Clinical devices play a vital role in diagnosing and monitoring people’s health. A pulse oximeter (PO) is one of the most common clinical devices for critical medical care. In this paper, we explain how we developed a wearable PO. We propose a new electronic circuit based on an analog filter that can separate red and green photoplethysmography (PPG) signals, acquire clean PPG signals, and estimate the pulse rate (PR) and peripheral capillary oxygen saturation (SpO₂). We propose a PR and SpO₂ measurement algorithm with and without the motion artifact. We consider three types of motion artifacts with our acquired clean PPG signal from our proposed electronic circuit. To evaluate our proposed algorithm, we measured the accuracy of our estimated SpO₂ and PR. To evaluate the quality of our estimated PR (bpm) and SpO₂ (%) with and without the finger motion artifact, we used the quality evaluation metrics: mean absolute percentage error (MAPE), mean absolute error (MAE), and reference closeness factor (RCF). Without the finger motion condition, we found that our proposed wearable PO device achieved an average 2.81% MAPE, 2.08 bpm MAE, 0.97 RCF, and 98.96% SpO₂ accuracy. With a finger motion, the proposed wearable PO device achieved an average 4.5% MAPE, 3.66 bpm MAE, 0.96 RCF, and 96.88% SpO₂ accuracy. We also show a comparison of our proposed PO device with a commercial Fingertip PO (FPO) device. We have found that our proposed PO device performs better than the commercial FPO device under finger motion conditions. To demonstrate the implementation of our wearable PO, we developed a smartphone app to allow the PO device to share PPG signals, PR, and SpO₂ through Bluetooth communication. We also show the possible applications of our proposed PO as a wearable, hand-held PO device, and a PPG signal acquisition system.

Blood cell image classification is an important part for medical diagnosis system. In this paper, we propose a fused convolutional neural network (CNN) model to classify the images of white blood cell (WBC). We use five convolutional layer, three max-pooling layer and a fully connected network with single hidden layer. We fuse the feature maps of two convolutional layers by using the operation of max-pooling to give input to the fully connected neural network layer. We compare the result of our model accuracy and computational time with CNN-recurrent neural network (RNN) combined model. We also show that our model trains faster than CNN-RNN model.

Most of the paralyzed or physically disabled persons are unable to communicate with others, easily. To minimize this problem, different types of Human-Computer Interface (HCI) systems have been developed in recent years. In this paper, a single channel electrooculography (EOG) based HCI system has been proposed to increase the communication ability as well as quality of life for paralyzed persons who cannot speak or move their limbs. The extracted EOG signals are processed by our EOG acquisition system and sent it to a microcontroller unit which processes those signals for interfacing with computer via serial communication. A Graphical User Interface (GUI) is designed using MATLAB which contains some buttons to help a user to express what he/she wants through messages. A liquid crystal display (LCD) is used to show the messages. Our experimental results show that the maximum and minimum average time recorded for selecting 10 buttons for a particular user are 4.27 and 4.11 second, respectively. Particularly for selecting a button, the maximum and minimum average time recorded by every user are respectively 5.58 second and 1.82 second. We have found that the average button selection accuracy is around 95%.

Low dynamic range (LDR) image may contain low‐light and highlight areas due to the limitations of the dynamic range of conventional image sensors. Low‐light and highlight phenomena limit colour richness and visibility of objects in an image. Therefore, it can cause a reduction in the quality of images and a loss in accuracy in the application of image recognition. To overcome this, high dynamic range (HDR)‐like images have been developed with rich colours such as those seen by the human eye. In this study, the authors propose a method to obtain an HDR‐like image from a single LDR image by removing the specular component from highlight pixels as well as strengthening the actual colour. Next, they select low‐light image enhancement via illumination map estimation as a low‐light enhancement technique by showing the comparison with gamma‐based expansion operator. They evaluate their HDR‐like output images with non‐reference and full‐reference metrics. They show the comparison of their proposed method with six other methods. Besides, visually, their proposed method delivers more pleasing output than the output of other competitive methods.