1. A system for analyzing motor activity in children with autism spectrum disorder (ASD) during video games, the system comprising: an input module for receiving video game input data from a user device, wherein the video game input data comprising data about motor activity of the child during gameplay; an AI engine configured to analyse the video game input data and generate a motor activity report to determine cognitive propensity factor, wherein the motor activity report comprises an assessment of the child's motor activity during gameplay; and a user interface for presenting the motor activity report to a user.

2. The system of claim 1, wherein the video game input data comprises one or more of the following data points: keystrokes, mouse clicks, joystick movements, and touchscreen inputs.

3. The system of claim 1, wherein the AI engine comprises a machine learning algorithm trained on a dataset of video game input data from children with ASD.

4. The system of claim 1, wherein the AI engine is configured to generate a cognitive training program based on the child’s specific needs and abilities.

5. The system of claim 1, wherein the cognitive training programs comprise one or more of the following: memory games, attention training, visual processing exercises, and social skills training.

6. The system of claim 1, further comprising a database for storing the video game input data and the motor activity report.

7. A method for analyzing motor activity in children with autism spectrum disorder (ASD) during video games using a computer-implemented system, the method comprising: receiving video game input data from a user device, wherein the video game input data comprising data about motor activity of the child during gameplay; analyzing the video game input data using an AI engine to generate a motor activity report, wherein the motor activity report comprises an assessment of the child's motor activity during gameplay; presenting the motor activity report to a user via a user interface.

8. The method of claim 7, wherein the video game input data comprises one or more of the following data points: keystrokes, mouse clicks, joystick movements, and touchscreen inputs.

9. The method of claim 8, wherein the AI engine comprises a machine learning algorithm trained on a dataset of cognitive assessment data and behavioural data from children with ASD. AI-BASED TECHNIQUE FOR ANALYZING MOTOR ACTIVITY IN CHILDREN WITH AUTISM SPECTRUM DISORDER (ASD) Abstract The present disclosure relates to a system for analysing motor activity in children with autism spectrum disorder (ASD) while they play video games may be included among the embodiments of the present disclosure. This system may include an input module for receiving video game input data from a user device. The video game input data may include information about the child's motor activity. An AI engine that is able to analyse the data that is input into a video game and generate a motor activity report in order to determine a cognitive propensity factor may also be included in embodiments. In certain implementations, the motor activity report could include an analysis of the child's level of motor activity while they were playing a game. A user interface may also be included in embodiments so that a user can be presented with the report of their motor activity. , C , Claims:Claims :

1. A system for analyzing motor activity in children with autism spectrum disorder (ASD) during video games, the system comprising: an input module for receiving video game input data from a user device, wherein the video game input data comprising data about motor activity of the child during gameplay; an AI engine configured to analyse the video game input data and generate a motor activity report to determine cognitive propensity factor, wherein the motor activity report comprises an assessment of the child's motor activity during gameplay; and a user interface for presenting the motor activity report to a user.

2. The system of claim 1, wherein the video game input data comprises one or more of the following data points: keystrokes, mouse clicks, joystick movements, and touchscreen inputs.

3. The system of claim 1, wherein the AI engine comprises a machine learning algorithm trained on a dataset of video game input data from children with ASD.

4. The system of claim 1, wherein the AI engine is configured to generate a cognitive training program based on the child’s specific needs and abilities.

5. The system of claim 1, wherein the cognitive training programs comprise one or more of the following: memory games, attention training, visual processing exercises, and social skills training.

6. The system of claim 1, further comprising a database for storing the video game input data and the motor activity report.

7. A method for analyzing motor activity in children with autism spectrum disorder (ASD) during video games using a computer-implemented system, the method comprising: receiving video game input data from a user device, wherein the video game input data comprising data about motor activity of the child during gameplay; analyzing the video game input data using an AI engine to generate a motor activity report, wherein the motor activity report comprises an assessment of the child's motor activity during gameplay; presenting the motor activity report to a user via a user interface.

8. The method of claim 7, wherein the video game input data comprises one or more of the following data points: keystrokes, mouse clicks, joystick movements, and touchscreen inputs.

9. The method of claim 8, wherein the AI engine comprises a machine learning algorithm trained on a dataset of cognitive assessment data and behavioural data from children with ASD.

