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IRJET- Learning Assistance System for Autistic Child
1.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 05 | May 2019 www.irjet.net p-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 7231 LEARNING ASSISTANCE SYSTEM FOR AUTISTIC CHILD SHERIN M WILSON, ASWANI S RAJAN 1SHERIN M WILSON M.Tech Computer Science & Engineering. Sree Buddha College of Engineering, Ayathil, Elavumthitta Pathanamthitta , Kerala, India. 2 Ms. ASWANI S RAJAN Assistant Professor Computer Science & Engineering. Sree Buddha College of Engineering, Ayathil, Elavumthitta, Pathanamthitta, Kerala, India ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - Autism or Autism Spectrum Disorder (ASD) characterizes a vast range of developmental disabilities associated with social interaction, communication and behavior. Though basic education is a must for everyone, but teaching autism community through traditional approach is still quite complex. This project is aims in teachingMalayalam to autism students according to their disease condition. Here the affected area of the children is detected and according to that the learning is undertaken. The mainareascoveringover here is data mining, neuroimaging, deep learning. Key Words: Autism Spectrum Disorder, AAC, CALL 1. INTRODUCTION Autism Spectrum Disorder (ASD) is a neurodevelopmental disability with atypical traits in behavioral andphysiological responses. Autism is a group of complex disorders of brain development. The autism students need special training to make them familiar to words,lettersandobjects.Toimprove autism patients reading and understanding skills, a new system is developed which trained them on understanding words, letters (Malayalam) and objects. Even today, there is no system for training in our mother tongue Malayalam.The assessment was based on the voice recorded and it was analyzed using MFCC algorithm. But the developed system’s output was not accurate. Thus by using deep learning algorithms the output is being modified. The learning tool is being modified by analyzing the level by which the student had learnt the letters. If he learnt properly, then he will be promoted to study words and then sentences. After learning an exam will be conducted and the result is provided to the trainer. While learning, the sound of student is being recorded. Thus by using that sound (recorded), the disease prediction is being performed. It is mainly classified into 4 1) Nasal problems 2) Tongue problems 3) Auditory problems 4) Brain defects by using ABIDE dataset 1.1. GENERAL BACKGROUND If you've met one person with autism,you'vemetoneperson with autism: - DR. STEPHEN SHORE. Autism spectrum disorder (ASD) is a complex developmental disability, typically appearingduringchildhoodandaffectinga person's ability to communicate and interact with others. The word 'spectrum' describes the range of difficulties that people on the autism spectrum may experience and the degree to which they may be affected. The main areas of difficulty are in social communication, social interaction and restricted or repetitive behaviors and interests. People on the autism spectrum may also have: 1) Unusual sensory interests such as sniffing objects or staring intently at moving objects 2) Sensory sensitivities including avoidingeverydaysounds and textures such as hair dryers, vacuum cleaners and sand 3) Intellectual impairment or learning difficulties This project is based upon machine learning concept. By using machine learning algorithms, the output of the project is improved to an extent. MachineLearningisundeniablyone of the most influential and powerful technologies in today’s world. More importantly, we are far from seeing its full potential. There’s no doubt, it will continue to be making headlines for the foreseeable future.Thisarticleis designedas an introduction to the Machine Learning concepts, covering all the fundamental ideas without being too high level. Machine learning is a tool for turning information into knowledge. In the past 50 years, there has been an explosion of data. This mass of data is useless unless we analyze it and find the patterns hidden within. Machine learning techniques are used to automatically find the valuable underlying patterns within complex data that we would otherwise struggle to discover.Thehiddenpatternsandknowledgeabout a problem can be used to predict future events and perform all kinds of complex decision making. Most of us are unaware that we already interact with Machine Learning every single day. Every time we Google something, listen to a song or even take a photo, Machine Learning is becoming part of the engine behind it, constantly learning and improving from every interaction. It’s also
