BEHAVIOR ANALYSIS FUNCTIONALITY IN AUTISM SPECTRUM DISORDER (ASD) USERS THROUGH SMARTPHONE APPLICATION

Abstract

ABSTRACT “BEHAVIOR ANALYSIS FUNCTIONALITY IN AUTISM SPECTRUM DISORDER (ASD) USERS THROUGH SMARTPHONE APPLICATION” The present invention provides behavior analysis functionality in autism spectrum disorder (ASD) users through smartphone application that proposes to design a personalized smartphone application ‘ASD-Behave’ to analyze the behavioural data of ASD users in natural setting. Behaviour driven data recordings are prepared and communicated to caretakers as well as experts to determine the reasoning. Disruptive behaviors are identified and their corresponding pre-happenings which triggered those behaviours are noted. Suitable feedback is provided by caretakers. The behavioural dataset is trained using machine learning model. The model further recommends appropriate message and suggestion when any such disruptive action is observed in the user. Figure 1 illustrates the skeleton workflow of the model.

Specification

Description:TECHNICAL FIELD
[0001] The present invention relates to the field of artificial intelligence and automated health care systems, and more particularly, the present invention relates to the behavior analysis functionality in autism spectrum disorder (ASD) users through smartphone application.
BACKGROUND ART
[0002] The following discussion of the background of the invention is intended to facilitate an understanding of the present invention. However, it should be appreciated that the discussion is not an acknowledgment or admission that any of the material referred to was published, known, or part of the common general knowledge in any jurisdiction as of the application's priority date. The details provided herein the background if belongs to any publication is taken only as a reference for describing the problems, in general terminologies or principles or both of science and technology in the associated prior art.
[0003] Autism affected children are usually seen with disruptive behaviours and they make different gestures during communication. An unanticipated event may happen at any time without the knowledge of the caretaker. Existing models may not be so scalable and robust to use. In such scenarios, capturing camera recordings are helpful in assessing behavioral aspects of the patients.
[0004] Problem in the existing product:
- Most existing models are either used to monitor ASD user activities for a short time or used as a simple classification of ASD patients from normal individuals.
- Models lack scalability since they may handle limited input data but beyond that it affects the response time.
- A personalized support to assist ASD users is need of the hour.
[0005] Applied Behavior Analysis (ABA): It is a widely used therapeutic approach focusing on reinforcing desired behaviors while reducing unwanted behaviors. Cognitive Behavioral Therapy (CBT): It is used to address anxiety and behavioral issues by changing negative thought patterns. Machine Learning Models: It is used to analyze behavioral data collected from various sources to identify patterns and predict behaviors.
[0006] In light of the foregoing, there is a need for Behavior analysis functionality in autism spectrum disorder (ASD) users through smartphone application that overcomes problems prevalent in the prior art associated with the traditionally available method or system, of the above-mentioned inventions that can be used with the presented disclosed technique with or without modification.
[0007] All publications herein are incorporated by reference to the same extent as if each individual publication or patent application were specifically and individually indicated to be incorporated by reference. Where a definition or use of a term in an incorporated reference is inconsistent or contrary to the definition of that term provided herein, the definition of that term provided herein applies, and the definition of that term in the reference does not apply.
OBJECTS OF THE INVENTION
[0008] The principal object of the present invention is to overcome the disadvantages of the prior art by providing behavior analysis functionality in autism spectrum disorder (ASD) users through smartphone application.
[0009] Another object of the present invention is to provide behavior analysis functionality in autism spectrum disorder (ASD) users through smartphone application that can help in identifying early indicators of autism through observation and data collection, allowing for timely diagnosis and intervention.
[0010] Another object of the present invention is to provide behavior analysis functionality in autism spectrum disorder (ASD) users through smartphone application that enables the development of customized intervention strategies that address specific strengths and challenges.
[0011] Another object of the present invention is to provide behavior analysis functionality in autism spectrum disorder (ASD) users through smartphone application that monitors behaviors in real time, providing immediate feedback to caregivers and educators.
