A study on tiredness assessment by using eye blink detection

In this paper, the loss of attention of automotive drivers is studied by using eye blink detection. Facial landmark detection for detecting eye is explored. Afterward, eye blink is detected using Eye Aspect Ratio. By comparing the time of eye closure to a particular period, the driver’s tiredness is decided. The total number of eye blinks in a minute is counted to detect drowsiness. Calculation of total eye blinks in a minute for the driver is done, then compared it with a known standard value. If any of the above conditions fulfills, the system decides the driver is unconscious. A total of 120 samples were taken by placing the light source front, back, and side. There were 40 samples for each position of the light source. The maximum error rate occurred when the light source was placed back with a 15% error rate. The best scenario was 7.5% error rate where the light source was placed front side. The eye blinking process gave an average error of 11.67% depending on the various position of the light source. Another 120 samples were taken at a different time of the day for calculating total eye blink in a minute. The maximum number of blinks was in the morning with an average blink rate of 5.78 per minute, and the lowest number of blink rate was in midnight with 3.33% blink rate. The system performed satisfactorily and achieved the eye blink pattern with 92.7% accuracy

A study of left ventricular (LV) segmentation on cardiac cine-MR images

Left ventricular segmentation from cardiac images has high impact to have early diagnosis of various cardiovascular disorders. However, it is really a challenging task to segment left ventricular images from magnetic resonance image (MRI). In this paper, we explore several state-of-the-art segmentation algorithms applied on left ventricular (LV) segmentation on cardiac cine-MR images. Both adaptive and global thresholding algorithms along with region-based segmentation algorithm have been explored. Edge-based segmentation is disregard due to the absence of edge information in the employed dataset. For evaluation, we explored a benchmark dataset that was used for the MICCAI 3D segmentation challenge. We found that the cardiac MRI global thresholding has proved to be much efficient and robust than the adaptive thresholding. We achieved more than 92% accuracy for global thresholding, whereas, about 78% accuracy for the adaptive thresholding approach. The use of entropy or histogram to characterize segmentation in place of the intensity value of the pixel has a vital effect on segmentation efficiency. It is evident that the intensity information is corrupted by acquisition procedure, as well as the structure of organs. Due to the lack of boundary information in cardiac cine-MRI, clustering and region-based segmentation have produced more than 93% segmentation accuracy. For the case of soft clustering, the increased accuracy is found as 96%. However, more explorations are required, specially based on deep learning approaches on very large datasets

An optimized structure for enhancing optical absorption of solar energy inelliptical gaAs nanowire array solar cell

The enhanced optical absorption of solar energy in a solar cell using nanostructured materials is a very demanding and important research area. Hence, an optimized structure to increase the optical absorption of solar energy in an elliptical GaAs (Gallium Arsenide) nanowire array solar cell was proposed in this paper. The influence of geometric parameters on the optical absorption of elliptical GaAs nanowire solar cells was investigated by exploiting the finite difference in time domain simulations. Based on the design and analysis, the elliptical GaAs nanowire array performed better than any circular GaAs nanowire. It was found that the enhanced performance was due to the reduced reflection from the top surface and a reduced transmission from the bottom surface of the elliptical GaAs nanowire array. The structural parameters of the elliptical GaAs nanowire were optimized by calculating the short circuit current density (Jsc) for different geometries over the solar spectrum. It was demonstrated that there was an increase of 9.4% in the Jsc compared with the circular GaAs nanowire under the optimized conditions

A Method for Sensor-Based Activity Recognition in Missing Data Scenario

Sensor-based human activity recognition has various applications in the arena of healthcare, elderly smart-home, sports, etc. There are numerous works in this field—to recognize various human activities from sensor data. However, those works are based on data patterns that are clean data and have almost no missing data, which is a genuine concern for real-life healthcare centers. Therefore, to address this problem, we explored the sensor-based activity recognition when some partial data were lost in a random pattern. In this paper, we propose a novel method to improve activity recognition while having missing data without any data recovery. For the missing data pattern, we considered data to be missing in a random pattern, which is a realistic missing pattern for sensor data collection. Initially, we created different percentages of random missing data only in the test data, while the training was performed on good quality data. In our proposed approach, we explicitly induce different percentages of missing data randomly in the raw sensor data to train the model with missing data. Learning with missing data reinforces the model to regulate missing data during the classification of various activities that have missing data in the test module. This approach demonstrates the plausibility of the machine learning model, as it can learn and predict from an identical domain. We exploited several time-series statistical features to extricate better features in order to comprehend various human activities. We explored both support vector machine and random forest as machine learning models for activity classification. We developed a synthetic dataset to empirically evaluate the performance and show that the method can effectively improve the recognition accuracy from 80.8% to 97.5%. Afterward, we tested our approach with activities from two challenging benchmark datasets: the human activity sensing consortium (HASC) dataset and single chest-mounted accelerometer dataset. We examined the method for different missing percentages, varied window sizes, and diverse window sliding widths. Our explorations demonstrated improved recognition performances even in the presence of missing data. The achieved results provide persuasive findings on sensor-based activity recognition in the presence of missing data

Assessment of pulmonary function of post-COVID-19 patients of a tertiary hospital at three months following discharge