2.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 05 | May 2019 www.irjet.net p-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 7232 behind world-changing advances like detecting cancer, creating new drugs and self-driving cars. The reason that Machine Learning is so exciting, is because it is a step away from all our previous rule-based systems of: if(x = y): do z Traditionally, software engineeringcombinedhumancreated rules with data to create answers to a problem. Instead, machine learning uses data and answers to discover therules behind a problem. To learn the rules governing a phenomenon, machines have to go through a learning process, trying different rules and learning from how well they perform. There are multiple forms of MachineLearning; supervised, unsupervised, semi-supervised and reinforcement learning. Each form of Machine Learning has differing approaches, but they all follow the same underlying process and theory. The process in machine learning is as follows: 1. Data Collection: Collect the data that the algorithm will learn from. 2. Data Preparation: Format and engineer the datainto the optimal format, extracting important features and performing dimensionaility reduction. 3. Training: Also known as the fitting stage, this is where the Machine Learning algorithm actually learns by showing it the data that has been collected and prepared. 4. Evaluation: Test the model to see how well it performs. 5. Tuning: Fine tune the model to maximize it’s performance. Fig 2.1: Process in machine learning 1.2 OBJECTIVE The main objective of the project is to provide a better learning experience to autistic society. Autistic children are very much different from normal children. They require special treatment. They may be slow learners and need time to learn something. So it won’t be a good for them to provide a normal children learning experience. Various studies have shown that they are very much fond of visualizing things rather than by hearting it. Keeping this in mind the project is developed with good aesthetics and rather than just showing the letters and words, audio of each letter/word is provided. Thus the students can listen to it and can learn it more effectively. The status of their learningisalsonotedandhence it is possible for the trainers to change the strategies of learning. In addition to that, the main objective of the project is to provide learning according to their health status. Sometimes their ENT characteristics are affected due to which they may find it difficult to follow the instructions.Sometimesthe brain is affected more and hence it will affect their thinking and understanding skills. Hence it is very necessary to provide them learning based on their health status. Thus a complete learning toolkit for autistic society along with monitoring their health status is the objective. This will improve thelives of the most beautiful creation in the world. Justlikeeveryone, they should also have the right to education. Thusthisproject aims to provide that education in a more flexible way. 1.3. SCOPE Sometimes the difficulties of autism can lead to behaviors that are quite challenging for us to understand and address. Most individuals with autism will display challenging behaviors of some sort at some point in their lives. These behaviors can often be the result of the underlying conditions associated with autism. Challenging behaviors represent some of the most concerning and stressful features of autism. These behaviors can often cause harm or damage, family and staff stress, isolation, and caregiver burnout. Parents may feel guilty or responsible, but it is important to know that you should not blame yourself for behaviors that you find difficult. Sometimes, the extraordinary steps parents go through for their children with complex needs might not be enough, and additional supports and resources might be necessary. It is important not to think of your child, or these behaviors, as ‘bad,’ but to learn how to better understand and respond to challenging situations to make them more manageable for everyone. Thus this learning kit will help to tackle this challenging behavior and provide a better learning experience to the autistic children. 3. RESULT AND ANALYSIS 3.1 RESULT This section discusses the experimental results of the regularization of the model and trust model for learning assistance system for autistic child (LASAC). The system that uses the operating system for windows 10 and windows
3.
International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 05 | May 2019 www.irjet.net p-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 7233 platforms here is c#.net. And the database created is a SQL server. The proposed system is using results of machine learning algorithms for results assessment. There is a test data and training data. The training data is collected from various hospitals. The test data is obtained from the users of this system. The proposed system is implemented using three modules and different sub-modules. The system's input is the audio and the checkup list details of the student. The details about the students ear, tongue, nose and brain is obtained. The ear parameters are HlevelRAC, HlevelRBC, HlevelLAC,HlevelLBC, bilateral/unilateral, reliability, eardrum affected or not, ear discharge present or not, perforation is present or not. The audiogram of the patient is obtained from the hospitals. The audiogram is interpreted is as follows: 1. Is there a hearing loss or not? To find that compare the collectedresultswith normal results. 2. If there is a hearing loss, what is the type of it? Compare AC (air conduction)andBC(boneconduction) results. BC should always less than AC. HlevelRAC and HlevelRBC are the AC and BC values of right ear respectively. Similarly HlevelLACandHleveLBC arethe AC and BC values of left ear respectively. Fig 3.1.1: Audiogram of a patient 3. What is its degree? The degree of hearing loss is as follows. From this degree the reliability can be found. Reliability have values like good, fair, severe, moderateloss,mildsevere, poor etc. Fig 3.1.2: Degree of hearing loss 4. Bilateral or Unilateral: If both the ears are affected, then the ear is said to be bilateral. Otherwise unilateral Fig 3.1.3: Test data collection of ear The details of nose and tongue are also collected. The parameters used for nose is hyper nasality, hypo nasality, air emission and esr. Hyper nasality means too much sound resonating in the nasal cavity. Hypo nasality means not enough nasal resonance on nasal sounds. ESR is checked to help the diagnosis of inflammation.Airemissionischeckedto