Description:AI-BASED TECHNIQUE FOR ANALYZING MOTOR ACTIVITY IN CHILDREN WITH AUTISM SPECTRUM DISORDER (ASD)
Field of the Invention
[0001] The present disclosure generally relates to a system and method for monitoring of children suffering from autism spectrum disorder (ASD). More particularly, the disclosure relates to an AI-based framework for analyzing motor activity in children with autism spectrum disorder (ASD).
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] Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disorder that affects social communication, behaviour, and motor skills. One of the challenges in treating children with ASD is identifying the specific motor impairments that they exhibit, as these can vary widely from child to child. While ASD can be diagnosed as early as two years of age, it can be challenging to diagnose in very young children, especially those with mild or moderate symptoms. Cognitive propensity refers to a child's innate abilities, such as memory, attention, language, and problem-solving skills. Identifying cognitive propensities in pre-school children with ASD can be helpful in understanding their strengths and challenges and can aid in developing tailored interventions and supports.
[0004] The identification of cognitive propensity in pre-school children with mild and moderate ASD is an important research area because it can provide insights into how these children's minds work, how they learn, and how to optimize their learning potential. Additionally, understanding their cognitive strengths and weaknesses can aid in the development of interventions and supports that are personalized to their individual needs, which can improve their overall quality of life and functioning.
[0005] In patent literature various techniques are focused on the identification of cognitive propensity in pre-school children with mild and moderate ASD would contribute to the field of autism research Few of them discussed below.
[0006] US20120128683A1 (by: Totada R. Shantha) - A safe and effective treatment to curtail and cure autism spectrum disorders has been described in this invention using insulin, IGF-1, with multiple known adjuvant therapeutic agents, as well as other pharmaceutical, biochemical, nurticeuticals, and biological agents or compounds delivered through the olfactory mucosal region of the nose and external auditory meatus.
[0007] AU2018237366B2 (by: Research Foundation of State University of New York, Penn State Research Foundation, Quadrant Biosciences Inc) This application provides methods to differentiate between subjects with autism spectrum disorder (ASD) and typically developing (TD) or developmentally delayed (DD) subjects using miRNA and/or microbiome levels detected in saliva samples and patient information. The method can be used to monitor the progress of ASD and guide its treatment. RNA-seq, qPCR, or other methods determine counts and abundance of miRNA or microbiomes. MicroRNA and/or microbiome sequencing data are refined by normalization to expression levels or abundance of time-invariant miRNAs and/or microbial RN As to control for time of sample collection or to compensate for circadian fluctuations in these levels. Multivariate logistic regression and nonlinear classification techniques are further used to select a panel of miRNAs and microbiomes that accurately differentiate between subjects with ASD, DD, and TD in subjects with an unknown ASD status. These panels of miRNAs and microbiomes may be developed into a RNA assay kit.
[0008] US20200054872A1 (By: ElectroCore Inc) - Devices, systems and methods are disclosed for treating or preventing an autism spectrum disorder, a pervasive developmental disorder, or a disorder of psychological development. The methods comprise transmitting impulses of energy non-invasively to selected nerve fibers, particularly those in a vagus nerve. The nerve stimulation may be used as a behavior conditioning tool, by producing euphoria in an autistic individual. Vagus nerve stimulation is also used to modulate circulating serotonin levels in a pregnant woman so as to reduce the risk of having an autistic child; modulate the levels of growth factors within a child; promote balance of neuronal excitation/inhibition; modulate the activity of abnormal resting state neuronal networks; increase respiratory sinus arrhythmia; and avert episodes of motor stereotypies with the aid of forecasting methods.
[0009] US7384399B2 (by: SYNC-THINK Inc) - A system for testing a subject's cognition and motor timing includes an actuator, a sensor and a computer. The actuator is configured to present to the subject multiple stimuli, including predictable stimuli and non-predictable (e.g., random or pseudo-random) stimuli. The sensor generates sensor signals associated with the subject responding to the stimuli. The computer stores timing values associated with the sensor signals for a plurality of the sequences of stimuli, and analyzes the timing values to determine if the subject has an anticipatory timing impairment. The system may also be configured to provide feedback signals to the user, in which case the system also functions as cognition timing and motor training system.
[00010] However, known techniques are not efficient and required extensive technical setup. Thus, there is technological advancement scope in this domain.

