Self-organizing neural network for reproducing human postural mode alternation through deep reinforcement learning.

A self-organized phenomenon in postural coordination is essential for understanding the auto-switching mechanism of in-phase and anti-phase postural coordination modes during standing and related supra-postural activities. Previously, a model-based approach was proposed to reproduce such self-organized phenomenon. However, if we set this problem including the process of how we establish the internal predictive model in our central nervous system, the learning process is critical to be considered for establishing a neural network for managing adaptive postural control. Particularly when body characteristics may change due to growth or aging or are initially unknown for infants, a learning capability can improve the hyper-adaptivity of human motor control for maintaining postural stability and saving energy in daily living. This study attempted to generate a self-organizing neural network that can adaptively coordinate the postural mode without assuming a prior body model regarding body dynamics and kinematics. Postural coordination modes are reproduced in head-target tracking tasks through a deep reinforcement learning algorithm. The transitions between the postural coordination types, i.e. in-phase and anti-phase coordination modes, could be reproduced by changing the task condition of the head tracking target, by changing the frequencies of the moving target. These modes are considered emergent phenomena existing in human head tracking tasks. Various evaluation indices, such as correlation, and relative phase of hip and ankle joint, are analyzed to verify the self-organizing neural network performance to produce the postural coordination transition between the in-phase and anti-phase modes. In addition, after learning, the neural network can also adapt to continuous task condition changes and even to unlearned body mass conditions keeping consistent in-phase and anti-phase mode alternation.

A New Home-Based Upper- and Lower-Limb Telerehabilitation Platform with Experimental Validation.

The world is witnessing interesting challenges in several fields, including medicine. Solutions to many of these challenges are being developed in the field of artificial intelligence. As a result, artificial intelligence techniques can be used in telerehabilitation to facilitate the work of doctors and to find methods that can be used to better treat methods that can be used to better treat patients. Motion rehabilitation is an essential procedure for elderly people and patients undergoing physiotherapy after physical procedures such as surgery for the anterior cruciate ligament (ACL), a frozen shoulder. To regain normal motion, the patient must participate in rehabilitation sessions. Furthermore, telerehabilitation has become a significant trend in research studies because of the COVID-19 pandemic, which is continuing to affect the world through the delta and the omicron variants, and other epidemics. In addition, because of other special issues like the vastness of the desert area in Algeria and the lack of facilities, it is ideal to avoid requiring patients to travel for all of their rehabilitation sessions; patients should be able to perform their rehabilitation exercises at home. Thus, telerehabilitation could lead to promising developments in this field. Therefore, our project's goal is to develop a website for telerehabilitation that can be used to facilitate rehabilitation from a distance. We also want to track the progress of patients' range of motion (ROM) in real time using artificial intelligence techniques, by controlling the angles of the motion of a limbs about a joint.

Classification of Human Balance Recovery Strategies Through Kinematic Motor Synergy Analysis.

A key problem in human balance recovery lies in understanding the mechanism of balance behavior with redundant bio-mechanical motors. Motor synergy has been known as an efficient tool to analyze characteristics of motion behavior and reconstruct control command. In this paper, motor synergy analysis for different control strategies is proposed to analyze different balance motion coordination for various levels of pushing force, and understand the coordination of human multiple joints regarding balance recovery. The spatial synergy of specific joint angles for different pushing force levels exerted on the subject's back is computed with the principal component analysis (PCA) to evaluate the adaptive balance motion response patterns and illustrate the improvement of balance robustness through the switch of joint coordination. Therefore, the switch of postural coordination over multiple joints in balance recovery movements was analyzed to better understand the mechanism of balance strategy generation in this study.

Reproducing Human Arm Strategy and Its Contribution to Balance Recovery Through Model Predictive Control.

The study of human balance recovery strategies is important for human balance rehabilitation and humanoid robot balance control. To date, many efforts have been made to improve balance during quiet standing and walking motions. Arm usage (arm strategy) has been proposed to control the balance during walking motion in the literature. However, limited research exists on the contributions of the arm strategy for balance recovery during quiet standing along with ankle and hip strategy. Therefore, in this study, we built a simplified model with arms and proposed a controller based on nonlinear model predictive control to achieve human-like balance control. Three arm states of the model, namely, active arms, passive arms, and fixed arms, were considered to discuss the contributions of arm usage to human balance recovery during quiet standing. Furthermore, various indexes such as root mean square deviation of joint angles and recovery energy consumption were verified to reveal the mechanism behind arm strategy employment. In this study, we demonstrate to computationally reproduce human-like balance recovery with and without arm rotation during quiet standing while applying different magnitudes of perturbing forces on the upper body. In addition, the conducted human balance experiments are presented as supplementary information in this paper to demonstrate the concept on a typical example of arm strategy.

Some control-related issues in mini-robotics for endoluminal surgery.

This paper introduces some issues related to the development of robotics for endoluminal surgery from control point of view. Endoluminal surgery are incisionless procedures performed through natural orifices within the natural pathways. New devices are then required to achieve these new surgical procedures. Besides the development of new devices, control issues arise in both technological and theoretical aspects. The paper presents some of them and we propose a teleoperation architecture that has already been tested for needle insertion that could be used for teleoperated endoluminal surgery especially for instance for biopsies or anastomoses.