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International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 05 | May 2019 www.irjet.net p-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 7234 find air leaks through the valve. It is also helpful in finding defects in voiceless consonants. The tongue test parameters are tongue tie, tonsil enlargement and phonation Fig 3.1.4: Test data collection of nose and tongue In addition to the above ENT statistics, brain impressions of the student are also collected. The main three parameters of brain used here are presence of white matter, virchow robin, temporal leftlobe, temporal right lobe. 1. White matter: It refers to areas of the central nervous system (CNS) that are mainly made up of myelinated axons, also called tracts.[1] Long thought to be passive tissue, white matter affects learning and brain functions, modulating the distribution of action potentials, acting as a relay and coordinating communication between different brain regions. White matter is the tissue through which messages pass between different areas of gray matter within the central nervous system. The white matter is white because of the fatty substance (myelin) that surrounds the nerve fibers (axons). This myelin is found in almost all long nerve fibers, and acts as an electrical insulation. This is important because it allows the messages to pass quickly from place to place. If white matter problem is present, it will affect the thinking capability, the ability to walk straight. The various problems associated with white matter is as follows: Trouble learning or remembering new things A hard time with problem solving Slowed thinking Leaking urine Depression Problems walking Balance issues and more falls Fig 3.1.5: White matter abnormalities Dilated Virchow-Robin: Virchow-Robin spaces (VRSs) are CSF spaces that accompany blood vessels as they perforate the brain substance. Dilatation of VRS is associated with microangiopathy. Microvascular diseasehasa major etiologic and pathogenetic role in dementias. To our knowledge, no investigators have looked at the relationship between dilated VRS on MR imaging and cerebral microvascular disease. Dilated perivascular spaces are common among the elderly and uncommon in children. Studies have noted the association between both developmental delay and non- syndromic autism and enlarged or dilated perivascular spaces. Non-syndromic autism categorizes autistic patients for which there is no known cause. Fig 3.1.6: Dilated Virchow robin
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International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 05 | May 2019 www.irjet.net p-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 7235 Temporal left and right lobe: The temporal lobe is involved in processing sensory input into derived meanings for the appropriate retention of visual memory, language comprehension, and emotion association. The temporallobe consists of structures thatarevital for declarativeorlong-term memory. Declarative (denotative) or explicit memory is conscious memory divided into semantic memory(facts)and episodic memory (events). Medial temporal lobe structures that are critical for long-term memory include the hippocampus, along with the surrounding hippocampal region consisting of the perirhinal, parahippocampal, and entorhinal neocortical regions. The hippocampus is critical for memory formation, andthesurroundingmedialtemporal cortex is currently theorized to be critical for memory storage. The prefrontal and visual cortices are also involved in explicit memory. The temporal lobes are involved in the primary organization of sensory input (Read, 1981). Individuals with temporal lobes lesions have difficultyplacing words or pictures into categories. Language can be affected by temporal lobe damage. Left temporal lesions disturb recognition of words. Right temporal damagecancausealoss of inhibition of talking. The temporal lobes are highly associated with memory skills. Left temporallesionsresultin impaired memory for verbal material. Right side lesions result in recall of non-verbal material, such as music and drawings. Seizures of the temporal lobe can have dramatic effects on an individual's personality. Temporal lobe epilepsy can cause perseverative speech, paranoia and aggressive rages. Fig 3.1.7: Temporal lobe problem First of all, trainer teaches malayalam letters, words etc. to autistic students with the help of audio. Then the trainer will ask the students to repeat the same. Those audios are recorded and send for analysis. So that the trainer will be aware about how much the student had learn. In addition to the checking of his/herlearningcapability,thestudentshealth condition is also checked. All the necessary details for understanding the health status is obtained from the test results provided by the students. Results of students are collected and it is fed in to the system. Then it is checked against the dataset collected from the hospitals. Finally a suggestion is given to the trainer. So that trainer can improve training of students according to their disease conditions. Autistic patients find it difficult to remember their daily schedule. Hence, a schedule notification is also provided. Hence, LASAC is trying to improve the lives of the most wonderful and differently abled students. Fig 3.1.8: Analysis of finding which algorithm is better Fig 3.1.9: Disease is being predicted and sound is analyzed using algorithms
