Facile Fabrication of 3D Porous Sponges Coated with Synergistic Carbon Black/Multiwalled Carbon Nanotubes for Tactile Sensing Applications.

Recently, flexible tactile sensors based on three-dimensional (3D) porous conductive composites, endowed with high sensitivity, a wide sensing range, fast response, and the capability to detect low pressures, have aroused considerable attention. These sensors have been employed in different practical domain areas such as artificial skin, healthcare systems, and human-machine interaction. In this study, a facile, cost-efficient method is proposed for fabricating a highly sensitive piezoresistive tactile sensor based on a 3D porous dielectric layer. The proposed sensor is designed with a simple dip-coating homogeneous synergetic conductive network of carbon black (CB) and multi-walled carbon nanotube (MWCNTs) composite on polydimethysiloxane (PDMS) sponge skeletons. The unique combination of a 3D porous structure, with hybrid conductive networks of CB/MWCNTs displayed a superior elasticity, with outstanding electrical characterization under external compression. The piezoresistive tactile sensor exhibited a high sensitivity of (15 kPa-1), with a rapid response time (100 ms), the capability of detecting both large and small compressive strains, as well as excellent mechanical deformability and stability over 1000 cycles. Benefiting from a long-term stability, fast response, and low-detection limit, the piezoresistive sensor was successfully utilized in monitoring human physiological signals, including finger heart rate, pulses, knee bending, respiration, and finger grabbing motions during the process of picking up an object. Furthermore, a comprehensive performance of the sensor was carried out, and the sensor's design fulfilled vital evaluation metrics, such as low-cost and simplicity in the fabrication process. Thus, 3D porous-based piezoresistive tactile sensors could rapidly promote the development of high-performance flexible sensors, and make them very attractive for an enormous range of potential applications in healthcare devices, wearable electronics, and intelligent robotic systems.

Motion and Trajectory Constraints Control Modeling for Flexible Surgical Robotic Systems.

Success of the da Vinci surgical robot in the last decade has motivated the development of flexible access robots to assist clinical experts during single-port interventions of core intrabody organs. Prototypes of flexible robots have been proposed to enhance surgical tasks, such as suturing, tumor resection, and radiosurgery in human abdominal areas; nonetheless, precise constraint control models are still needed for flexible pathway navigation. In this paper, the design of a flexible snake-like robot is presented, along with the constraints model that was proposed for kinematics and dynamics control, motion trajectory planning, and obstacle avoidance during motion. Simulation of the robot and implementation of the proposed control models were done in Matlab. Several points on different circular paths were used for evaluation, and the results obtained show the model had a mean kinematic error of 0.37 ± 0.36 mm with very fast kinematics and dynamics resolution times. Furthermore, the robot's movement was geometrically and parametrically continuous for three different trajectory cases on a circular pathway. In addition, procedures for dynamic constraint and obstacle collision detection were also proposed and validated. In the latter, a collision-avoidance scheme was kept optimal by keeping a safe distance between the robot's links and obstacles in the workspace. Analyses of the results showed the control system was optimal in determining the necessary joint angles to reach a given target point, and motion profiles with a smooth trajectory was guaranteed, while collision with obstacles were detected a priori and avoided in close to real-time. Furthermore, the complexity and computational effort of the algorithmic models were negligibly small. Thus, the model can be used to enhance the real-time control of flexible robotic systems.

Learning-based Parameter Estimation for Hysteresis Modeling in Robotic Catheterization.

In the last half decade, nearly 31% of annual global deaths are linked to cardiovascular diseases. Thus, robotic catheterizations are recently proposed for interventions of conditions such as aneurism or atherosclerosis formed along vascular paths leading to the heart. However, existence of mild to strong hysteresis while navigating unactuated catheters with the current robotic systems inhibits autonomous control for vascular surgery. Thus, immersion of surgeons remains high with most of their time spent on steering the catheter in-and- out of the vessels. In this study, an autoregressive nonlinear neural network model is adapted for parameterization of vital causal factors of hysteresis during robotic catheterization. Crucial for autonomous control, hysteretic behaviors of endovascular tool are modeled while suitable values are estimated and analyzed for five contributory factors. The network model is validated with hysteresis data we obtained from a two degree-of-freedom robotic system and an unactuated catheter. Result validation shows accurate description of the hysteresis profile recorded during catheterization trials with a vascular phantom model.

Deep Learning Intervention for Health Care Challenges: Some Biomedical Domain Considerations.

The use of deep learning (DL) for the analysis and diagnosis of biomedical and health care problems has received unprecedented attention in the last decade. The technique has recorded a number of achievements for unearthing meaningful features and accomplishing tasks that were hitherto difficult to solve by other methods and human experts. Currently, biological and medical devices, treatment, and applications are capable of generating large volumes of data in the form of images, sounds, text, graphs, and signals creating the concept of big data. The innovation of DL is a developing trend in the wake of big data for data representation and analysis. DL is a type of machine learning algorithm that has deeper (or more) hidden layers of similar function cascaded into the network and has the capability to make meaning from medical big data. Current transformation drivers to achieve personalized health care delivery will be possible with the use of mobile health (mHealth). DL can provide the analysis for the deluge of data generated from mHealth apps. This paper reviews the fundamentals of DL methods and presents a general view of the trends in DL by capturing literature from PubMed and the Institute of Electrical and Electronics Engineers database publications that implement different variants of DL. We highlight the implementation of DL in health care, which we categorize into biological system, electronic health record, medical image, and physiological signals. In addition, we discuss some inherent challenges of DL affecting biomedical and health domain, as well as prospective research directions that focus on improving health management by promoting the application of physiological signals and modern internet technology.