Postural Adjustments in HTLV-1 Infected Patients during a Self-Initiated Perturbation.

BACKGROUND: Human T-cell lymphotropic virus type 1 (HTLV-1) infection can be associated with tropical spastic paraparesis (TSP/HAM), which causes neurological myelopathy and sensory and muscle tone alterations, leading to gait and balance impairments. Once trunk perturbation is predicted, the motor control system uses anticipatory and compensatory mechanisms to maintain balance by recruiting postural muscles and displacement of the body's center of mass. METHODS: Twenty-six participants (control or infected) had lower limb muscle onset and center of pressure (COP) displacements assessed prior to perturbation and throughout the entire movement. RESULTS: Semitendinosus (ST) showed delayed onset in the infected group compared to the control group. The percentage of trials with detectable anticipatory postural adjustment was also lower in infected groups in the tibialis anterior and ST. In addition, COP displacement in the infected group was delayed, had a smaller amplitude, and took longer to reach the maximum displacement. CONCLUSIONS: HTLV-1 infected patients have less efficient anticipatory adjustments and greater difficulty recovering their postural control during the compensatory phase. Clinical assessment of this population should consider postural stability during rehabilitation programs.

Deep Q-network for social robotics using emotional social signals.

Social robotics represents a branch of human-robot interaction dedicated to developing systems to control the robots to operate in unstructured environments with the presence of human beings. Social robots must interact with human beings by understanding social signals and responding appropriately to them. Most social robots are still pre-programmed, not having great ability to learn and respond with actions adequate during an interaction with humans. Recently more elaborate methods use body movements, gaze direction, and body language. However, these methods generally neglect vital signs present during an interaction, such as the human emotional state. In this article, we address the problem of developing a system to turn a robot able to decide, autonomously, what behaviors to emit in the function of the human emotional state. From one side, the use of Reinforcement Learning (RL) represents a way for social robots to learn advanced models of social cognition, following a self-learning paradigm, using characteristics automatically extracted from high-dimensional sensory information. On the other side, Deep Learning (DL) models can help the robots to capture information from the environment, abstracting complex patterns from the visual information. The combination of these two techniques is known as Deep Reinforcement Learning (DRL). The purpose of this work is the development of a DRL system to promote a natural and socially acceptable interaction among humans and robots. For this, we propose an architecture, Social Robotics Deep Q-Network (SocialDQN), for teaching social robots to behave and interact appropriately with humans based on social signals, especially on human emotional states. This constitutes a relevant contribution for the area since the social signals must not only be recognized by the robot but help him to take action appropriated according to the situation presented. Characteristics extracted from people's faces are considered for extracting the human emotional state aiming to improve the robot perception. The development and validation of the system are carried out with the support of SimDRLSR simulator. Results obtained through several tests demonstrate that the system learned satisfactorily to maximize the rewards, and consequently, the robot behaves in a socially acceptable way.