The second most common cause of injury in the elderly population is falling. In an effort to understand the mechanism behind the reduced ability to maintain balance in any posture or activity, we study the performance of the central nervous system as a controller of the body, while maintaining the balance in some postures or activities. Towards this direction, forty-five subjects aged over 70 were tested in different trials of quiet stance: a) hard stable surface with open eyes, b) stable surface with closed eyes, c) soft unstable surface with open eyes, and d) unstable surface, while eyes were closed. In the sequel, the body kinematics were described by legs and trunk segment angles in the sagittal plane, while the muscle activations were described by a weighted sum of rectified EMG signals from tibialis anterior and gastrocnemius muscles of left and right legs. Using the neuro-science hypothesis and adaptive control theory, a completely novel model was identified for the CNS based on the feedback internal model. The proposed model is able to predict the output commands, based on a recurrent neural network, while the efficiency of the proposed scheme has been proven based on multiple experimental results, showing that the model can sufficiently predict the muscle activity based on the optimum sensory inputs.

The human body is mechanically unstable, while the brain as the main controller, is responsible to maintain our balance. However, the mechanisms of the brain towards balancing are still an open research question and thus in this article, we propose a novel modeling architecture for replicating and understanding the fundamental mechanisms for generating balance in the humans. Towards this aim, a nonlinear Recurrent Neural Network (RNN) has been proposed and trained that has the ability to predict the performance of the Central Nervous System (CNS) in stabilizing the human body with high accuracy and that has been trained based on multiple collected human based balancing data and by utilizing system identification techniques. One fundamental contribution of the article is the fact that the obtained network, for the balancing mechanisms, is experimentally evaluated on a single link inverted pendulum that replicates the basic model of the human balance and can be directly extended in the area of humanoids and balancing exoskeletons.

Understanding the performance of the human brain to stabilize the body remains an open fundamental research question. In this article, we study the hypothesis of internal model of the Central Nervous System (CNS) by a novel proposed architecture based on a recurrent neural network. The overall objective of the article and the main contribution stems from demonstrating the capability of replicating the balancing mechanisms of the brain by training the proposed bio-inspired network architecture with human balancing data and in the sequel applying the resulting control structure for controlling a single link inverted pendulum. Towards this direction, the body kinetics and kinematics measurements of forty-five subjects during upright stance trails were collected and utilized for training the proposed neural network. The efficacy of the proposed scheme will be proven through multiple simulation results with a single link inverted pendulum, where it will be demonstrated that the brain-inspired control scheme achieves a proper balance.