A Comparative Study between Scanning Devices for 3D Printing of Personalized Ostomy Patches

This papers presents a comparative study of three different 3D scanning modalities to acquire 3D meshes of stoma barrier rings from ostomized patients. Computerized Tomography and Structured light scanning methods were the digitization technologies studied in this research. Among the Structured Light systems, the Go!Scan 20 and the Structure Sensor were chosen as the handheld 3D scanners. Nineteen ostomized patients took part in this study, starting from the 3D scans acquisition until the printed ostomy patches validation. 3D mesh processing, mesh generation and 3D mesh comparison was carried out using commercial softwares. The results of the presented study show that the Structure Sensor, which is the low cost structured light 3D sensor, has a great potential for such applications. This study also discusses the benefits and reliability of low-cost structured light systems.

Automatic Detection of Fiducial Landmarks Toward the Development of an Application for Digitizing the Locations of EEG Electrodes: Occipital Structure Sensor-Based Work

The reconstruction of electrophysiological sources within the brain is sensitive to the constructed head model, which depends on the positioning accuracy of anatomical landmarks known as fiducials. In this work, we propose an algorithm for the automatic detection of fiducial landmarks of EEG electrodes on the 3D human head model. Our proposal combines a dimensional reduction approach with a perspective projection from 3D to 2D object space; the eye and ear automatic detection in a 2D face image by two cascades of classifiers and geometric transformations to obtain 3D spatial coordinates of the landmarks and to generate the head coordinate system, This is accomplished by considering the characteristics of the scanner information. Capturing the 3D model of the head is done with Occipital Inc. ST01 structure sensor and the implementation of our algorithm was carried out on MATLAB R2018b using the Computer Vision Toolbox and the FieldTrip Toolbox. The experimental results were aimed at recursively exploring the efficacy of the facial feature detectors as a function of the projection angle; they show that robust results are obtained in terms of false acceptance rate. Our proposal is an initial step of an approach for the automatic digitization of electrode locations. The experimental results demonstrate that the proposed method detects anatomical facial landmarks automatically, accurately, and rapidly.

Comparison of geometrical accuracy of active devices for 3D orthopaedic reconstructions

The use of 3D digitizing tools is becoming the base for subject-specific products, such as the orthopaedic production process of orthoses and prostheses. This paper aims at comparing the metrological behaviour of low-cost devices (Kinect 1 and 2 by Microsoft, Structure Sensor by Occipital) and high-resolution active sensors (O&P Scan by Rodin4D, NextEngine Ultra HD, Konica Minolta Vivid 9i, GOM ATOS II 400 and Artec Leo) for the survey of human body parts. A calibrated flat plane and a test-field composed of eight calibrated spheres of different radii and placed at different heights were used to evaluate the standard quality parameters (flatness, probing errors in form and size and the standard deviation) for each device as recommended by the VDI/VDE 2634 guidelines. Subsequently, three different parts of a mannequin were surveyed as samples of human body parts. The results demonstrated the higher accuracy of fixed devices with respect to handheld ones, among which Artec Leo and Structure Sensor provided a satisfying level of accuracy for the orthopaedic application. Moreover, the handheld devices enabled performing a fast reconstruction of the mannequin parts in about 20 s, which is acceptable for a person that has to remain as still as possible. For this reason, the Structure Sensor was further tested with five motion approaches which identified that smooth motion provides the lowest deviation and higher reliability. The work demonstrated the appropriateness of handheld devices for the orthopaedic application requirements in terms of speed, accuracy and costs.

A General Grid-Less Design Method for Location and Pressure Sensors with High Precision

Bionic electronic skin can accurately sense and locate surface pressure, which is widely demanded in many fields. Traditional electronic skin design usually relies on grid-architecture sensor arrays, requiring complex grid and interconnection arrangements as well as high cost. Grid-less planar sensors can solve the problem by using electrodes only at the edges, but they usually require the use of mapping software such as electrical impedance tomography to achieve high precision. In this work, a design method of high-precision grid-less planar pressure sensors based on the back-propagation (BP) neural network is proposed. The measurement precision of this method is demonstrated to be over two orders of magnitude higher than that of a grid-structure sensor array with the same electrode distribution density. Moreover, this method can be used for irregularly-shaped and non-uniform sensors, which further reduces the manufacturing difficulty and increases the application flexibility.