[0012] Another object of the present invention is to provide behavior analysis functionality in autism spectrum disorder (ASD) users through smartphone application that supports therapies such as Applied Behaviour Analysis (ABA) by providing data on behavior frequency, intensity, and contexts.
[0013] Another object of the present invention is to provide behavior analysis functionality in autism spectrum disorder (ASD) users through smartphone application that help children practice social interactions in a safe environment.
[0014] Another object of the present invention is to provide behavior analysis functionality in autism spectrum disorder (ASD) users through smartphone application that is environment friendly.
[0015] The foregoing and other objects of the present invention will become readily apparent upon further review of the following detailed description of the embodiments as illustrated in the accompanying drawings.
SUMMARY OF THE INVENTION
[0016] The present invention relates to behavior analysis functionality in autism spectrum disorder (ASD) users through smartphone application.
[0017] Autism affected children are usually seen with disruptive behaviours and they make different gestures during communication. An unanticipated event may happen at any time without the knowledge of the caretaker. Existing models may not be so scalable and robust to use. In such scenarios, capturing camera recordings are helpful in assessing behavioral aspects of the patients. This work proposes to design a personalized smartphone application 'ASD-Behave' to analyze the behavioural data of ASD users in natural setting. Behaviour driven data recordings are prepared and communicated to caretakers as well as experts to determine the reasoning. Disruptive behaviors are identified and their corresponding pre-happenings which triggered those behaviours are noted. Suitable feedback is provided by caretakers. The behavioural dataset is trained using machine learning model. The model further recommends appropriate message and suggestion when any such disruptive action is observed in the user. Figure 1 illustrates the skeleton workflow of the model. Table 1 shows a sample behavioural dataset depicting pre-activities that triggered the user, disruptive reactions of ASD user and their respective reaction recommended by counsellor.
Table 1: Sample Examples of 'Abnormal Behaviour' Dataset
Before reaction scene Disruptive reaction Suitable message from caretaker
Person not giving attention Screaming Wait for some time and talk again to the person
Facing a crowd Unable to make eye contact Be comfortable and look at everyone one by one
Person getting angry Leaving the place Be patient and tell the reason for anger
Missed the bus Anxious and panic Wait for the next bus or call me
Unable to find an object Irritation Remember the previous location of object and ask close people about it
- Children with autism exhibit behaviour that is often disruptive to themselves or others.
- Events may happen at any time and the caretaker may not be aware of it. Thus, capturing recording through camera helps in capturing natural behaviors.
- To capture and handle disruptive behaviors of ASD children, behavioral analysis functionality is designed to analyze abnormal behavioural data of ASD users in natural settings through installed camera in indoor settings.
- Behaviour based reactions to events are recorded along with their time of occurrence, context of event and predecessor happening which triggered a certain reaction.
- These recordings are sent to caretakers on weekly basis. Caretaker in consultation with expert assesses the information to recognize the functionality. As an example, an ASD patient could be shouting loudly to get attention of someone present in the room.
- Disruptive behaviors are identified and their corresponding pre-happenings which triggered those behaviors are noted.
- Caretakers provide appropriate and corrective feedback action through audio, text or video message.
- The prepared 'Abnormal Behaviour' dataset shown in table 1 consists of 3 columns. Column 1 will have all pre-activity which triggered the user to show disruptive behavior. Column 2 has all instances of disruptive/ abnormal reactions of ASD user. Column 3 will have its respective suitable reaction recommended by caretaker/counsellor.
- The dataset is trained with machine learning technique. Further in future if any such incident occurs then model will immediately suggest suitable message in the form of audio, video or text based on its severity.
- Also the model will give positive suggestion/ recommendation when the child encounters similar kind of scenarios.
Unique features of our solution include the following:
[0018] Limited Data Sources: Existing models rely on small datasets, which may not represent the diversity of the autism population. Our model uses an extensive and continuous dataset which evolves over time.
[0019] Environmental Influence: Existing models fail to capture behaviors that are influenced by environmental factors like time of day. Our model can handle the environmental features quite well as the dataset is trained using all types of variables.
