Deep neural network-based robotic visual servoing for satellite target tracking.

In response to the costly and error-prone manual satellite tracking on the International Space Station (ISS), this paper presents a deep neural network (DNN)-based robotic visual servoing solution to the automated tracking operation. This innovative approach directly addresses the critical issue of motion decoupling, which poses a significant challenge in current image moment-based visual servoing. The proposed method uses DNNs to estimate the manipulator's pose, resulting in a significant reduction of coupling effects, which enhances control performance and increases tracking precision.

A neuromechanical model characterizing the motor planning and posture control in the voluntary lean in Parkinson's disease.

Parkinson's disease targets patients' cognitive and motor abilities, including postural control. Many studies have been carried out to introduce mathematical models for a better understanding of postural control in such patients and the relation between the model parameters and the clinical assessments. So far, these studies have addressed this connection merely in static tests, such as quiet stance.

Overground Walking With a Transparent Exoskeleton Shows Changes in Spatiotemporal Gait Parameters.

Lower-limb gait training (GT) exoskeletons have been successfully used in rehabilitation programs to overcome the burden of locomotor impairment. However, providing suitable net interaction torques to assist patient movements is still a challenge. Previous transparent operation approaches have been tested in treadmill-based GT exoskeletons to improve user-robot interaction.

Upper-Limb Rehabilitation of Patients with Neuromotor Deficits Using Impedance-Based Control of a 6-DOF Robot.

This paper presents a study on the use of impedance-based control of a 6-degree-of-freedom robot for upper-limb rehabilitation of patients with neuromotor deficits. The control strategy is based on impedance and does not require external force sensors at the end-effector for implementation. The experimental setup involved using the control algorithm to move the robot to a desired position, follow a desired trajectory while being moved out of the trajectory by the user, and reproduce three different rehabilitation exercises (passive, isometric, and ADL).

A Systematic Study on Electromyography-Based Hand Gesture Recognition for Assistive Robots Using Deep Learning and Machine Learning Models.

Upper limb amputation severely affects the quality of life and the activities of daily living of a person. In the last decade, many robotic hand prostheses have been developed which are controlled by using various sensing technologies such as artificial vision and tactile and surface electromyography (sEMG). If controlled properly, these prostheses can significantly improve the daily life of hand amputees by providing them with more autonomy in physical activities.

Identification of human balance control responses to visual inputs using virtual reality.

Human upright balance is maintained through feedback mechanisms that use a variety of sensory modalities. Vision senses information about the position and velocity of the visual surround motion to improve balance by reducing the sway evoked by external disturbances. This study characterized the effects of visual information on human anterior-posterior body sway in upright stance by presenting perturbations through a virtual reality system.

Contributions of Vision in Human Postural Control: A Virtual Reality-based Study.

During human standing, it has been previously observed that information about the position and frequency of visual surround motion improves balance by reducing sway responses to external disturbances. However, experimental limitations only allowed for independent investigation of such parameters while being incapable of providing a fully immersive experience of a real environment. The aim of this study is to investigate the effect of visual information on dynamic body sway in the human upright stance by presenting perturbations through a virtual reality (VR) system.

Experimental Methods to Study Human Postural Control.

Many components of the nervous and musculoskeletal systems act in concert to achieve the stable, upright human posture. Controlled experiments accompanied by appropriate mathematical methods are needed to understand the role of the different sub-systems involved in human postural control. This article describes a protocol for performing perturbed standing experiments, acquiring experimental data, and carrying out the subsequent mathematical analysis, with the aim of understanding the role of musculoskeletal system and central control in human upright posture.

Molecular prevalence of JC virus in Tehran, Iran.

Aim: Since data about prevalence of JC virus in Iranian population is scarce, this study was designed to evaluate the prevalence of JC virus in healthy individuals who had attended Fajr hospital and Farjam clinical laboratory in Tehran, Iran.

Background: JC virus is the causative agent of progressive multifocal encephalopathy (PML) in individuals with suppressed immune system. There are some evidences that this virus is responsible for some forms of cancers for example colorectal and gastric cancers in humans.