Adaptive optimal multi-critic based neuro-fuzzy control of MIMO human musculoskeletal arm model

Utilising redundancy in musculoskeletal systems for adaptive stiffness and muscle failure compensation: a model-free inverse statics approach

Vertebrates possess a biomechanical structure with redundant muscles, enabling adaptability in uncertain and complex environments. Harnessing this inspiration, musculoskeletal systems offer advantages like variable stiffness and resilience to actuator failure and fatigue. Despite their potential, the complex structure presents modelling challenges that are difficult to explicitly formulate and control. This difficulty arises from the need for comprehensive knowledge of the musculoskeletal system, including details such as muscle arrangement, and fully accessible muscle and joint states. Whilst existing model-free methods do not need explicit formulations, they also underutilise the benefits of muscle redundancy. Consequently, they necessitate retraining in the event of muscle failure and require manual tuning of parameters to control joint stiffness limiting their applications under unknown payloads. Presented here is a model-free local inverse statics controller for musculoskeletal systems, employing a feedforward neural network trained on motor babbling data. Experiments with a musculoskeletal leg model showcase the controller's adaptability to complex structures, including mono and bi-articulate muscles. The controller can compensate for changes such as weight variations, muscle failures, and environmental interactions, retaining reasonable accuracy without the need for any additional retraining.

Neuromechanics-Based Neural Feedback Controller for Planar Arm Reaching Movements

Based on the principles of neuromechanics, human arm movements result from the dynamic interaction between the nervous, muscular, and skeletal systems. To develop an effective neural feedback controller for neuro-rehabilitation training, it is important to consider both the effects of muscles and skeletons. In this study, we designed a neuromechanics-based neural feedback controller for arm reaching movements. To achieve this, we first constructed a musculoskeletal arm model based on the actual biomechanical structure of the human arm. Subsequently, a hybrid neural feedback controller was developed that mimics the multifunctional areas of the human arm. The performance of this controller was then validated through numerical simulation experiments. The simulation results demonstrated a bell-shaped movement trajectory, consistent with the natural motion of human arm movements. Furthermore, the experiment testing the tracking ability of the controller revealed real-time errors within one millimeter, with the tensile force generated by the controller's muscles being stable and maintained at a low value, thereby avoiding the issue of muscle strain that can occur due to excessive excitation during the neurorehabilitation process.

A Survey of Brain-Inspired Intelligent Robots: Integration of Vision, Decision, Motion Control, and Musculoskeletal Systems

Current robotic studies are focused on the performance of specific tasks. However, such tasks cannot be generalized, and some special tasks, such as compliant and precise manipulation, fast and flexible response, and deep collaboration between humans and robots, cannot be realized. Brain-inspired intelligent robots imitate humans and animals, from inner mechanisms to external structures, through an integration of visual cognition, decision making, motion control, and musculoskeletal systems. This kind of robot is more likely to realize the functions that current robots cannot realize and become human friends. With the focus on the development of brain-inspired intelligent robots, this article reviews cutting-edge research in the areas of brain-inspired visual cognition, decision making, musculoskeletal robots, motion control, and their integration. It aims to provide greater insight into brain-inspired intelligent robots and attracts more attention to this field from the global research community.

Improving the Learning Rate, Accuracy, and Workspace of Reinforcement Learning Controllers for a Musculoskeletal Model of the Human Arm

Cervical spinal cord injuries frequently cause paralysis of all four limbs - a medical condition known as tetraplegia. Functional electrical stimulation (FES), when combined with an appropriate controller, can be used to restore motor function by electrically stimulating the neuromuscular system. Previous works have demonstrated that reinforcement learning can be used to successfully train FES controllers. Here, we demonstrate that transfer learning and curriculum learning can be used to improve the learning rates, accuracies, and workspaces of FES controllers that are trained using reinforcement learning.

Hindsight Experience Replay Improves Reinforcement Learning for Control of a MIMO Musculoskeletal Model of the Human Arm

High-level spinal cord injuries often result in paralysis of all four limbs, leading to decreased patient independence and quality of life. Coordinated functional electrical stimulation (FES) of paralyzed muscles can be used to restore some motor function in the upper extremity. To coordinate functional movements, FES controllers should be developed to exploit the complex characteristics of human movement and produce the intended movement kinematics and/or kinetics. Here, we demonstrate the ability of a controller trained using reinforcement learning to generate desired movements of a horizontal planar musculoskeletal model of the human arm with 2 degrees of freedom and 6 actuators. The controller is given information about the kinematics of the arm, but not the internal state of the actuators. In particular, we demonstrate that a technique called "hindsight experience replay" can improve controller performance while also decreasing controller training time.