[0020] Overfitting: complex models may overfit to the training data, failing to generalize to new cases. Our model uses a scalable dataset which grows over time so scope of over-fitting is less.
[0021] Real-Time Monitoring: Many existing models lack the capability for real-time monitoring and feedback, which is crucial for timely interventions. Our model overcomes this issue and guide as a real-time monitoring model which provides suitable recommendations also based on user's action.
[0022] While the invention has been described and shown with reference to the preferred embodiment, it will be apparent that variations might be possible that would fall within the scope of the present invention.
BRIEF DESCRIPTION OF DRAWINGS
[0023] So that the manner in which the above-recited features of the present invention can be understood in detail, a more particular description of the invention, briefly summarized above, may have been referred by embodiments, some of which are illustrated in the appended drawings. It is to be noted, however, that the appended drawings illustrate only typical embodiments of this invention and are therefore not to be considered limiting of its scope, for the invention may admit to other equally effective embodiments.
[0024] These and other features, benefits, and advantages of the present invention will become apparent by reference to the following text figure, with like reference numbers referring to like structures across the views, wherein:
[0025] Figure 1: Overall workflow of the proposed model.
DETAILED DESCRIPTION OF THE INVENTION
[0026] While the present invention is described herein by way of example using embodiments and illustrative drawings, those skilled in the art will recognize that the invention is not limited to the embodiments of drawing or drawings described and are not intended to represent the scale of the various components. Further, some components that may form a part of the invention may not be illustrated in certain figures, for ease of illustration, and such omissions do not limit the embodiments outlined in any way. It should be understood that the drawings and the detailed description thereto are not intended to limit the invention to the particular form disclosed, but on the contrary, the invention is to cover all modifications, equivalents, and alternatives falling within the scope of the present invention as defined by the appended claim.
[0027] As used throughout this description, the word "may" is used in a permissive sense (i.e. meaning having the potential to), rather than the mandatory sense, (i.e. meaning must). Further, the words "a" or "an" mean "at least one" and the word "plurality" means "one or more" unless otherwise mentioned. Furthermore, the terminology and phraseology used herein are solely used for descriptive purposes and should not be construed as limiting in scope. Language such as "including," "comprising," "having," "containing," or "involving," and variations thereof, is intended to be broad and encompass the subject matter listed thereafter, equivalents, and additional subject matter not recited, and is not intended to exclude other additives, components, integers, or steps. Likewise, the term "comprising" is considered synonymous with the terms "including" or "containing" for applicable legal purposes. Any discussion of documents, acts, materials, devices, articles, and the like are included in the specification solely for the purpose of providing a context for the present invention. It is not suggested or represented that any or all these matters form part of the prior art base or were common general knowledge in the field relevant to the present invention.
[0028] In this disclosure, whenever a composition or an element or a group of elements is preceded with the transitional phrase "comprising", it is understood that we also contemplate the same composition, element, or group of elements with transitional phrases "consisting of", "consisting", "selected from the group of consisting of, "including", or "is" preceding the recitation of the composition, element or group of elements and vice versa.
[0029] The present invention is described hereinafter by various embodiments with reference to the accompanying drawing, wherein reference numerals used in the accompanying drawing correspond to the like elements throughout the description. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiment set forth herein. Rather, the embodiment is provided so that this disclosure will be thorough and complete and will fully convey the scope of the invention to those skilled in the art. In the following detailed description, numeric values and ranges are provided for various aspects of the implementations described. These values and ranges are to be treated as examples only and are not intended to limit the scope of the claims. In addition, several materials are identified as suitable for various facets of the implementations. These materials are to be treated as exemplary and are not intended to limit the scope of the invention.
[0030] The present invention relates to behavior analysis functionality in autism spectrum disorder (ASD) users through smartphone application.
