Center of pressure displacement due to graded controlled perturbations to the trunk in standing subjects: the force–impulse paradigm

Paterna, Maria; Dvir, Zeevi; Benedictis, Carlo De; Maffiodo, Daniela; Franco, Walter; Ferraresi, Carlo; Roatta, Silvestro

doi:10.1007/s00421-021-04844-9

Cited by 3 publications

(2 citation statements)

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“…Although increased with respect to the best profile presented in the HIL test (Fig. 8b, c), the variability of the perturbation magnitude is comparable with that observed in previous experimental studies [41][42][43][44]. Moreover, FID and TAE mean values are, respectively, still less than 5% and 15%.…”

Section: Human-in-the-loop-optimization Resultssupporting

confidence: 88%

“…The presented MPC algorithm can be used to control the mechanical disturbances (i.e., perturbations) provided to a patient’s body [37, 38, 40] to investigate balance and posture issues. Preliminary studies highlighted that the force impulse (force-time integral, FI) resulting from the contact should range within 2–10 Ns to elicit a detectable postural response and, at the same time, to keep the subject in the standing position without any risk of falling [41, 42]. To obtain the desired FI in a brief time, comparable with the neuromuscular response time, a rectangular force profile of 250 ms and a magnitude between 20 N and 50 N was chosen as the reference force profile.…”

Section: Model Predictive Control To Manage Human-machine Interactionmentioning

confidence: 99%

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Contact force regulation in physical human-machine interaction based on model predictive control

Pacheco Quiñones,

Paterna,

De Benedictis

et al. 2023

Robotica

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With increasing attention to physical human-machine interaction (pHMI), new control methods involving contact force regulation in collaborative and coexistence scenarios have spread in recent years. Thanks to its internal robustness, high dynamic performance, and capabilities to avoid constraint violations, a Model Predictive Control (MPC) action can pose a viable solution to manage the uncertainties involved in those applications. This paper uses an MPC-driven control method that aims to apply a well-defined and tunable force impulse on a human subject. After describing a general control design suitable to achieve this goal, a practical implementation of such a logic, based on an MPC controller, is shown. In particular, the physical interaction considered is the one occurring between the body of a patient and an external perturbation device in a dynamic posturography trial. The device prototype is presented in both its hardware architecture and software design. The MPC-based main control parameters are thus tuned inside hardware-in-the-loop and human-in-the-loop environments to get optimal behaviors. Finally, the device performance is analyzed to assess the MPC algorithm’s accuracy, repeatability, flexibility, and robustness concerning the several uncertainties due to the specific pHMI environment considered.

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Section: Human-in-the-loop-optimization Resultssupporting

confidence: 88%