The machine learning model may use supervised learning techniques to identify patterns in the image data associated with specific mental health conditions. The machine learning model may use feature extraction and selection techniques to identify the most important facial expression and body language features associated with specific mental health conditions. In some embodiments, the machine learning model may use natural language processing techniques to analyze speech data associated with the captured images.
[00031] FIG. 2 is a block diagram that describes a system 200, according to some embodiments of the present disclosure. In some embodiments, the system 200 may include a data collection module 210 for collecting facial expression and body language data from college students, a machine learning module 220 for training a machine learning model using the collected data, and a prediction module 230 for using the trained model to predict the mental health status of new college students based on their image data. The machine learning module 220 may include a cloud-based platform that enables the use of scalable machine learning algorithms. The prediction module 230 may provide personalized feedback and resources to college students based on their predicted mental health status.
[00032] FIG. 3 is a block diagram that further describes the system 200 from FIG. 2, according to some embodiments of the present disclosure. In some embodiments, the data collection module 210 may include a camera 312 and a mobile application 314 that captures video or images of college students during their daily activities.
[00033] A college student's mental health may be evaluated with the use of machine learning and image analysis, according to one embodiment of the present disclosure. The technique involves gathering facial expression and body language data from a representative student population. In certain embodiments, the acquired data is used to train a machine learning model. The trained algorithm might potentially be used to make predictions about the mental health of incoming college students using just their photographs.
[00034] In certain implementations, video recordings or still images are used to gather data on facial expressions and body language. The machine learning model may, in certain implementations, use supervised learning strategies to spot telltale signs of particular mental health problems in the aforementioned visual data. In certain implementations, the machine learning model extracts and selects the most salient facial expression and body language variables that are connected with distinct mental health problems. In other implementations, the machine learning model analyses the collected pictures and voice data using natural language processing algorithms.
[00035] A data collection module for collecting facial expression and body language data from college students is one example of an embodiment that may be used in a system for evaluating the mental health condition of college students using machine learning and image analysis. In certain implementations, the data gathered is used to train a machine learning model. In other embodiments, a module dedicated to prediction is included so that the trained model may be used to make inferences about the mental health state of incoming college students using just their picture data.
[00036] In some implementations, the data gathering module is a camera or mobile app that records pupils going about their normal routines. A cloud-based platform that supports scalable machine learning algorithms is included in certain implementations of the machine learning module. There are implementations of the prediction module that tailor information and support to each individual student depending on their projected psychological well-being in higher education.
[00037] A number of implementations have been described. Nevertheless, various modifications may be made without departing from the spirit and scope of the invention. In addition, the logic flows depicted in the figures do not require the particular order shown, or sequential order, to achieve desirable results. In addition, other steps may be provided, or steps may be eliminated, from the described flows, and other components may be added to, or removed from, the described systems. Accordingly, other implementations are within the scope of the following claims.
[00038] Various techniques may be described herein in the general context of software, hardware elements, or program modules. Generally, such modules include routines, programs, objects, elements, components, data structures, and so forth that perform particular tasks or implement particular abstract data types. The terms module, functionality, and component as used herein generally represent software, firmware, hardware, or a combination thereof. The features of the techniques described herein are platform-independent, meaning that the techniques may be implemented on a variety of commercial computing platforms having a variety of processors Executable instructions stored on the computer-readable media or memory can include, for example, an operating system, a data management framework , and/or other modules, programs, or applications that are loadable and executable by the processor(s) or any appropriate hardware logic components/CPU(s).
[00039] It will be obvious to a person skilled in the art that, as the technology advances, the inventive concept can be implemented in various ways. The above described embodiments are given for describing rather than limiting the disclosure, and it is to be understood that modifications and variations may be resorted to without departing from the spirit and scope of the disclosure as those skilled in the art readily understand. Such modifications and variations are considered to be within the scope of the disclosure and the appended claims. The protection scope of the disclosure is defined by the accompanying claims.
[00040] Conditional language such as, among others, include, including, comprise, comprising, can, could, might or may, unless specifically stated otherwise, is understood within the context to present that certain examples include, while other examples do not include, certain features, elements and/or steps. Thus, such conditional language is not generally intended to imply that certain features, elements and/or steps are in any way required for one or more examples or that one or more examples necessarily include logic for deciding, with or without user input or prompting, whether certain features, elements and/or steps are included or are to be performed in any particular example. Conjunctive language such as the phrase at least one of X, Y or Z, unless specifically stated otherwise, is to be understood to present that an item, term, etc. may be any of X, Y, or Z, or a combination or sub-combination thereof.As described above, the exemplary embodiment provides both a method and corresponding apparatus consisting of various modules providing functionality for performing the steps of the method. The modules/engines may be implemented as hardware (embodied in one or more chips including an integrated circuit such as an application specific integrated circuit), or may be implemented as software or firmware for execution by a computer processor. In particular, in the case of firmware or software, the exemplary embodiment can be provided as a computer program product including a computer readable storage structure embodying computer program code (i.e., software or firmware) thereon for execution by the computer processor.
[00041] Modifications, additions, or omissions may be made to the systems and apparatuses described herein without departing from the scope of the disclosure. The components of the systems and apparatuses may be integrated or separated. Moreover, the operations of the systems and apparatuses may be performed by more, fewer, or other components. Additionally, operations of the systems and apparatuses may be performed using any suitable logic comprising software, hardware, and/or other logic. As used in this document, each refers to each member of a set or each member of a subset of a set.
[00042] Many different embodiments have been disclosed herein, in connection with the above description and the drawings. It will be understood that it would be unduly repetitious and obfuscating to literally describe and illustrate every combination and sub-combination of these embodiments. Accordingly, all embodiments may be combined in any way and/or combination, and the present specification, including the drawings, shall be construed to constitute a complete written description of all combinations and sub-combinations of the embodiments described herein, and of the manner and process of making and using them, and shall support claims to any such combination or sub-combination.
[00043] The above description is intended to be illustrative, and not restrictive. Although the present disclosure has been described with references to specific illustrative examples and implementations, it will be recognized that the present disclosure is not limited to the examples and implementations described. The scope of the disclosure should be determined with reference to the following claims, along with the full scope of equivalents to which the claims are entitled.

