Disentangling the underlying systems involved in standing balance using system identification techniques

Even though maintaining upright quiet stance might be considered by humans as a trivial task, it requires a complex balance control mechanism, in which sensory, nervous and muscle subsystems continuously cooperate and interchange environmental information to stabilize posture. Age, diseases and medication often degrade the related subsystems and affect balance control. System redundancy in the closed-loop control system allows for corrections and, as such, certain balance control compensation strategies are employed by humans to regain stability. However, there are cases in which the compensation strategies are not sufficient, resulting in impaired human balance and increased risk of falling. Therefore, there is a need of an efficient model of human balance control, able to disentangle the underlying systems involved in standing balance and detect the primary source of balance control impairment. In the context of this study, first a multi-segmental human balance control model was developed, based on an experimental setup, which highly resembled balance control hazards encountered by humans in daily life. The model’s ability to describe the dynamics of the balance control subsystems and to quantify balance control parameters of physiological relevance was subsequently tested on simulated and experimental data, using system identification and parameter estimation techniques. The developed balance control model proved to be efficient in identifying the dynamics and estimating the related parameters of the nervous and muscle subsystems involved in standing balance, using the particular experimental paradigm. Further adjustments will enable the model to fully capture the dynamics of all of the underlying systems involved in standing balance and thus to be utilized in clinical applications.BMEBioMechanical EngineeringMechanical, Maritime and Materials Engineerin