In this cross-sectional study from October 2020 to September 2021, pulmonary function was assessed in 40 (24 severe and 16 non-severe) post- COVID-19 patients at three months following hospital discharge by convenient sampling in post-COVID follow-up clinic at Department of Respiratory Medicine, Bangabandhu Sheikh Mujib Medical University, Dhaka. Among 40 subjects, 17 (42.5%) had restrictive defect, 1 (2.5%) had obstructive defect and 22 (55%) had normal spirometry. There was reduced total lung capacity (TLC) in 17 (42.5%) subjects and reduced diffusing lung capacity for carbon monoxide (DLCO) in 20 (50%) subjects. Mean six minutes walking distance (6MWD) was low (387.6±99.4 meters). Oxygen desaturation was in 4 (10%) cases. Frequency of restrictive defect was higher in severe than non-severe subjects (54.2% vs. 25%). Reduced TLC was in 62.5% of severe and 12.5% of non-severe cases. There was higher frequency of reduced DLCO in severe than non-severe casese (62.5% vs. 31.3%). 6MWD was lower in severe than non-severe group (354.0±134.9 vs. 438.2±51.4 meters, P=0.02). Pulmonary function was found significantly impaired in post-COVID patients which was more prominent in severe disease group. BSMMU J 2022; 15(3): 192-19

Status of deep learning for EEG-based brain–computer interface applications

In the previous decade, breakthroughs in the central nervous system bioinformatics and computational innovation have prompted significant developments in brain–computer interface (BCI), elevating it to the forefront of applied science and research. BCI revitalization enables neurorehabilitation strategies for physically disabled patients (e.g., disabled patients and hemiplegia) and patients with brain injury (e.g., patients with stroke). Different methods have been developed for electroencephalogram (EEG)-based BCI applications. Due to the lack of a large set of EEG data, methods using matrix factorization and machine learning were the most popular. However, things have changed recently because a number of large, high-quality EEG datasets are now being made public and used in deep learning-based BCI applications. On the other hand, deep learning is demonstrating great prospects for solving complex relevant tasks such as motor imagery classification, epileptic seizure detection, and driver attention recognition using EEG data. Researchers are doing a lot of work on deep learning-based approaches in the BCI field right now. Moreover, there is a great demand for a study that emphasizes only deep learning models for EEG-based BCI applications. Therefore, we introduce this study to the recent proposed deep learning-based approaches in BCI using EEG data (from 2017 to 2022). The main differences, such as merits, drawbacks, and applications are introduced. Furthermore, we point out current challenges and the directions for future studies. We argue that this review study will help the EEG research community in their future research

Can Ensemble of Classifiers Provide Better Recognition Results in Packaging Activity?

Skeleton-based Motion Capture (MoCap) systems have been widely used in the game and film industry for mimicking complex human actions for a long time. MoCap data has also proved its effectiveness in human activity recognition tasks. However, it is a quite challenging task for smaller datasets. The lack of such data for industrial activities further adds to the difficulties. In this work, we have proposed an ensemble-based machine learning methodology that is targeted to work better on MoCap datasets. The experiments have been performed on the MoCap data given in the Bento Packaging Activity Recognition Challenge 2021. Bento is a Japanese word that resembles lunch-box. Upon processing the raw MoCap data at first, we have achieved an astonishing accuracy of 98% on 10-fold Cross-Validation and 82% on Leave-One-Out-Cross-Validation by using the proposed ensemble model

Assessment of pulmonary function of post-COVID-19 patients of a tertiary hospital at three months following discharge

In this cross-sectional study from October 2020 to September 2021, pulmonary function was assessed in 40 (24 severe and 16 non-severe) post- COVID-19 patients at three months following hospital discharge by convenient sampling in post-COVID follow-up clinic at Department of Respiratory Medicine, Bangabandhu Sheikh Mujib Medical University, Dhaka. Among 40 subjects, 17 (42.5%) had restrictive defect, 1 (2.5%) had obstructive defect and 22 (55%) had normal spirometry. There was reduced total lung capacity (TLC) in 17 (42.5%) subjects and reduced diffusing lung capacity for carbon monoxide (DLCO) in 20 (50%) subjects. Mean six minutes walking distance (6MWD) was low (387.6±99.4 meters). Oxygen desaturation was in 4 (10%) cases. Frequency of restrictive defect was higher in severe than non-severe subjects (54.2% vs. 25%). Reduced TLC was in 62.5% of severe and 12.5% of non-severe cases. There was higher frequency of reduced DLCO in severe than non-severe casese (62.5% vs. 31.3%). 6MWD was lower in severe than non-severe group (354.0±134.9 vs. 438.2±51.4 meters, P=0.02). Pulmonary function was found significantly impaired in post-COVID patients which was more prominent in severe disease group. BSMMU J 2022; 15(3): 192-19

Wearable Sensor-Based Gait Analysis for Age and Gender Estimation

Wearable sensor-based systems and devices have been expanded in different application domains, especially in the healthcare arena. Automatic age and gender estimation has several important applications. Gait has been demonstrated as a profound motion cue for various applications. A gait-based age and gender estimation challenge was launched in the 12th IAPR International Conference on Biometrics (ICB), 2019. In this competition, 18 teams initially registered from 14 countries. The goal of this challenge was to find some smart approaches to deal with age and gender estimation from sensor-based gait data. For this purpose, we employed a large wearable sensor-based gait dataset, which has 745 subjects (357 females and 388 males), from 2 to 78 years old in the training dataset; and 58 subjects (19 females and 39 males) in the test dataset. It has several walking patterns. The gait data sequences were collected from three IMUZ sensors, which were placed on waist-belt or at the top of a backpack. There were 67 solutions from ten teams—for age and gender estimation. This paper extensively analyzes the methods and achieved-results from various approaches. Based on analysis, we found that deep learning-based solutions lead the competitions compared with conventional handcrafted methods. We found that the best result achieved 24.23% prediction error for gender estimation, and 5.39 mean absolute error for age estimation by employing angle embedded gait dynamic image and temporal convolution network

コンピュータを用いた人の動作認識と挙動解析の研究

九州工業大学博士学位論文（要旨） 学位記番号:工博甲第289号 学位授与年月日:平成21年9月30日九州工業大