A novel adaptive multi-critic based separated-states neuro-fuzzy controller: Architecture and application to chaos control

The design process of a novel adaptive critic based neuro-fuzzy controller is described. The concept is based on separating state variables into groups, assigning the groups to a multi-layer structure, and employing individual neuro-fuzzy controllers for each layer. The correlation procedure between layers is then defined and a proportional-derivative critic is generalized to be used with this structure. The controller's structure leads to a high reduction in the number of tunable parameters. For easier tuning, different gains are considered in the structure and an approach for the synthesis of the networks' initial parameters is given. The effectiveness of the controller is then verified by investigating two case studies: 1. Ball and beam regulation 2. Stabilizing the chaotic spinning disk's lateral vibrations. Simulations proved the feasibility and robustness of the proposed controller.

Fuzzy Logic Modeling of Factors Affecting Musculoskeletal Disorders in a Steel Factory in Iran: A Cross-Sectional Study

OBJECTIVES

The aim of this study was to model associated factors affecting musculoskeletal disorders (MSDs), using fuzzy logic in a steel factory in Iran.

METHODS

A cross-sectional study was conducted on steel industry workers. A 6-part questionnaire was used, consisting of demographic characteristics, occupational stress, work-family conflict, general health, occupational postures, and Nordic Musculoskeletal Questionnaire. Pearson correlation was used for statistical analysis.

RESULTS

The prevalence of MSDs for 270 participants in the studied factory was 94.8%. Job stress, work-family conflict, general health, and work posture had a statistically significant relationship with MSDs (P < .05). The fuzzy model demonstrated 23.8% predictability for the actual data of the study. The defuzzification data had significant correlation with real data of MSDs.

CONCLUSIONS

The results of this study provided a new perspective about associated factors affecting MSDs and demonstrate that fuzzy logic can be used as a possible tool for evaluating MSDs.

The Optimal Adaptive-Based Neurofuzzy Control of the 3-DOF Musculoskeletal System of Human Arm in a 2D Plane

Each individual performs different daily activities such as reaching and lifting with his hand that shows the important role of robots designed to estimate the position of the objects or the muscle forces. Understanding the body's musculoskeletal system's learning control mechanism can lead us to develop a robust control technique that can be applied to rehabilitation robotics. The musculoskeletal model of the human arm used in this study is a 3-link robot coupled with 6 muscles which a neurofuzzy controller of TSK type along multicritic agents is used for training and learning fuzzy rules. The adaptive critic agents based on reinforcement learning oversees the controller's parameters and avoids overtraining. The simulation results show that in both states of with/without optimization, the controller can well track the desired trajectory smoothly and with acceptable accuracy. The magnitude of forces in the optimized model is significantly lower, implying the controller's correct operation. Also, links take the same trajectory with a lower overall displacement than that of the nonoptimized mode, which is consistent with the hand's natural motion, seeking the most optimum trajectory.

A planar neuro-musculoskeletal arm model in post-stroke patients

Mathematical modeling of the neuro-musculoskeletal system in healthy subjects has been pursued extensively. In post-stroke patients, however, such models are very primitive. Besides improving our general understanding of how stroke affects the limb motions, they can be used to evaluate rehabilitation strategies by computer simulations before clinical evaluations. A planar neuro-musculoskeletal arm model for post-stroke patients is developed. The main idea is to use a set of new coefficients, Muscle Significance Factors (MSF), to incorporate the effects of stroke in the muscle control performance. The model uses the optimal control theory to mimic the performance of the CNS and a two-link skeletal model with six muscles for the biomechanical part. The model was developed and evaluated using experimental data from six post-stroke patients with Brunnstrom levels of 4-6. The results show that MSFs are relatively distinct and independent from the arm motion which is used to determine their values. Its variation is in the range of 0-2.58% and decreases in higher Brunnstrom levels. The mean error of the model in predicting the path of motion varies from 0.9% in level 6 to 5.58% in level 4 subjects which can be considered a promising level of accuracy. Using the proposed model and the MSF to customize the model for each individual stroke patient seems a promising approach. It shows a reasonable level of robustness, i.e., independence from the type of motions and correlated with the severity of stroke, and accuracy in predicting the shape of the motion path.

Optimal neuro-fuzzy control of hepatitis C virus integrated by genetic algorithm

Hepatitis C blood born virus is a major cause of liver disease that more than three per cent of people in the world is dealing with, and the spread of hepatitis C virus (HCV) infection in different populations is one of the most important issues in epidemiology. In the present study, a new intelligent controller is developed and tested to control the hepatitis C infection in the population which the authors refer to as an optimal adaptive neuro-fuzzy controller. To design the controller, some data is required for training the employed adaptive neuro-fuzzy inference system (ANFIS) which is selected by the genetic algorithm. Using this algorithm, the best control signal for each state condition is chosen in order to minimise an objective function. Then, the prepared data is utilised to build and train the Takagi-Sugeno fuzzy structure of the ANFIS and this structure is used as the controller. Simulation results show that there is a significant decrease in the number of acute-infected individuals by employing the proposed control method in comparison with the case of no intervention. Moreover, the authors proposed method improves the value of the objective function by 19% compared with the ordinary optimal control methods used previously for HCV epidemic.