Maintaining an upright posture is a relatively easy task for healthy humans [1, 2]. However, bipedal upright stance is inherently unstable, as small deviations from the upright posture result in disturbing torques due to gravity, which drives the system further away from upright posture . To stay upright, the body generates corrective torques to counteract the effects of internal (e.g., motor and sensory noise) and external (e.g., uneven surfaces) perturbations.
When postural deviations are small, the body is often simplified as an inverted pendulum pivoting at the ankles, which describes the so-called ankle strategy [3–5]. However, several studies demonstrated that human movement during stance is multi-segmental [6–8] and for example, the hips substantially contribute to upright stance (i.e., the hip strategy ). Human balance control is a closed-loop multi-segmental process, i.e., sensory signals about the movement of the body are fed back to the central nervous system (CNS), and the CNS controls the muscles to generate adequate responses [10, 11]. In a noisy closed-loop system, like human balance control, causality is difficult to determine and the dynamics of the different components (i.e., the body and the stabilizing mechanisms located in the CNS) affect both the input (joint angles) and output signals (joint torques). To “open” the loop and to separate the dynamics of the different components, the balance system need to be perturbed [11, 12]. Furthermore, when estimating the dynamics in a multivariate system, multiple perturbations need to be applied [13, 14].
Most studies investigating the multivariate nature of balance control do not take the multivariate noisy closed-loop nature into account, by either not using perturbations [15–17], or by using only one perturbation [18–20]. Only two studies investigated the multivariate nature of balance control by applying two perturbations [2, 21].
Fujisawa and colleagues  investigated the role of the hip joint to upright stance by applying pseudorandom perturbations (bandwidth 0 - 0.83 Hz) while manipulating the support surface width. Subsequently, an ARMAX model (with joint angles as input and joint torques as outputs) was fitted to the data to obtain the Frequency Response Functions (FRFs) of a two-segment model of balance control. Results showed an increase of balance contribution of the hip joint, when the support surface became narrower.
Jeka and colleagues  identified neural feedback during upright stance in 18 subjects, while applying two mechanical perturbations (springs attached to a linear motor) and one sensory perturbation (visual scene rotations). By comparing the identified neural feedback (from joint angles to weighted electromyograms (EMGs) of the leg and trunk segments) with a large range of cost functions, it was concluded that the CNS stabilizes stance with near minimum muscle activation.
Ageing and many neurological diseases are associated with balance impairments and falls . Understanding the (patho)physiology of upright stance could aid to detect individuals with an increased risk of falls, help to design and evaluate intervention programs or monitor disease progression. Therefore, for clinical applications, it is very important to obtain a reliable individual estimate of balance control.
Of all neurological diseases, PD patients are at the highest risk of falling [22–24], but the pathophysiology of balance impairments in PD remains unclear [25, 26]. Recently, it was suggested that one of the factors contributing to decreased balance control in PD patients, is impaired trunk control [27, 28] or a decreased intersegmental coordination [29, 30]. Another factor could be asymmetrical balance control, that is when one leg produces more force than the other leg to maintain an upright posture. Asymmetries in balance control have been rarely studied in PD, although it is an asymmetrical disease . One study  found balance control asymmetries in 24% of the PD participants, indicating that balance asymmetries are important in PD.
Currently, there is no method available that can identify a multisegmental stabilizing mechanism of balance control on an individual level, separating the contribution of the joints of the left and right body side. We developed and evaluated a non-parametric MIMO (Multiple-Input-Multiple-Output) identification method based on the previously used non-parametric system SISO (Single-Input-Single-Output) identification method . To obtain a reliable individual response, periodic perturbations were applied, which have the advantage of having power at specific frequencies, decreasing the measurement time, and increasing the participants response. In addition, the stabilizing mechanisms were estimated based on left and right joint torques (contrary to weighted EMGs; ), to be able to investigate balance control asymmetries.
In sum, our goal was to develop a method that can reliably estimate the stabilizing mechanisms of the closed-loop multivariate balance control system of individual participants, making a distinction between the contribution of the left and right leg. The (clinical) applicability is demonstrated in an experiment that perturbed the balance of seven healthy participants and a PD patient with a novel device that can apply two independent mechanical perturbations.