Summary
[00011] The present disclosure generally relates to a system and method for monitoring of children suffering from autism spectrum disorder (ASD). More particularly, the disclosure relates to an AI-based framework for analyzing motor activity in children with autism spectrum disorder (ASD).
[00012] The following presents a simplified summary of various aspects of this disclosure in order to provide a basic understanding of such aspects. This summary is not an extensive overview of all contemplated aspects, and is intended to neither identify key or critical elements nor delineate the scope of such aspects. Its purpose is to present some concepts of this disclosure in a simplified form as a prelude to the more detailed description that is presented later.
[00013] The following paragraphs provide additional support for the claims of the subject application.
[00014] Embodiments of the present disclosure may include a system for analyzing motor activity in children with autism spectrum disorder (ASD) during video games, wherein the system including an input module for receiving video game input data from a user device. The video game input data includes data about motor activity of the child during gameplay. Embodiments may also include an AI engine configured to analyse the video game input data and generate a motor activity report to determine a cognitive propensity factor. In some embodiments, the motor activity report may include an assessment of the child's motor activity during gameplay. Embodiments may also include a user interface for presenting the motor activity report to a user.
[00015] In some embodiments, the video game input data may include one or more of the following data points keystrokes, mouse clicks, joystick movements, and touchscreen inputs. In some embodiments, the AI engine may include a machine learning algorithm trained on a dataset of video game input data from children with ASD.
[00016] In some embodiments, the AI engine may be configured to generate a cognitive training program based on the child's specific needs and abilities. In some embodiments, the cognitive training programs may include one or more of the following memory games, attention training, visual processing exercises, and social skills training. In some embodiments, the AI engine may include a machine learning algorithm trained on a dataset of cognitive assessment data and behavioural data from children with ASD. In some embodiments, the system may include a database for storing the video game input data and the motor activity report.
[00017] Embodiments of the present disclosure may also include a method for analyzing motor activity in children with autism spectrum disorder (ASD) during video games using a computer-implemented system, wherein the method includes receiving video game input data from a user device, the video game input data including data about motor activity of the child during gameplay.
[00018] Embodiments may also include analyzing the video game input data using an AI engine to generate a motor activity report. In some embodiments, the motor activity report may include an assessment of the child's motor activity during gameplay. Embodiments may also include presenting the motor activity report to the user via the user interface. In some embodiments, the video game input data may include one or more of the following data points keystrokes, mouse clicks, joystick movements, and touchscreen inputs.
Brief Description of the Drawings
[00019] The features and advantages of the present disclosure would be more clearly understood from the following description taken in conjunction with the accompanying drawings in which:
[00020] FIG. 1 is a block diagram illustrating a system for analyzing motor activity in children with autism spectrum disorder (ASD) during video games, according to some embodiments of the present disclosure.
[00021] FIG. 2 is a detailed block diagram further illustrating the system (from FIG. 1) for analyzing motor activity in children with autism spectrum disorder (ASD) during video games, according to some embodiments of the present disclosure.
[00022] FIG. 3 is a modified block diagram further illustrating the system from FIG. 1 for analyzing motor activity in children with autism spectrum disorder (ASD) during video games, according to some embodiments of the present disclosure.
[00023] FIG. 4 is a flowchart illustrating a method for analyzing motor activity in children, according to some embodiments of the present disclosure.
Detailed Description
[00024] In the following detailed description of the invention, reference is made to the accompanying drawings that form a part hereof, and in which is shown, by way of illustration, specific embodiments in which the invention may be practiced. In the drawings, like numerals describe substantially similar components throughout the several views. These embodiments are described in sufficient detail to claim those skilled in the art to practice the invention. Other embodiments may be utilized and structural, logical, and electrical changes may be made without departing from the scope of the present invention. The following detailed description is, therefore, not to be taken in a limiting sense, and the scope of the present invention is defined only by the appended claims and equivalents thereof.
[00025] The use of the terms “a” and “an” and “the” and “at least one” and similar referents in the context of describing the invention (especially in the context of the following claims) are to be construed to cover both the singular and the plural, unless otherwise indicated herein or clearly contradicted by context. The use of the term “at least one” followed by a list of one or more items (for example, “at least one of A and B”) is to be construed to mean one item selected from the listed items (A or B) or any combination of two or more of the listed items (A and B), unless otherwise indicated herein or clearly contradicted by context. The terms “comprising,” “having,” “including,” and “containing” are to be construed as open-ended terms (i.e., meaning “including, but not limited to,”) unless otherwise noted. Recitation of ranges of values herein are merely intended to serve as a shorthand method of referring individually to each separate value falling within the range, unless otherwise indicated herein, and each separate value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g., “such as”) provided herein, is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention unless otherwise claimed. No language in the specification should be construed as indicating any non-claimed element as essential to the practice of the invention.
[00026] The present disclosure generally relates to a system and method for monitoring of children suffering from autism spectrum disorder (ASD). More particularly, the disclosure relates to an AI-based framework for analyzing motor activity in children with autism spectrum disorder (ASD).
[00027] A system 110 is broken down into its component parts and diagrammatically depicted in FIG. 1 for analyzing motor activity in children with autism spectrum disorder (ASD) during video games, in accordance with some implementations of the present disclosure. The system 110 may, in certain implementations, comprise the following components: an input module 112 for receiving video game input data 120 (interchangeably referred as input data 120) from a user device; an AI engine 114 configured to analyse the video game input data 120 and generate a motor activity report 130 for the purpose of determining a cognitive propensity factor; and a user interface 116 for presenting the motor activity report 130 to a user. The input data 120 for the video game may include data 122 regarding the child's motor activity while they are playing the game. An evaluation 132 of the child's level of motor activity while they are playing may be included in the motor activity report 130.
[00028] The AI engine 114 may, in some implementations, contain a machine learning algorithm that has been trained on a dataset consisting of video game input data 120 from children with autism spectrum disorder (ASD). In certain implementations, the AI engine 114 can be programmed to generate a cognitive training programme that is tailored to the individual requirements and capabilities of the child. The system 110 may, in some implementations, be equipped with a database that can be used to store the video game input data 120 as well as the motor activity report 130.
[00029] In accordance with particular implementations of the present disclosure, the system 110 from Figure 1 is depicted in greater detail in FIG. 2, which is a detailed block diagram for analyzing motor activity in children with autism spectrum disorder (ASD) during video games. In some implementations, the data 120 for the video game input may consist of inputs such as keystrokes 222, mouse clicks 223, movements of the joystick 224, and touches on the touchscreen 225.