[0031] Autism affected children are usually seen with disruptive behaviours and they make different gestures during communication. An unanticipated event may happen at any time without the knowledge of the caretaker. Existing models may not be so scalable and robust to use. In such scenarios, capturing camera recordings are helpful in assessing behavioral aspects of the patients. This work proposes to design a personalized smartphone application 'ASD-Behave' to analyze the behavioural data of ASD users in natural setting. Behaviour driven data recordings are prepared and communicated to caretakers as well as experts to determine the reasoning. Disruptive behaviors are identified and their corresponding pre-happenings which triggered those behaviours are noted. Suitable feedback is provided by caretakers. The behavioural dataset is trained using machine learning model. The model further recommends appropriate message and suggestion when any such disruptive action is observed in the user. Figure 1 illustrates the skeleton workflow of the model. Table 1 shows a sample behavioural dataset depicting pre-activities that triggered the user, disruptive reactions of ASD user and their respective reaction recommended by counsellor.
Table 1: Sample Examples of 'Abnormal Behaviour' Dataset
Before reaction scene Disruptive reaction Suitable message from caretaker
Person not giving attention Screaming Wait for some time and talk again to the person
Facing a crowd Unable to make eye contact Be comfortable and look at everyone one by one
Person getting angry Leaving the place Be patient and tell the reason for anger
Missed the bus Anxious and panic Wait for the next bus or call me
Unable to find an object Irritation Remember the previous location of object and ask close people about it
- Children with autism exhibit behaviour that is often disruptive to themselves or others.
- Events may happen at any time and the caretaker may not be aware of it. Thus, capturing recording through camera helps in capturing natural behaviors.
- To capture and handle disruptive behaviors of ASD children, behavioral analysis functionality is designed to analyze abnormal behavioural data of ASD users in natural settings through installed camera in indoor settings.
- Behaviour based reactions to events are recorded along with their time of occurrence, context of event and predecessor happening which triggered a certain reaction.
- These recordings are sent to caretakers on weekly basis. Caretaker in consultation with expert assesses the information to recognize the functionality. As an example, an ASD patient could be shouting loudly to get attention of someone present in the room.
- Disruptive behaviors are identified and their corresponding pre-happenings which triggered those behaviors are noted.
- Caretakers provide appropriate and corrective feedback action through audio, text or video message.
- The prepared 'Abnormal Behaviour' dataset shown in table 1 consists of 3 columns. Column 1 will have all pre-activity which triggered the user to show disruptive behavior. Column 2 has all instances of disruptive/ abnormal reactions of ASD user. Column 3 will have its respective suitable reaction recommended by caretaker/counsellor.
- The dataset is trained with machine learning technique. Further in future if any such incident occurs then model will immediately suggest suitable message in the form of audio, video or text based on its severity.
- Also the model will give positive suggestion/ recommendation when the child encounters similar kind of scenarios.
Unique features of our solution include the following:
[0032] Limited Data Sources: Existing models rely on small datasets, which may not represent the diversity of the autism population. Our model uses an extensive and continuous dataset which evolves over time.
[0033] Environmental Influence: Existing models fail to capture behaviors that are influenced by environmental factors like time of day. Our model can handle the environmental features quite well as the dataset is trained using all types of variables.
[0034] Overfitting: complex models may overfit to the training data, failing to generalize to new cases. Our model uses a scalable dataset which grows over time so scope of over-fitting is less.
[0035] Real-Time Monitoring: Many existing models lack the capability for real-time monitoring and feedback, which is crucial for timely interventions. Our model overcomes this issue and guide as a real-time monitoring model which provides suitable recommendations also based on user's action.
[0036] ASD-Behave vs Applied Behavior Analysis (ABA): ABA mainly targets observable behaviors without always addressing the underlying emotional or social factors contributing to those behaviors. On the other hand ASD-Behave address the pre-occurrence events which led to the disruptive behaviors in ASD patients.
[0037] In ABA, skills are learned in structured settings and it may not always generalize to natural environments thereby limiting their effectiveness in real-life situations. However in ASD-Behave, the users are camera monitored continuously in natural home environment so skillset is acquired in a more natural manner.
[0038] ASD-Behave vs Cognitive Behavioral Therapy (CBT): CBT requires users to have a certain level of cognitive awareness into their behaviors, which can be challenging for some individuals, particularly young children. But ASD-Behave do not come with this restriction. Users are free to explore themselves in natural settings without being cognitive aware.
