دانلود مقاله ارزیابی اختلال طیف اوتیسم
خلاصه
1. معرفی
2. بررسی ادبیات
3. روش شناسی
4. رویکردهای ASD
5. بحث
6. مسائل را باز کنید
7. نتیجه گیری
در دسترس بودن داده ها
تضاد علاقه
منابع
Abstract
1. Introduction
2. Literature Review
3. Methodology
4. ASD Approaches
5. Discussion
6. Open Issues
7. Conclusion
Data Availability
Conflicts of Interest
References
چکیده
رفتارهای کودکان مبتلا به اختلال طیف اوتیسم (ASD) اغلب نامنظم است و پیش بینی آن دشوار است. بیشتر اوقات، آنها قادر به برقراری ارتباط موثر به زبان خود نیستند. در عوض، آنها با استفاده از حرکات دست و عبارات اشاره ارتباط برقرار می کنند. به همین دلیل، درک نیازهای بیماران برای مراقبین ممکن است دشوار باشد، اگرچه تشخیص زودهنگام این بیماری می تواند این امر را بسیار ساده تر کند. فناوری کمکی و اینترنت اشیا (IoT) می تواند عدم وجود ارتباطات کلامی و غیرکلامی را در جامعه کاهش دهد. راه حل های مبتنی بر اینترنت اشیا از الگوریتم های یادگیری ماشینی (ML) و یادگیری عمیق (DL) برای تشخیص و بهبود زندگی بیماران استفاده می کنند. بررسی کامل تکنیک‌های ASD در تنظیمات دستگاه‌های IoT در این تحقیق ارائه شده است. شناسایی روندهای مهم در تحقیقات مراقبت های بهداشتی مبتنی بر اینترنت اشیا، هدف اصلی این بررسی است. همچنین یک طبقه بندی فنی برای سازماندهی مقالات فعلی در مورد الگوریتم ها و روش های ASD بر اساس عوامل مختلف مانند هوش مصنوعی، شبکه SS، ML و IoT وجود دارد. بر اساس معیارهایی مانند دقت و حساسیت، تحلیل های آماری و عملیاتی تکنیک های ASD مورد بررسی ارائه شده است.
Abstract
The behaviors of children with autism spectrum disorder (ASD) are often erratic and difficult to predict. Most of the time, they are unable to communicate effectively in their own language. Instead, they communicate using hand gestures and pointing phrases. Because of this, it can be difficult for caregivers to grasp their patients’ requirements, although early detection of the condition can make this much simpler. Assistive technology and the Internet of Things (IoT) can alleviate the absence of verbal and nonverbal communication in the community. The IoT-based solutions use machine Learning (ML) and deep learning (DL) algorithms to diagnose and enhance the lives of patients. A thorough review of ASD techniques in the setting of IoT devices is presented in this research. Identifying important trends in IoT-based health care research is the primary objective of this review. There is also a technical taxonomy for organizing the current articles on ASD algorithms and methodologies based on different factors such as AI, SS network, ML, and IoT. On the basis of criteria such as accuracy and sensitivity, the statistical and operational analyses of the examined ASD techniques are presented.
Introduction
Disabilities in social behavior and interaction are characteristics of ASD. According to Jon Baio [1], an estimated 1 in every 59 children is diagnosed with ASD. Special care and welfare facilities are needed by all impaired children more than by healthy youngsters [2]. In addition to limiting the lives of the sufferers, this long-term condition has a detrimental impact on their caretakers’ quality of life (QoL). Patients can be monitored remotely using systems based on IoT devices, which have numerous beneficial characteristics. There have so been a number of healthcare applications leveraging IoT devices in recent years. GPS, heart rate, microphone, and ear clips [2] are some of the most common IoT sensors used in wearable devices like smartwatches and smartphones. Sensors and devices are used to identify autistic youngsters, rather than traditional techniques of diagnosis [3, 4].
To help protect youngsters from developing life-threatening disorders, several studies have been conducted during the last decade. However, there were no major breakthroughs. Hence, the most important components of assisting the patient are early diagnosis and improving the QoL of the patients. Autistic children are frequently misdiagnosed until they are two years old [4]. As a result, they are still unable to carry out their daily routines. Consequently, this article examines several IoT device techniques for children with ASD to evaluate and contrast novel ways of detecting the disorder or enhancing quality of life for individuals already diagnosed [4, 5]. The Internet of Things is using artificial intelligence, machine learning, SS network, and deep learning to identify and protect patients from physical and emotional problems [5]. Patients’ vital signs are gathered by these systems, which then use various machine learning and deep learning algorithms to select the most appropriate responses. They may even be able to assist in the early detection of ASD. There are risky behaviors that autistic children perform when they are irritated, which can impair their physical health. An alarm is sent to caretakers and doctors, informing them of the condition and requesting assistance. Every one of these IoT-based devices monitors the body’s vital signs and records any changes depending on a variety of criteria (e.g., sensitivity, specificity, time, and accuracy) [6].
Conclusion
Patients with ASD benefit greatly from the adoption of IoT devices with SS network consist. One of the most difficult aspects of treating autism in children is finding the correct IoT solutions. Sensors, platforms, and methodologies in ASD can have a significant influence on the children, and this is often the case. There were 28 articles included in this review that looked at various methods to ASD published between 2014 and 2020. In both 2016 and 2018, the number of articles published was close to the previous year’s total. The most papers are published in the IEEE journal, with a percentage of 51%. Selected 28 studies were divided into two groups: those that focused on diagnosing patients and those that supported efforts to enhance the QoL of such patients. Nearly 43% of respondents thought of studies examining new methods for diagnosing and assessing the severity of ASD in children, while 57% thought about ways to enhance the quality of life for such youngsters. Additionally, all of the selected methodologies were evaluated in terms of accuracy, sensitivity, specificity, and time, among other variables. There has been a comparative examination of ASD and IoT-based devices based on the case studies offered. Most research studies are aimed at enhancing the QoL of autistic children, according to the findings. New ASD techniques and gadgets for autistic persons will benefit from research into IoT-based variations on traditional ASD approaches such as AL, ML, and SS network.
To keep classic ASD techniques safe, IoT-based devices are becoming more and more common. Also, several studies have shown that children with ASD prefer robots over people for assistance. NAO’s eye contact attention is steadier than in a traditional classroom for youngsters with ASD [47]. In addition, IoT solutions cut caregiving and support expenses and are simple to use, in contrast to traditional methods of protecting patients.