Human postural control is a complex system and changes as we age. Frequency based analyses have been argued to be useful to identify altered postural control strategies in balance tasks. The aim of this study was to explore the frequency domain of the quiet stance centre of pressure of older adults with various degrees of fall-related concerns and sensorimotor functioning. We included 45 community dwelling older adults and used a force plate to register 30 seconds of quiet stance with eyes open and closed respectively. We also measured sensory and motor functions, as well as fall-related concerns and morale. We analysed the centre of pressure power spectrum density and extracted the frequency of 4 of its features for each participant. Orthogonal projection of latent structures–discriminant analysis revealed two groups for each quiet stance trial. Group 1 of each trial showed less sensory and motor decline, low/no fall-related concerns and higher frequencies. Group 2 showed more decline, higher fall-related concerns and lower frequencies. During the closed eyes trial, group 1 and group 2 shifted their features to higher frequencies, but only group 1 did so in any significant way. Higher fall-related concerns, sensory and motor decline, and explorative balancing strategies are highly correlated. The control system of individuals experiencing this seems to be highly dependent on vision. Higher fall-related concerns, and sensory and motor decline are also correlated with the inability to adjust to faster, more reactive balancing strategies, when vision is not available.

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.

No abstract

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.

No abstract