Towards Artificial Intelligence Driven Emotion Aware Fall Monitoring Framework Suitable for Elderly People with Neurological Disorder
Brain Informatics, 2020
The contemporary world’s emerging issue is how the mental health and falling of a senior citizen ... more The contemporary world’s emerging issue is how the mental health and falling of a senior citizen with a neurological disorder can be maintained living at their homes as the number of aged people is increasing with the rising of life expectancy. With the advancement of the Internet of Things (IoT) and big data analytics, several works had been done on smart home health care systems that deal with in house monitoring for fall detection. Despite so much work, the challenges remain for not considering emotional care in the fall detection system for the old ones. As a remedy to the problems mentioned above, we propose an emotion aware fall monitoring framework using IoT, Artificial Intelligence (AI) Algorithms, and Big data analytics, which will deal with emotion recognition of the aged people, predictions about health conditions, and real-time fall monitoring. In the case of an emergency, the proposed framework alerts about a situation of urgency to the predefined caregiver. A smart ambulance or mobile clinic will reach the older adult’s location at minimum time.
Fall causes trauma or critical injury among the geriatric population which is a second leading ac... more Fall causes trauma or critical injury among the geriatric population which is a second leading accidental cause of post-injury mortality around the world. It is crucial to keep elderly people under supervision by ensuring proper privacy and comfort. Thus the elderly fall detection and prediction using wearable/ non-wearable sensors become an active field of research. In this work, a novel pipeline for fall detection based on wearable accelerometer data has been proposed. Three publicly available datasets have been used to validate our proposed method, and more than 7700 cross-disciplinary time-series features were investigated for each of the datasets. After following a series of feature reduction techniques such as mutual information, removing highly correlated features using the Pearson correlation coefficient, Boruta algorithm, we have obtained the dominant features for each dataset. Different classical machine learning (ML) algorithms were utilized to detect falls based on the obtained features. For individual datasets, the simple ML classifiers achieved very good accuracy. We trained our pipeline with two of the three datasets and tested with the remaining one dataset until all three datasets were used as the test set to show the generalization capability of our proposed pipeline. A set of 39 high-performing features is selected, and the classifiers were trained with them. For all the cases, the proposed pipeline showed excellent efficiency in detecting falls. This architecture performed better than most of the existing works in all the used publicly available datasets, proving the supremacy of the proposed data analysis pipeline.
Bulletin of Electrical Engineering and Informatics, 2020
Handwritten character recognition is a very tough task in case of complex shaped alphabet set lik... more Handwritten character recognition is a very tough task in case of complex shaped alphabet set like Bangla script. As optical character recognition (OCR) has a huge application in mobile devices, model needs to be suitable for mobile applications. Many researches have been performed in this arena but none of them achieved satisfactory accuracy or could not detect more than 200 characters. MobileNet is a state of art (convolutional neural network) CNN architecture which is designed for mobile devices as it requires less computing power. In this paper, we used MobileNet for handwritten character recognition. It has achieved 96.46% accuracy in recognizing 231 classes (171 compound, 50 basic and 10 numerals), 96.17% accuracy in 171 compound character classes, 98.37% accuracy in 50 basic character classes and 99.56% accuracy in 10 numeral character classes.
Towards Social Group Optimization and Machine Learning Based Diabetes Prediction
2021 International Conference on Information and Communication Technology for Sustainable Development (ICICT4SD)
Diabetes has become a major health concern among people around the world. It may reduce the life ... more Diabetes has become a major health concern among people around the world. It may reduce the life span, and enhances the probability of various kind of cardiovascular diseases. Prediction of diabetes may provide an alarm to the people who are required to check their health status. It is a very challenging task as medical data is very much saturated and complex. In this work, we have predicted diabetes from several lifestyle parameters such as BMI, age, pregnancy and symptoms such as weight loss, visual blurring, weight loss, and so on. Several machine learning algorithms were used to predict diabetes. These machine learning algorithms were further optimized using Particle Swarm Optimizer and Social Group Optimizer. Social Group Optimized Gradient Boosted Classifier (GBC) achieved an accuracy of 71.85% in predicting diabetes from lifestyle parameters. The proposed architecture achieved 98.26% accuracy in case of prediction from symptoms using Social Group Optimized Random Forest Algorithm.
