Nowadays, the study of the gait by analyzing the distribution of plantar pressure is a well-established technique. The use of intelligent insoles allows real-time monitoring of the user. Thus, collecting and analyzing information is a more accurate process than consultations in so-called gait laboratories. Most of the previous published studies consider the composition and operation of these insoles based on resistive sensors. However, the use of capacitive sensors could provide better results, in terms of linear behavior under the pressure exerted. This behavior depends on the properties of the dielectric used. In this work, the design and implementation of an intelligent plantar insole composed of capacitive sensors is proposed. The dielectric used is a polydimethylsiloxane (PDMS)-based composition. The sensorized plantar insole developed achieves its purpose as a tool for collecting pressure in different areas of the sole of the foot. The fundamentals and details of the composition, manufacture, and implementation of the insole and the system used to collect data, as well as the data samples, are shown. Finally, a comparison of the behavior of both insoles, resistive and capacitive sensor-equipped, is made. The prototype presented lays the foundation for the development of a tool to support the diagnosis of gait abnormalities.

In this work we introduce NeoCam , an open source hardware-software platform for video-based monitoring of preterms infants in Neonatal Intensive Care Units (NICUs). NeoCam includes an edge computing device that performs video acquisition and processing in real-time. Compared to other proposed solutions, it has the advantage of handling data more efficiently by performing most of the processing on the device, including proper anonymisation for better compliance with privacy regulations. In addition, it allows to perform various video analysis tasks of clinical interest in parallel at speeds of between 20 and 30 frames-per-second. We introduce algorithms to measure without contact the breathing rate, motor activity, body pose and emotional status of the infants. For breathing rate, our system shows good agreement with existing methods provided there is sufficient light and proper imaging conditions. Models for motor activity and stress detection are new to the best of our knowledge. NeoCam has been tested on preterms in the NICU of the University Hospital Puerta del Mar (Cádiz, Spain), and we report the lessons learned from this trial.

The use of both hands is a common practice in everyday life. The capacity to interact with the environment is largely dependent on the ability to use both hands. A thorough review of the current state of the art reveals that commercially available prosthetic hands designed for children are very different in functionality from those developed for adults, primarily due to prosthetic hands for adults featuring a greater number of actuated joints. Many times, patients stop using their prosthetic device because they feel that it does not fit well in terms of shape and size. With the idea of solving these problems, the design of HandBot-Kid has been developed with the anthropomorphic qualities of a child between the ages of eight and twelve in mind. Fitting the features of this age range, the robotic hand has a length of 16 cm, width of 7 cm, thickness of 3.6 cm, and weight of 328 g. The prosthesis is equipped with a total of fifteen degrees of freedom (DOF), with three DOFs allocated to each finger. The concept of design for manufacturing and assembly (DFMA) has been integrated into the development process, enabling the number of parts to be optimized in order to reduce the production time and cost. The utilization of 3D printing technology in conjunction with aluminum machining enabled the manufacturing process of the robotic hand prototype to be streamlined. The flexion–extension movement of each finger exhibits a trajectory that is highly similar to that of a real human finger. The four-bar mechanism integrated into the finger design achieves a mechanical advantage (MA) of 40.33% and a fingertip pressure force of 10.23 N. Finally, HandBot-Kid was subjected to a series of studies and taxonomical tests, including Cutkosky (16 points) and Kapandji (4 points) score tests, and the functional results were compared with some commercial solutions for children mentioned in the state of the art.

This paper presents an integrated preconcentration cartridge for [¹⁸F]Fluoride radiochemistry in a polydimethylsiloxane (PDMS) disposable chip. The fabrication process for the chip is designed to allow the construction of a final device for full synthesis of [¹⁸F]fludeoxyglucose, one of the standard radiopharmaceuticals used in positron-emission tomography imaging. The preconcentration is the first step in that synthesis and is composed of two phases: 1) retention; and 2) elution. The experimental results show that both the retention and elution rates are close to 100% for initial activities of 500 μCi, operating pressures of 500 mbar, volumes up to 2 mL and elution with 30 μL of potassium carbonate (K₂CO₃), and cryptand solution (Kryptofix K222). These results prove that PDMS can be used for radiopharmaceutical synthesis, and its low cost allows the fabrication of disposable chips, which is very convenient in these applications.

This work presents an automated welding inspection system based on a neural network trained through a series of 2D images of welding seams obtained in the same study. The object detection method follows a geometric deep learning model based on convolutional neural networks. Following an extensive review of available solutions, algorithms, and networks based on this convolutional strategy, it was determined that the You Only Look Once algorithm in its version 8 (YOLOv8) would be the most suitable for object detection due to its performance and features. Consequently, several models have been trained to enable the system to predict specific characteristics of weld beads. Firstly, the welding strategy used to manufacture the weld bead was predicted, distinguishing between two of them (Flux-Cored Arc Welding (FCAW)/Gas Metal Arc Welding (GMAW)), two of the predominant welding processes used in many industries, including shipbuilding, automotive, and aeronautics. In a subsequent experiment, the distinction between a well-manufactured weld bead and a defective one was predicted. In a final experiment, it was possible to predict whether a weld seam was well-manufactured or not, distinguishing between three possible welding defects. The study demonstrated high performance in three experiments, achieving top results in both binary classification (in the first two experiments) and multiclass classification (in the third experiment). The average prediction success rate exceeded 97% in all three experiments.

Silicon photomultipliers (SiPM) can be used to sense the position and movement of the radioactive reagents involved in the synthesis of radiopharmaceuticals, while providing information on the evolution through the different steps of this synthesis. An SiPM array can be placed next to a lab-on-chip substrate made of PDMS and is able to detect particles emitted by 18F, the radioisotope used in fluorine-based positron-emission tomography (PET) imaging. In this research, an array of 16 SiPM sensors is used to monitor the synthesis of 18F-based radiopharmaceuticals. Results prove that it is possible to measure the position of the radioactive sample with adequate spatial and temporal resolution.