[00030] In accordance with particular implementations of the present disclosure, the system 110 from Figure 1 is depicted in greater detail in FIG. 3, which is a modified block diagram for analyzing motor activity in children with autism spectrum disorder (ASD) during video games. Memory games 342 and social skills training 346 are both examples of potential components of various iterations of the cognitive training program 340. In addition to attention training and visual processing exercises 344, the cognitive training programs 340 may also include these activities. The AI engine 114 may, in some implementations, contain the machine learning algorithm that has been trained on a dataset consisting of cognitive assessment data and behavioural data from children who have autism spectrum disorder (ASD).
[00031] The method for analysing the motor activity of children is laid out in flowchart form in FIG. 4 for analyzing motor activity in children with autism spectrum disorder (ASD) during video games, which describes the method according to some implementations of the present disclosure. Receiving video game input data 120 from a user device may be an option at step 410 in certain implementations of the method. The video game input data 120 may include information regarding the child's motor activity while they are playing the video game. At step 420, the method might include analysing the input data from the video game with the AI engine 114 in order to generate a report on the player's motor activity. The motor activity report 130 could be resent to a user through the user interface 116 at step 430, which is one possible implementation of this step in the method. An analysis of the child's motor activity while they are playing may be included in the report on the child's motor activity. In certain implementations, the input data for the video game may include one or more of the data points listed below, and the method may call for the performance of one or more additional steps. Inputs can be given via key presses, mouse clicks, joystick movements, or touchscreens.
[00032] The system 110 for analysing motor activity in children with autism spectrum disorder (ASD) while they play video games may be included among the embodiments of the present disclosure. This system 110 may include the input module 112 for receiving video game input data 120 from a user device. The video game input data 120 may include information about the child's motor activity while they are playing the game. The AI engine 114 that is able to analyse the data that is input into a video game and generate a motor activity report 130 in order to determine a cognitive propensity factor may also be included in embodiments. In certain implementations, the motor activity report 130 could include an analysis of the child's level of motor activity while they were playing a game. The user interface 116 may also be included in embodiments so that a user can be presented with the report of their motor activity.
[00033] The data points that make up the input for a video game may include one or more of the following categories (depending on the specific implementation): keystrokes, mouse clicks, joystick movements, and touchscreen inputs. The AI engine 114 may, in certain embodiments, include the machine learning algorithm that was trained on a dataset consisting of video game input data 120 from children with autism spectrum disorder (ASD).
[00034] In certain implementations, the AI engine 114 can be programmed to generate a cognitive training programme that is tailored to the individual requirements and capabilities of the child. Memory games 342, attention training, visual processing exercises, and social skills training are just some of the potential components of the cognitive training programmes that can be included in various embodiments of the technology. The machine learning algorithm that has been trained on a dataset containing cognitive assessment data and behavioural data from children with autism spectrum disorder (ASD) may be included in some embodiments of the AI engine. In some implementations of the system, a database may be included for the purpose of storing the data obtained from playing video games as well as the motor activity report 130.
[00035] A method for analysing motor activity in children with autism spectrum disorder (ASD) during video games using a computer-implemented system may also be included in embodiments of the present disclosure. The method may include receiving video game input data 120 from a user device, with the video game input data 120 including data about the child's motor activity while they are playing the game.
[00036] In some embodiments, the motor activity report 130 can be generated by performing an analysis of the input data from a video game using an artificial intelligence engine. In certain implementations, the motor activity report 130 could include an analysis of the child's level of motor activity while they were playing a game. It's possible that some embodiments will include sending the motor activity report 130 to the user through some kind of user interface. The data points that make up the input for a video game may include one or more of the following categories(depending on the specific implementation): keystrokes, mouse clicks, joystick movements, and touchscreen inputs.
[00037] Example embodiments herein have been described above with reference to block diagrams and flowchart illustrations of methods and apparatuses. It will be understood that each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations, respectively, can be implemented by various means including hardware, software, firmware, and a combination thereof. For example, in one embodiment, each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations can be implemented by computer program instructions. These computer program instructions may be loaded onto a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions which execute on the computer or other programmable data processing apparatus create means for implementing the functions specified in the flowchart block or blocks.
[00038] Throughout the present disclosure, the term ‘processing means’ or ‘microprocessor’ or ‘processor’ or ‘processors’ includes, but is not limited to, a general purpose processor (such as, for example, a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, a microprocessor implementing other types of instruction sets, or a microprocessor implementing a combination of types of instruction sets) or a specialized processor (such as, for example, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), or a network processor).
[00039] The term “non-transitory storage device” or “storage” or “memory,” as used herein relates to a random access memory, read only memory and variants thereof, in which a computer can store data or software for any duration.
[00040] Operations in accordance with a variety of aspects of the disclosure is described above would not have to be performed in the precise order described. Rather, various steps can be handled in reverse order or simultaneously or not at all.
[00041] While several implementations have been described and illustrated herein, a variety of other means and/or structures for performing the function and/or obtaining the results and/or one or more of the advantages described herein may be utilized, and each of such variations and/or modifications is deemed to be within the scope of the implementations described herein. More generally, all parameters, dimensions, materials, and configurations described herein are meant to be exemplary and that the actual parameters, dimensions, materials, and/or configurations will depend upon the specific application or applications for which the teachings is/are used. Those skilled in the art will recognize, or be able to ascertain using no more than routine experimentation, many equivalents to the specific implementations described herein. It is, therefore, to be understood that the foregoing implementations are presented by way of example only and that, within the scope of the appended claims and equivalents thereto, implementations may be practiced otherwise than as specifically described and claimed. Implementations of the present disclosure are directed to each individual feature, system, article, material, kit, and/or method described herein. In addition, any combination of two or more such features, systems, articles, materials, kits, and/or methods, if such features, systems, articles, materials, kits, and/or methods are not mutually inconsistent, is included within the scope of the present disclosure.