A Monitoring System for Patients of Autism Spectrum Disorder Using Artificial Intelligence
When the world is suffering from the deadliest consequences of COVID-19, people with autism find ... more When the world is suffering from the deadliest consequences of COVID-19, people with autism find themselves in the worst possible situation. The patients of autism lack social skills, and in many cases, show repetitive behavior. Many of them need outside support throughout their life. During the COVID-19 pandemic, as many of the places are in lockdown conditions, it is very tough for them to find help from their doctors and therapists. Suddenly, the caregivers and parents of the ASD patients find themselves in a strange situation. Therefore, we are proposing an artificial intelligence-based system that uses sensor data to monitor the patient’s condition, and based on the emotion and facial expression of the patient, adjusts the learning method through exciting games and tasks. Whenever something goes wrong with the patient’s behavior, the caregivers and the parents are alerted about it. We then presented how this AI-based system can help them during COVID-19 pandemic. This system ca...
Performance Analysis of State of the Art Convolutional Neural Network Architectures in Bangla Handwritten Character Recognition
Pattern Recognition and Image Analysis, 2021
Bangla handwritten character recognition is a popular research topic as its difficulty is higher ... more Bangla handwritten character recognition is a popular research topic as its difficulty is higher than the recognition of other languages because of multiple formats of compound characters. State of the art Convolutional neural network (CNN) architectures are very much useful in computer vision applications. Some works have been carried out in Bangla handwritten character recognition but most of them either not very efficient or they can not classify a lot of characters. In this work, state of art pre-trained CNN architectures is used to classify 231 different Bangla handwritten characters using CMATERdb dataset. The images were first converted to B&W form with white as the foreground color. The size of the images is reduced to 28 × 28 form. These images are used as input to the CNN architectures. The weights of the state-of-the-art CNN models are kept as it was. The training learning rate was set to 0.001 and categorical cross-entropy as the error function. After 50 epochs, Inceptio...
A Comprehensive Review on Recognition Techniques for Bangla Handwritten Characters
2019 International Conference on Bangla Speech and Language Processing (ICBSLP), 2019
Handwritten character recognition is a challenging task in OCR and for a cursive and complex char... more Handwritten character recognition is a challenging task in OCR and for a cursive and complex character set like Bangla, it is even harder to implement. Many researchers have proposed different methods for recognizing Bangla Handwritten character set. It is done through analyzing the structure of the characters or through some machine learning process. This paper represents an analysis and overview of the existing methods for recognizing handwritten basic and compound characters. Methods, success rate, limitations and future scope has been mentioned in this paper. The purpose of this paper is to find out the fields in which the systems necessitate improvement and contribute to establish an ideal Bangla Handwritten Character Recognition System.
Social Group Optimized Machine-Learning Based Elderly Fall detection Approach Using Interdisciplinary Time-Series Features
2021 International Conference on Information and Communication Technology for Sustainable Development (ICICT4SD)
Fall can be life-taking for elderly people. Early recognition of fall events can reduce the impac... more Fall can be life-taking for elderly people. Early recognition of fall events can reduce the impact among this people. Fall detection has become an active research area now a days. Many researches have been carried out using a lot of sensors and deep learning algorithms that are costly and computationally expensive. In this work, a fall detection system with a simple accelerometer and interdisciplinary time domain features were introduced. Three publicly available datasets were used and five machine learning classifiers were finally introduced to classify fall events. Dimensionality reduction using principal component analysis was also studied in this work. Finally, machine learning classifiers were optimized using social group optimization to find out best performing hyperparameters. The proposed architecture showed great robustness and efficiency as it achieved almost perfect accuracy, sensitivity and specificity in all three datasets.
