Longitudinal In-Bed Pressure Signals Decomposition and Gradients Analysis for Pressure Injury Monitoring.

Pressure injury (PI) is a major problem for patients that are bound to a wheelchair or bed, such as seniors or people with spinal cord injuries. This condition can be life threatening in its later stages. It can be very costly to the healthcare system as well. Fortunately with proper monitoring and assessment, PI development can be prevented. The major factor that causes PI is prolonged interface pressure between the body and the support surface. A possible solution to reduce the chance of developing PI is changing the patient's in-bed pose at appropriate times. Monitoring in-bed pressure can help healthcare providers to locate high-pressure areas, and remove or minimize pressure on those regions. The current clinical method of interface pressure monitoring is limited by periodic snapshot assessments, without longitudinal measurements and analysis. In this paper we propose a pressure signal analysis pipeline to automatically eliminate external artefacts from pressure data, estimate a person's pose, and locate and track high-risk regions over time so that necessary attention can be provided.

Marker-Less 3d Object Recognition and 6d Pose Estimation for Homogeneous Textureless Objects: An RGB-D Approach.

The task of recognising an object and estimating its 6d pose in a scene has received considerable attention in recent years. The accessibility and low-cost of consumer RGB-D cameras, make object recognition and pose estimation feasible even for small industrial businesses. An example is the industrial assembly line, where a robotic arm should pick a small, textureless and mostly homogeneous object and place it in a designated location. Despite all the recent advancements of object recognition and pose estimation techniques in natural scenes, the problem remains challenging for industrial parts. In this paper, we present a framework to simultaneously recognise the object's class and estimate its 6d pose from RGB-D data. The proposed model adapts a global approach, where an object and the Region of Interest (ROI) are first recognised from RGB images. The object's pose is then estimated from the corresponding depth information. We train various classifiers based on extracted Histogram of Oriented Gradient (HOG) features to detect and recognize the objects. We then perform template matching on the point cloud based on surface normal and Fast Point Feature Histograms (FPFH) to estimate the pose of the object. Experimental results show that our system is quite efficient, accurate and robust to illumination and background changes, even for the challenging objects of Tless dataset.

Corrigendum to "A high-performance glucose biosensor using covalently immobilised glucose oxidase on a poly(2,6-diaminopyridine)/carbon nanotube electrode".

A highly-sensitive glucose biosensor amenable to ultraminiaturisation was fabricated by immobilization of glucose oxidase (GOX), onto a poly(2,6-diaminopyridine)/multi-walled carbon nanotube/glassy carbon electrode (poly(2,6-DP)/MWCNT/GCE). Cyclic voltammetry was used for both the electrochemical synthesis of poly-(2,6-DP) on the surface of a MWCNT-modified GC electrode, and characterization of the polymers deposited on the GC electrode. The synergistic effect of the high active surface area of both the conducting polymer, i.e., poly-(2,6-DP) and MWCNT gave rise to a remarkable improvement in the electrocatalytic properties of the biosensor. The transfer coefficient (α), heterogeneous electron transfer rate constant and Michaelis-Menten constant were calculated to be 0.6, 4s-1 and 0.22mM at pH 7.4, respectively. The GOx/poly(2,6-DP)/MWCNT/GC bioelectrode exhibited two linear responses to glucose in the concentration ranging from 0.42µM to 8.0 mM with a correlation coefficient of 0.95, sensitivity of 52.0µAmM-1cm-2, repeatability of 1.6% and long-term stability, which could make it a promising bioelectrode for precise detection of glucose in the biological samples.

Determining team cognition from delay analysis using cross recurrence plot.

Team cognition is an important factor in evaluating and determining team performance. Forming a team with good shared cognition is even more crucial for laparoscopic surgery applications. In this study, we analyzed the eye tracking data of two surgeons during a laparoscopic simulation operation, then performed Cross Recurrence Analysis (CRA) on the recorded data to study the delay behaviour for good performer and poor performer teams. Dual eye tracking data for twenty two dyad teams were recorded during a laparoscopic task and then the teams were divided into good performer and poor performer teams based on the task times. Eventually we studied the delay between two team members for good and poor performer teams. The results indicated that the good performer teams show a smaller delay comparing to poor performer teams. This study is compatible with gaze overlap analysis between team members and therefore it is a good evidence of shared cognition between team members.

A high-performance glucose biosensor using covalently immobilised glucose oxidase on a poly(2,6-diaminopyridine)/carbon nanotube electrode.

A highly-sensitive glucose biosensor amenable to ultra-miniaturisation was fabricated by immobilisation of glucose oxidase (GOx), onto a poly(2,6-diaminopyridine)/multi-walled carbon nanotube/glassy carbon electrode (poly(2,6-DP)/MWNT/GCE). Cyclic voltammetry was used for both the electrochemical synthesis of poly-(2,6-DP) on the surface of a MWNT-modified GC electrode, and characterisation of the polymers deposited on the GC electrode. The synergistic effect of the high active surface area of both the conducting polymer, i.e., poly-(2,6-DP) and MWNT gave rise to a remarkable improvement in the electrocatalytic properties of the biosensor. The transfer coefficient (α), heterogeneous electron transfer rate constant and Michaelis-Menten constant were calculated to be 0.6, 4 s(-1) and 0.20 mM at pH 7.4, respectively. The GOx/poly(2,6-DP)/MWNT/GC bioelectrode exhibited two linear responses to glucose in the concentration ranging from 0.42 µM to 8.0 mM with a correlation coefficient of 0.95, sensitivity of 52.0 µA mM(-1) cm(-2), repeatability of 1.6% and long-term stability, which could make it a promising bioelectrode for precise detection of glucose in the biological samples.