On Internal Modeling of the Upright Postural Control in Elderly

• Published in: 2018 IEEE International Conference on Robotics and Biomimetics (ROBIO) pp. 231 - 236
• Main Authors: Jafari, H., Pauelsen, M., Roijezon, U., Nyberg, L., Nikolakopoulos, G., Gustafsson, T.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.12.2018
• Subjects: Adaptation models; Brain modeling; Delay effects; internal model; Kinematics; modeling; Muscles; posture; Predictive models; Principal component analysis; recurrent neural network; system identification
• DOI: 10.1109/ROBIO.2018.8665209

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.