Abstract

Healthcare is one of the basic human rights, but providing proper healthcare in rural remote areas of developing countries has always been a challenging task. Telemonitoring has brought some relief with limitations due to accessing patient's health information by healthcare workers and specialists, but merging it with cloud services can be an optimal solution. This proposed framework, CMED (Cloud based MEDical system), is a setup consisting of both healthcare center and a portable healthcare service with a Community Health Worker(CHW), which will sort the patients into three different categories—healthy, alarming and emergency. Upon failing to diagnose a unique problem, it will suggest and contact a specialist doctor and provide suggestions. The privacy and confidentiality of patient's health information will be secured by Identity Based Encryption. In this paper, a framework has been presented for smart and secure healthcare monitoring system for rural under privileged people in developing countries.

Abstract:

Bangladesh’s health care system, particularly in rural areas, experiences enormous obstacles in
providing complete preventive and primary healthcare services due to the lack of adequate healthcare facilities,
resource constraints, and a non-functional referral system. To alleviate these problems, in this study, we introduce
the digital general practitioner (GP) model for rural Bangladesh, digital platforms and present a statistical
analysis of the data that was gathered from the pilot project.
Methods: A total of 12,746 people were provided regular health services during the pilot project, from all genders
and age groups, and provided their socio-demographic and healthcare-related data. We analyzed healthcarerelated
data by carrying out both descriptive and inferential statistics.
Results: By utilizing this digital GP model, rural residents can receive routine health screenings at their homes,
identify health risks early, receive consultation and health education, and be referred to GP and upper-level
health facilities as needed. We found that hypertension was more prevalent (4.84% of the served population),
and cancer was the least prevalent of all the NCDs in the studied population (0.05% of the served population).
The population for stroke, hypertension, diabetes increased until the 50–59 age range as age increased, following
which the population proportion declined as age increased. Additionally, 3.96% of young females were severely
malnourished, comparably higher proportion than young males (2.34%).
Conclusion: NCDs such as hypertension, diabetes was prevalent among rural people. Necessary steps should be
taken to raise preventive and primary healthcare awareness among rural people.
Public interest summary: The absence of proper healthcare facilities, resource constraints, and a non-functional
referral system hamper Bangladesh’s health care system’s ability to provide comprehensive preventive and
primary healthcare services in rural area. As a result, patients develop advanced ailments, including noncommunicable
diseases (NCDs), and must seek treatment at an expensive specialty hospital. To resolve this
issue, we introduce a digital GP model for rural Bangladesh, then show digital platforms that use the concept, and
lastly summarize significant findings from the piloted digital GP model. By utilizing this digital GP model, rural
residents can receive routine health screenings at their homes, identify health risks early, receive consultation
and health education, and be referred to GP and upper-level health facilities as need. From our data analysis, we
discovered high burden of NCDs such as hypertension and diabetes in the piloted area. Necessary steps should be
taken to raise preventive and primary healthcare awareness among rural people.

Abstract:

Over the past 10 years, the use of augmented reality (AR) applications to assist individuals with special needs such as intellectual disabilities, autism spectrum disorder (ASD), and physical disabilities has become more widespread. The beneficial features of AR for individuals with autism have driven a large amount of research into using this technology in assisting against autism-related impairments. This study aims to evaluate the effectiveness of AR in rehabilitating and training individuals with ASD through a systematic review using the PRISMA methodology. A comprehensive search of relevant databases was conducted, and 25 articles were selected for further investigation after being filtered based on inclusion criteria. The studies focused on areas such as social interaction, emotion recognition, cooperation, learning, cognitive skills, and living skills. The results showed that AR intervention was most effective in improving individuals’ social skills, followed by learning, behavioral, and living skills. This systematic review provides guidance for future research by highlighting the limitations in current research designs, control groups, sample sizes, and assessment and feedback methods. The findings indicate that augmented reality could be a useful and practical tool for supporting individuals with ASD in daily life activities and promoting their social interactions.

