Optically Sensorized Tendons for Articulate Robotic Needles

This study proposes an optically sensorized tendon composed of a 195 µm diameter, high strength, polarization maintaining (PM) fiber Bragg gratings (FBG) optical fiber which resolves the cross-sensitivity issue of conventional FBGs. The bare fiber tendon is locally reinforced with a 250 µm diameter Kevlar bundle enhancing the level of force transmission and enabling high curvature tendon routing. The performance of the sensorized tendons is explored in terms of strength (higher than 13N for the bare PM-FBG fiber tendon, up to 40N for the Kevlar-reinforced tendon under tensile loading), strain sensitivity (0.127 percent strain per newton for the bare PM-FBG fiber tendon, 0.04 percent strain per newton for the Kevlar-reinforced tendon), temperature stability, and friction-independent sensing behavior. Subsequently, the tendon is instrumented within an 18 Ga articulate NiTi cannula and evaluated under static and dynamic loading conditions, and within phantoms of varying stiffness for tissue-stiffness estimation. The results from this series of experiments serve to validate the effectiveness of the proposed tendon as a bi-modal sensing and actuation component for robot-assisted minimally invasive surgical instruments

Brain Activity-Based Metrics for Assessing Learning States in VR under Stress among Firefighters: An Explorative Machine Learning Approach in Neuroergonomics

The nature of firefighters’ duties requires them to work for long periods under unfavorable conditions. To perform their jobs effectively, they are required to endure long hours of extensive, stressful training. Creating such training environments is very expensive and it is difficult to guarantee trainees’ safety. In this study, firefighters are trained in a virtual environment that includes virtual perturbations such as fires, alarms, and smoke. The objective of this paper is to use machine learning methods to discern encoding and retrieval states in firefighters during a visuospatial episodic memory task and explore which regions of the brain provide suitable signals to solve this classification problem. Our results show that the Random Forest algorithm could be used to distinguish between information encoding and retrieval using features extracted from fNIRS data. Our algorithm achieved an F-1 score of 0.844 and an accuracy of 79.10% if the training and testing data are obtained at similar environmental conditions. However, the algorithm’s performance dropped to an F-1 score of 0.723 and accuracy of 60.61% when evaluated on data collected under different environmental conditions than the training data. We also found that if the training and evaluation data were recorded under the same environmental conditions, the RPM, LDLPFC, RDLPFC were the most relevant brain regions under non-stressful, stressful, and a mix of stressful and non-stressful conditions, respectively

A Review on Mechanical and Thermal Properties of Aluminum Metal Matrix Composites

The Aluminum Metal Matrix Composites (AMMCs) have been becoming suitable materials for many devices in the application of various fields like heavy equipment’s industry, automobile, aeronautics and etc. because of its excellent physical and structural characteristics. The research on AMMC dealt the effect of reinforcement such as fly-ash, SiC, Al2O3, Graphite, B4C, Cubic Boron Nitride (CBN) on aluminium in different percentages. Every reinforcement has its own characteristics that enhance the base aluminium characteristics when added. By adding these types of reinforcement to metal base led to enhance the properties like wear resistance, stiffness, creep, tensile strength, fatigue, toughness, thermal conductivity, hardness in comparison with traditional approach on materials engineering. This review paper was aimed to give the detailed information about the impact of various reinforcements incorporated in matrix by illustrating its benefits and drawbacks. This extensive survey on AMMC could be useful to develop farther