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International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 05 | May 2019 www.irjet.net p-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 7236 Fig 3.1.10: Disease Prediction and Suggestion Page 3.2 ANALYSIS The analysis shows how much studenthadlearntandtherate of each disease in a group of individuals. By looking into this analysis it is possible to find the improvement of each student. In this project, disease prediction is based on ENT and brain results of student. 3.2.1 Analysis: ENT Statistics Here, the statistics of nose, ear and tongue condition in a group of students is depicted. From this result it is possible to find which part in the ENT section is affected mostly. This analysis will help to know theautisticsocietiescurrenthealth status. Fig 3.2.1.Analysis of ENT health condition in autistic society 3.2.2 Analysis: Brain Statistics Here, the statistics of brain MRI result inagroupofstudentsis depicted. From this result it is possible to find which part in the brain is affected mostly. Thus it is possible to understand their thinking, understanding and developmental capability. Fig 3.2.2.Analysis of brain MRI results of autistic society 3.2.3 Analysis: Sound spectrum analysis The sound of the autistic children is recorded and it is checked by using two methods i) Cosine similarity ii) Naïve bayes. The analysis of two methods is performed. Thus it is possible to find whether the student is pronouncing the word or letter correctly Fig 3.2.3.Sound spectrum analysis using cosine similarity and algorithm The similarity analysis will help us to find how much the student had learnt each letter. Thus it is possible to know the
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International Research Journal
of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 06 Issue: 05 | May 2019 www.irjet.net p-ISSN: 2395-0072 © 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 7237 improvement in learning. But the output of the cosine similarity was very much affected by noise in the environment. So to overcomethatdisadvantage,analgorithm is used to check the similarity. Naive bayes is the algorithm which is being used to check whether the sound of the student is correct or not. The output wasalmostcorrect.Here an analysis is performed on a group of autistic patients and find out that only 25% of the students were pronouncing the letters or words correctly. 4. CONCLUSION Autism is a group of complex disorders of brain development. The autism students need special training to make them familiar to regional language. To improve their reading and understandingskills,theLASACisdeveloped. By overcoming various disadvantages of traditional approach, the latest technologies like deep learning, neuro imaging are executed in the project. By this project, the life of the differently abled autism patients will be improved to an extent REFERENCES [1] Sherin M Wilson, Aswani S Rajan “Survey on Learning Assistance System For Autistic Children(LASAC)”. [2] Tarannum Zaki1, Muhammad Nazrul Islam2, Md. Sami Uddin3, Sanjida Nasreen Tumpa4, Md. Jubair Hossain5, Maksuda Rahman Anti6, Md. Mahedi Hasan“Towards Developing a Learning Tool for ChildrenwithAutism” Aug 2017 [3] Zhi Zheng, Qiang Fu, Huan Zhao, Amy R. Swanson, Amy S. Weitlauf, Zachary E. Warren, and Nilanjan Sarkar“Design of an Autonomous Social Orienting Training System (ASOTS) for Young Children with Autism”, Sep 2017 [4] ] Tarannum Zaki1, Muhammad Nazrul Islam2, Md. Sami Uddin3, Sanjida Nasreen Tumpa4, Md. Jubair Hossain5, Maksuda Rahman Anti6, Md.MahediHasan “Towards Developing a Learning Tool for Children with Autism” Aug 2017 [5] Fauzia Fazana1, Abeer Alsadoon1, P.W.C. Prasad1, Nectar Costadopoulos1, A. Elchouemi2, Sasikumaran Sreedharan3 1Charles Sturt University Study Centre, Sydney, Australia 2Walden University, USA “Integration of Assistive and Wearable Technology to Improve Communication, Social Interaction and Health Monitoring for ChildrenwithAutismSpectrum Disorder (ASD)”, Dec 2016 [6] Gema Syahidan Akbar Prawira, Emil R. Kaburuan, Veronikha Effendy “User Interface (UI) Design of Scheduling Activity Apps for Autistic Children” , Oct 2017 [7] Shobhit Tiwari,Sourav Khandelwal,Sanjiban Sekhar Roy “E-Learning Tool for Japanese language learning through English, Hindi and Tamil”, Jan 2018 [8] Li Guxin , Liu QiufangSoftwareDevelopmentCenterof Special Education, “Construction of Website-based Platform on Development Assessment of Children with Autism”, Sep 2015 BIOGRAPHIES Sherin M Wilson, She is currently pursuing her Masters degree in Computer Science and Engineering in Sree Buddha College of Engineering, Kerala, India. Her area of research include Intelligence, Data Mining and Security. Aswani S Rajan, She is an Assistant Professor in the Department of ComputerScienceandEngineering,Sree Buddha College of Engineering. Her main area of interest is Data Mining.
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