# Sensorimotor Control Using Adaptive Neuro-Fuzzy Inference for Human-Like Arm Movement
###### Abstract  
In this study, a sensorimotor controller is designed to characterize the required muscle force to enable a robotics system to perform a human-like circular movement. When the appropriate muscle internal forces are chosen, the arm end-point tracks the desired path via joint-space feedback. An objective function of the least-change rate of muscle forces is determined to find suitable feedback gains. The parameter defining the muscle force is then treated as a learning parameter through an adaptive neuro-fuzzy inference system, incorporating the rate of change of muscle forces. In experimental section, the arm motion of healthy subjects is captured using the inertial measurement unit sensors, and then the image of the drawn path is processed. The inertial measurement unit sensors detect each segment motion's orientation using quaternions, and the image is employed to identify the exact end-point position. Experimental data on arm movement are then utilized in the control parameter computation. The proposed brain-motor control mechanism enhances motion performance, resulting in a more human-like movement. sensorimotor control; adaptive neuro-fuzzy inference system; human-like movement +
Footnote †: journal: Medical  
The importance of incorporating these natural movements is particularly apparent in using rehabilitation robots. Accordingly, the control mechanisms for these robots require knowledge of natural human activity. However, while applications of human movements have been explored, performing human-like actions remains considerably challenging [14]. A significant factor contributing to the challenge is the complexity of ill-posed problems arising from the inherent joint and muscle redundancies. Moreover, the coupling between the muscle and joint spaces in the human body presents an additional obstacle. The nonlinear properties of muscles also add another complexity to the control of human-like actions [15]. In order to handle such challenges and perform human-like movements, model-free and model-based control approaches have been developed for the musculoskeletal systems. Several model-free control designs are addressed in the literature [16; 17; 18; 19]. To generate the muscle activation, deep neural networks (DNNs) are trained with inverse dynamics and optimal control for the point-to-point reaching movements [16]. A biomimetic sensorimotor structure is developed in [17] to control a human musculoskeletal model. Based on visual information, the authors partition the human body into separate modules that regulate muscle activations for specific body regions. Then, they generalize the method to encompass a comprehensive human biomechanical simulation with a sensorimotor control system comprising connected DNNs. In addition to the supervised learning techniques discussed in [16; 17], there have been efforts to apply reinforcement learning algorithms for the reaching movements, as studied in [18; 19]. In the study by [PERSON] et al. [18], a learning rule employing a recurrent neural network (RNN) is introduced for high-level motor control in a musculoskeletal robot. Meanwhile, [PERSON] et al. [19] proposes a motion learning framework inspired by human movements for a musculoskeletal system, which involves identifying muscle synergies and implementing an iterative learning controller. However, while model-free controllers offer benefits such as simplicity and adaptability, their lack of a precise model can lead to challenges in predicting system behavior, requiring extensive data for training and potentially resulting in less precise control in complex musculoskeletal systems. In model-based control methods, computing muscle force mostly relies on inverse kinematics, inverse dynamics, and muscle-force optimization approaches. Various model-based control designs have been examined to find the required muscle force, allowing the robotics systems to perform human-like movements. [PERSON] et al. [20] demonstrate that a redundant planar robot can achieve human-like multi-joint reaching movement.