Claims
I/We Claim:
1. A system for analyzing motor activity in children with autism spectrum disorder (ASD) during video games, the system comprising: an input module for receiving video game input data from a user device, wherein the video game input data comprising data about motor activity of the child during gameplay; an AI engine configured to analyse the video game input data and generate a motor activity report to determine cognitive propensity factor, wherein the motor activity report comprises an assessment of the child's motor activity during gameplay; and a user interface for presenting the motor activity report to a user.
2. The system of claim 1, wherein the video game input data comprises one or more of the following data points: keystrokes, mouse clicks, joystick movements, and touchscreen inputs.
3. The system of claim 1, wherein the AI engine comprises a machine learning algorithm trained on a dataset of video game input data from children with ASD.
4. The system of claim 1, wherein the AI engine is configured to generate a cognitive training program based on the child’s specific needs and abilities.
5. The system of claim 1, wherein the cognitive training programs comprise one or more of the following: memory games, attention training, visual processing exercises, and social skills training.
6. The system of claim 1, further comprising a database for storing the video game input data and the motor activity report.
7. A method for analyzing motor activity in children with autism spectrum disorder (ASD) during video games using a computer-implemented system, the method comprising: receiving video game input data from a user device, wherein the video game input data comprising data about motor activity of the child during gameplay; analyzing the video game input data using an AI engine to generate a motor activity report, wherein the motor activity report comprises an assessment of the child's motor activity during gameplay; presenting the motor activity report to a user via a user interface.
8. The method of claim 7, wherein the video game input data comprises one or more of the following data points: keystrokes, mouse clicks, joystick movements, and touchscreen inputs.
9. The method of claim 8, wherein the AI engine comprises a machine learning algorithm trained on a dataset of cognitive assessment data and behavioural data from children with ASD.

