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Soft sensors are attracting much attention from researchers worldwide due to their versatility in practical projects. There are already many applications of soft sensors in aspects of life, consisting of human-robot interfaces, flexible electronics, medical monitoring, and healthcare. However, most of these studies have focused on a specific area, such as fabrication, data analysis, or experimentation. This approach can lead to challenges regarding the reliability, accuracy, or connectivity of the components. Therefore, there is a pressing need to consider the sensor's placement in an overall system and find ways to maximize the efficiency of such flexible sensors. This paper proposes a fabrication method for soft capacitive pressure sensors with spacer fabric, conductive inks, and encapsulation glue. The sensor exhibits a good sensitivity of 0.04 kPa−1, a fast recovery time of 7 milliseconds, and stability of 10,000 cycles. We also evaluate how to connect the sensor to other traditional sensors or hardware components. Some machine learning models are applied to these built-in soft sensors. As expected, the embedded wearables achieve a high accuracy of 96% when recognizing human walking phases. [ABSTRACT FROM AUTHOR]

An accurate and compact electrocardiogram (ECG) device will greatly assist doctors in diagnosing heart diseases. It will also help to address the increasing number of deaths caused by heart disease. Accordingly, the goal of the project is to design and construct an easy-to-use compact 12-lead electrocardiogram device that communicates with a computer to create a system that can continuously monitor heart rate and which can be connected to allied medical systems. The design is based on an ECG receiver circuit utilizing an IC ADS1293 and an Arduino Nano. The ADS1293 has built-in input Electromagnetic Interference (EMI) filters, quantizers, and digital filters, which help in reducing the size of the device. The software has been created using the C# programming language, with Windows Presentation Foundation (WPF), aiding the collection of the ECG signals from the receiving circuit via the computer port. An ECG Multiparameter Simulator has been used to calibrate the ECG device. Finally, a plan has been developed to connect the arrangement to health systems according to HL7 FHIR (Health Level Seven Fast Healthcare Interoperability Resources) through Representational State Transfer Application Programming Interface (Rest API). The ECG device, completed at the cost of U$169 excluding labor, allows for the signal of 12 leads of ECG signal to be obtained from 10 electrodes mounted on the body. The processed ECG data was written to a JSON file with a maximum recording time of up to three days, managed by a Structured Query Language Server (SQL) Server database. The software retrieves patient data from electrical medical records in accordance with HL7 FHIR standards. A compact and easy-to-use ECG device was successfully designed to record ECG signals. An in-house developed software was also completed to display and store the ECG signals. [ABSTRACT FROM AUTHOR]

Tomato leaves can have different diseases which can affect harvest performance. Therefore, accurate classification for the early detection of disease for treatment is very important. This article proposes one classification model, in which 16,010 tomato leaf images obtained from the Plant Village database are segmented before being used to train a deep convolutional neural network (DCNN). This means that this classification model will reduce training time compared with that of the model without segmenting the images. In particular, we applied a VGG-19 model with transfer learning for re-training in later layers. In addition, the parameters such as epoch and learning rate were chosen to be suitable for increasing classification performance. One highlight point is that the leaf images were segmented for extracting the original regions and removing the backgrounds to be black using a hue, saturation, and value (HSV) color space. The segmentation of the leaf images is to synchronize the black background of all leaf images. It is obvious that this segmentation saves time for training the DCNN and also increases the classification performance. This approach improves the model accuracy to 99.72% and decreases the training time of the 16,010 tomato leaf images. The results illustrate that the model is effective and can be developed for more complex image datasets. [ABSTRACT FROM AUTHOR]