Towards Artificial Intelligence Driven Emotion Aware Fall Monitoring Framework Suitable for Elderly People with Neurological Disorder
Brain Informatics
The contemporary world’s emerging issue is how the mental health and falling of a senior citizen ... more The contemporary world’s emerging issue is how the mental health and falling of a senior citizen with a neurological disorder can be maintained living at their homes as the number of aged people is increasing with the rising of life expectancy. With the advancement of the Internet of Things (IoT) and big data analytics, several works had been done on smart home health care systems that deal with in house monitoring for fall detection. Despite so much work, the challenges remain for not considering emotional care in the fall detection system for the old ones. As a remedy to the problems mentioned above, we propose an emotion aware fall monitoring framework using IoT, Artificial Intelligence (AI) Algorithms, and Big data analytics, which will deal with emotion recognition of the aged people, predictions about health conditions, and real-time fall monitoring. In the case of an emergency, the proposed framework alerts about a situation of urgency to the predefined caregiver. A smart ambulance or mobile clinic will reach the older adult’s location at minimum time.
Fall causes trauma or critical injury among the geriatric population which is a second leading ac... more Fall causes trauma or critical injury among the geriatric population which is a second leading accidental cause of post-injury mortality around the world. It is crucial to keep elderly people under supervision by ensuring proper privacy and comfort. Thus the elderly fall detection and prediction using wearable/ non-wearable sensors become an active field of research. In this work, a novel pipeline for fall detection based on wearable accelerometer data has been proposed. Three publicly available datasets have been used to validate our proposed method, and more than 7700 cross-disciplinary time-series features were investigated for each of the datasets. After following a series of feature reduction techniques such as mutual information, removing highly correlated features using the Pearson correlation coefficient, Boruta algorithm, we have obtained the dominant features for each dataset. Different classical machine learning (ML) algorithms were utilized to detect falls based on the obtained features. For individual datasets, the simple ML classifiers achieved very good accuracy. We trained our pipeline with two of the three datasets and tested with the remaining one dataset until all three datasets were used as the test set to show the generalization capability of our proposed pipeline. A set of 39 high-performing features is selected, and the classifiers were trained with them. For all the cases, the proposed pipeline showed excellent efficiency in detecting falls. This architecture performed better than most of the existing works in all the used publicly available datasets, proving the supremacy of the proposed data analysis pipeline. INDEX TERMS Machine learning, feature selection, activities of daily living, feature extraction, signal magnitude vector.
The novel coronavirus disease (COVID-19) pandemic is provoking a prevalent consequence on mental ... more The novel coronavirus disease (COVID-19) pandemic is provoking a prevalent consequence on mental health because of less interaction among people, economic collapse, negativity, fear of losing jobs, and death of the near and dear ones. To express their mental state, people often are using social media as one of the preferred means. Due to reduced outdoor activities, people are spending more time on social media than usual and expressing their emotion of anxiety, fear, and depression. On a daily basis, about 2.5 quintillion bytes of data are generated on social media, analyzing this big data can become an excellent means to evaluate the effect of COVID-19 on mental health. In this work, we have analyzed data from Twitter microblog (tweets) to find out the effect of COVID-19 on peoples mental health with a special focus on depression. We propose a novel pipeline, based on recurrent neural network (in the form of long-short term memory or LSTM) and convolutional neural network, capable ...
An earthquake is a tremor felt on the surface of the earth created by the movement of the major p... more An earthquake is a tremor felt on the surface of the earth created by the movement of the major pieces of its outer shell. Till now, many attempts have been made to forecast earthquakes, which saw some success, but these attempted models are specific to a region. In this paper, an earthquake occurrence and location prediction model is proposed. After reviewing the literature, long short-term memory (LSTM) is found to be a good option for building the model because of its memory-keeping ability. Using the Keras tuner, the best model was selected from candidate models, which are composed of combinations of various LSTM architectures and dense layers. This selected model used seismic indicators from the earthquake catalog of Bangladesh as features to predict earthquakes of the following month. Attention mechanism was added to the LSTM architecture to improve the model's earthquake occurrence prediction accuracy, which was 74.67%. Additionally, a regression model was built using LSTM and dense layers to predict the earthquake epicenter as a distance from a predefined location, which provided a root mean square error of 1.25.
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Papers by Tapotosh Ghosh