AI-BASED TECHNIQUE FOR ANALYZING MOTOR ACTIVITY IN CHILDREN WITH AUTISM SPECTRUM DISORDER (ASD)
Abstract
The present disclosure relates to a system for analysing motor activity in children with autism spectrum disorder (ASD) while they play video games may be included among the embodiments of the present disclosure. This system may include an input module for receiving video game input data from a user device. The video game input data may include information about the child's motor activity. An AI engine that is able to analyse the data that is input into a video game and generate a motor activity report in order to determine a cognitive propensity factor may also be included in embodiments. In certain implementations, the motor activity report could include an analysis of the child's level of motor activity while they were playing a game. A user interface may also be included in embodiments so that a user can be presented with the report of their motor activity. , C , Claims:Claims
I/We Claim:
1. A system for analyzing motor activity in children with autism spectrum disorder (ASD) during video games, the system comprising: an input module for receiving video game input data from a user device, wherein the video game input data comprising data about motor activity of the child during gameplay; an AI engine configured to analyse the video game input data and generate a motor activity report to determine cognitive propensity factor, wherein the motor activity report comprises an assessment of the child's motor activity during gameplay; and a user interface for presenting the motor activity report to a user.
2. The system of claim 1, wherein the video game input data comprises one or more of the following data points: keystrokes, mouse clicks, joystick movements, and touchscreen inputs.
3. The system of claim 1, wherein the AI engine comprises a machine learning algorithm trained on a dataset of video game input data from children with ASD.
4. The system of claim 1, wherein the AI engine is configured to generate a cognitive training program based on the child’s specific needs and abilities.
5. The system of claim 1, wherein the cognitive training programs comprise one or more of the following: memory games, attention training, visual processing exercises, and social skills training.
6. The system of claim 1, further comprising a database for storing the video game input data and the motor activity report.
7. A method for analyzing motor activity in children with autism spectrum disorder (ASD) during video games using a computer-implemented system, the method comprising: receiving video game input data from a user device, wherein the video game input data comprising data about motor activity of the child during gameplay; analyzing the video game input data using an AI engine to generate a motor activity report, wherein the motor activity report comprises an assessment of the child's motor activity during gameplay; presenting the motor activity report to a user via a user interface.
8. The method of claim 7, wherein the video game input data comprises one or more of the following data points: keystrokes, mouse clicks, joystick movements, and touchscreen inputs.
9. The method of claim 8, wherein the AI engine comprises a machine learning algorithm trained on a dataset of cognitive assessment data and behavioural data from children with ASD.