Integrating 3D Model Representation for an Accurate Non-Invasive Assessment of Pressure Injuries with Deep Learning

Pressure injuries represent a major concern in many nations. These wounds result from prolonged pressure on the skin, which mainly occur among elderly and disabled patients. If retrieving quantitative information using invasive methods is the most used method, it causes significant pain and discomfort to the patients and may also increase the risk of infections. Hence, developing non-intrusive methods for the assessment of pressure injuries would represent a highly useful tool for caregivers and a relief for patients. Traditional methods rely on findings retrieved solely from 2D images. Thus, bypassing the 3D information deriving from the deep and irregular shape of this type of wounds leads to biased measurements. In this paper, we propose an end-to-end system which uses a single 2D image and a 3D mesh of the pressure injury, acquired using the Structure Sensor, and outputs all the necessary findings such as: external segmentation of the wound as well as its real-world measurements (depth, area, volume, major axis and minor axis). More specifically, a first block composed of a Mask RCNN model uses the 2D image to output the segmentation of the external boundaries of the wound. Then, a second block matches the 2D and 3D views to segment the wound in the 3D mesh using the segmentation output and generates the aforementioned real-world measurements. Experimental results showed that the proposed framework can not only output refined segmentation with 87% precision, but also retrieves reliable measurements, which can be used for medical assessment and healing evaluation of pressure injuries.

A Flow-Through Microfluidic Relative Permittivity Sensor

In this paper, we present the design, simulation, fabrication and characterization of a microfluidic relative permittivity sensor in which the fluid flows through an interdigitated electrode structure. Sensor fabrication is based on an silicon on insulator (SOI) wafer where the fluidic inlet and outlet are etched through the handle layer and the interdigitated electrodes are made in the device layer. An impedance analyzer was used to measure the impedance between the interdigitated electrodes for various non-conducting fluids with a relative permittivity ranging from 1 to 41. The sensor shows good linearity over this range of relative permittivity and can be integrated with other microfluidic sensors in a multiparameter chip.

A Novel Net Like Structure Sensor and Gas Detection Performance in Tunnel Construction

In this work, novel multiporous net-like WO3 · H2O nanostructures were successfully obtained by changing the cations in a hydrothermal reaction system. The gas-sensing performance was measured by a static system. The experimental results showed that this net-like WO3 · H2O nanostructure-based sensor exhibited an enhanced response to H2S compared with that of normal WO3 · H2O nanorods. The causes of this phenomenon are discussed in detail from the two perspectives of electron transfer and gas-sensing reaction processes. We believe that this work also indicate that the net like structure is hopeful to become the ideal candidate as high performance H2S gas sensors.

Woven Structure for Flexible Capacitive Pressure Sensors

Flexible and stretchable capacitive pressure sensors have been developed in recent years due to their potential applications in health monitoring, robot skins, body activity measurements and so on. In order to enhance sensor sensitivity, researchers have changed structure of the dielectric of parallel plate capacitive sensor . Here we enhance the sensor sensitivities by changing electrode composition and explore the use of a woven electrode structure sensor with silver coated nylon yarn and EcoflexTM. The woven structure enhanced sensitivity 2.3 times relative to a simple cross-grid geometry (sensitivity was 0.003 kPa-1). Furthermore, it is also observed that the sensor with the woven electrode also had better repeatability and showed less creep than a device using carbon black electrodes. The woven structure of the electrodes enabled the device to be compliant, despite the presence of the stiff nylon fibres – thereby enabling good sensitivity without the creep seen in softer electrodes.