Abstract

There are different kinds of technologies being used to improve the communication opportunity of children with verbal challenges. Portable communication devices provide the facility to the users for customizing applications according to their needs. Besides, the usability and costing range of these portable communication devices make them less condemning for the users. On the other hand, communication devices efficiently help the users with less motor skills. After considering these points, a communication device named “Bolte Chai”, has been developed and experimented in a special school’s students and teachers based on five criteria to evaluate usability and effectiveness. Those criteria are, the ability to customize the program to fit the needs of the users, motor skills for operating the applications, reasonable time and resources involved in the teaching and utilization procedure, update the system based on current research and proper costing, considering the user’s affordability. Based on the evaluation, it has been seen that this communication device is usable and effective to improve communication skills of nonverbal children.

Abstract: 

The Brain-Computer Interface (BCI) is a communication channel between the brain and the computer. It works by detecting the production of currents due to brain cell activity. Among many non-invasive brain signal recording methods, Electroencephalography (EEG) is one of the most popular methods. EEG controlled applications widely range from strictly medical to non-medical applications. Non-medical applications can not only be used for entertainment purposes, but also can help a subject to experience BCI application, achieve better control with rehabilitation systems and can be a strong motivation to practice BCI system. Brain signals are weak and contain lots of artifacts like muscle movements, cardiac, eye blink, power source, and amplitude artifacts. Although eye blink is considered as one of the strongest artifacts, this study uses eye blink as a tool to control signals to play BlinkFruity game, where users collect fruits into a basket using eye blink only. To attain this objective, at first, the brain activity was recorded using OpenBCI device and 500ms window data was taken to process in real-time. Then the notch filter was applied to remove powerline noise. The eye blink was detected by using signal thresholding method reading from EEG data. Blink detection

Abstract

Depression is the most common mental illness, which has become the major cause of fear and suicidal mortality or tendencies. Currently, about 10% of the world population has been suffering from depression. The classical approach for detecting depression relies on the clinical questionnaire, which depends on the patients’ responses as well as observing their behavioral activities. However, there is no established method to detect depression from EEG biomarkers. Therefore, exploration of EEG biomarkers for depression assessments is vital and has a great potential to improve our understanding and clinical interventions. In this study, we have conducted a systematic review of 52 research articles using the PRISMA-P systematic review protocol, where we analyzed their research methodologies and outcomes. We categorized the experimentations in these articles according to their physical and psychological aspects scaled by the commonly used clinical questionnaire-based assessments. This study finds that the negative stimuli are the better identification strategies for evaluating depression through EEG signals. From this exploration, researchers observed that the Neural Connectivity Analysis and Brain Topological Mapping have huge potentials for finding depression biomarkers, and it is evident that the right-side hemisphere and frontal and parietal-occipital cortex are distinct regions to detect depression using EEG signals. For this mechanism, researchers are using many signal processing and machine learning approaches. In the case of filtering, Independent Component Analysis (ICA) is commonly used to eliminate physiological and non-physiological artifacts. Among machine learning approaches, Convolutional Neural Network (CNN) and Support Vector Machine (SVM) showed better performance for classifying healthy and depressed brains. The authors hope this study will create an opportunity to explore more in the future for EEG as a diagnostic tool by analyzing brain functional connectivity for focusing on clinical interventions. 

Abstract

Call support centers operate over the telephone, connecting customers and receptionists to ensure customer satisfaction by solving their problems. Due to pandemics, customer call support centers have become a popular way of communication that has been used in different domains such as e-commerce, hospitals, banks, credit card support, government offices. Moreover, humans’ limitations to serve 24 hours a day and the fluctuation of waiting time makes it more challenging to satisfy all the customers over call centers. So, customer service needs to be automated to handle customers by providing a domain-based response in the native language, especially in a developing country like Bangladesh, where call support centers are increasing. Although most people use the Bangla language to communicate, little work has been done in customer care automation in the native language. Our developed architecture, “AIMS TALK” can respond to that customer’s need by recognizing users’ voices, specifying customers’ problems in the standardized Bangla language, collecting customers’ responses to the database to give feedback according to the queries. Besides, the system uses MFCC feature extraction for speaker recognition with an average accuracy of 94.38% on 42 people in real-time testing, an RNN-based model for Bangla Automatic Speech Recognition (ASR) with a word error rate (WER) of 42.15%, and sentence summarization we used Sentence similarity measurement technique having an average loss of 0.004. Lastly, we used gTTS that works as Text to Speech Synthesis for the Bangla language in WavNet architecture.