[0039] CBT primarily focuses on current thoughts and behaviors, which may neglect the exploration of issues from the past that could be influencing present behavior. ASD-Behave take into consideration both current actions, previous event that triggered the action and the corrective measure too.
[0040] ASD-Behave vs Machine Learning (ML): Autism symptoms can change over time, making it difficult for static ML models to accurately predict diagnoses across different developmental stages. In ASD-Behave, the dataset is continuously evolving and new behavioral patterns are always getting added through camera captured recordings.
[0041] ML models sometimes tend to over-fit in training dataset if the data size is small which leads to performance degradation on test samples and it restricts their generalization ability. In ASD-Behave, the dataset size is quite high so the model is thoroughly trained to handle variety of disruptive behaviors in ASD patients.
- Early Diagnosis and Screening: Behavioural analysis can help in identifying early indicators of autism through observation and data collection, allowing for timely diagnosis and intervention.
- Development of customized intervention strategies: Analyzing individual behavior patterns enables the development of customized intervention strategies that address specific strengths and challenges.
- Real-Time Monitoring and Feedback: Using sensors and mobile apps to monitor behaviors in real time, providing immediate feedback to caregivers and educators.
- Behavioral Therapy Support: Supporting therapies such as Applied Behaviour Analysis (ABA) by providing data on behavior frequency, intensity, and contexts.
- Social Skills Training Speech and Language Therapy: Using behavioral analysis to design interactive scenarios that help children practice social interactions in a safe environment.
[0042] Various modifications to these embodiments are apparent to those skilled in the art from the description and the accompanying drawings. The principles associated with the various embodiments described herein may be applied to other embodiments. Therefore, the description is not intended to be limited to the 5 embodiments shown along with the accompanying drawings but is to be providing the broadest scope consistent with the principles and the novel and inventive features disclosed or suggested herein. Accordingly, the invention is anticipated to hold on to all other such alternatives, modifications, and variations that fall within the scope of the present invention and appended claims. , Claims:CLAIMS
We Claim:
1) A smartphone application for behavior analysis in autism spectrum disorder (ASD) users, comprising:
- a camera module installed on a device to capture real-time video recordings of the user's behavior in a natural setting;
- a data processing module configured to analyze the captured video data and identify disruptive behaviors exhibited by the ASD user;
- a machine learning model trained on a continuously evolving dataset to predict disruptive behaviors and suggest corrective actions in the form of text, audio, or video messages to the caretaker.
2) The smartphone application as claimed in claim 1, wherein the machine learning model does trained using a behavioral dataset comprise:
- pre-occurrence events that trigger disruptive behaviors in the ASD user;
- the corresponding disruptive reactions exhibited by the ASD user;
- the recommended corrective feedback provided by a caretaker or counselor.
3) The smartphone application as claimed in claim 1, wherein the smartphone application further comprising:
- A real-time monitoring feature that provides immediate behavioral feedback and suggestions to the caregiver based on detected disruptive behavior;
- A notification system that alerts the caretaker via text, audio, or video message when a disruptive behavior is detected.
4) The smartphone application as claimed in claim 1, wherein the dataset is continuously updated to capture new behavioral patterns, improving the model's ability to generalize across different developmental stages of the ASD user, and reducing overfitting through the inclusion of diverse behavioral data.
5) The smartphone application as claimed in claim 1, wherein the machine learning model accounts for environmental factors, such as time of day or location, that influence the behavior of the ASD user, and incorporates these variables into the behavior prediction and feedback generation process.
6) A method for analyzing disruptive behaviors in ASD users through a smartphone application, comprising the steps of:
- Capturing real-time video recordings of the ASD user's behavior in natural settings using the smartphone camera;
- Processing the video data to identify disruptive behaviors and the pre-occurring events that trigger such behaviors;
- Using a machine learning model trained on a diverse and continuously evolving behavioral dataset to generate appropriate corrective feedback and send it to the caretaker in real-time; and
- Continuously updating the behavioral dataset based on new data to improve model accuracy and recommendation relevance.

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