Claims
I/We Claim:
1. A method for assessing the mental health status of college students using machine learning and image analysis, comprising the steps of: collecting facial expression and body language data from a sample of college students; training a machine learning model using the collected data; and using the trained model to predict the mental health status of new college students based on their image data.
2. The method of claim 1, wherein the collected image data includes facial expression and body language data captured through video recordings or image captures.
3. The method of claim 1, wherein the machine learning model uses supervised learning techniques to identify patterns in the image data associated with specific mental health conditions.
4. The method of claim 1, wherein the machine learning model uses feature extraction and selection techniques to identify the most important facial expression and body language features associated with specific mental health conditions.
5. The method of claim 1, wherein the machine learning model uses natural language processing techniques to analyze speech data associated with the captured images.
6. A system for assessing the mental health status of college students using machine learning and image analysis, comprising: a data collection module for collecting facial expression and body language data from college students; a machine learning module for training a machine learning model using the collected data; and a prediction module for using the trained model to predict the mental health status of new college students based on their image data.
7. The system of claim 6, wherein the data collection module includes a camera or a mobile application that captures video or images of college students during their daily activities.
8. The system of claim 6, wherein the machine learning module includes a cloud-based platform that enables the use of scalable machine learning algorithms.
9. The system of claim 6, wherein the prediction module provides personalized feedback and resources to college students based on their predicted mental health status. 

DERIVING PSYCHOLOGICAL STATE ASSESSMENT OF STUDENT VIA BEHAVIOUR PATTERNS USING ARTIFICIAL INTELLIGENCE
Abstract
A college student's mental health may be evaluated with the use of machine learning and image analysis, according to one embodiment of the present disclosure. The technique involves gathering facial expression and body language data from a representative student population. In certain embodiments, the acquired data is used to train a machine learning model. The trained algorithm might potentially be used to make predictions about the mental health of incoming college students using photographs.

, Claims:Claims
I/We Claim:
1. A method for assessing the mental health status of college students using machine learning and image analysis, comprising the steps of: collecting facial expression and body language data from a sample of college students; training a machine learning model using the collected data; and using the trained model to predict the mental health status of new college students based on their image data.
2. The method of claim 1, wherein the collected image data includes facial expression and body language data captured through video recordings or image captures.
3. The method of claim 1, wherein the machine learning model uses supervised learning techniques to identify patterns in the image data associated with specific mental health conditions.
4. The method of claim 1, wherein the machine learning model uses feature extraction and selection techniques to identify the most important facial expression and body language features associated with specific mental health conditions.
5. The method of claim 1, wherein the machine learning model uses natural language processing techniques to analyze speech data associated with the captured images.
6. A system for assessing the mental health status of college students using machine learning and image analysis, comprising: a data collection module for collecting facial expression and body language data from college students; a machine learning module for training a machine learning model using the collected data; and a prediction module for using the trained model to predict the mental health status of new college students based on their image data.
7. The system of claim 6, wherein the data collection module includes a camera or a mobile application that captures video or images of college students during their daily activities.
8. The system of claim 6, wherein the machine learning module includes a cloud-based platform that enables the use of scalable machine learning algorithms.
9. The system of claim 6, wherein the prediction module provides personalized feedback and resources to college students based on their predicted mental health status.