Abstract
Recent advances in the control of overground exoskeletons are being centered on improving balance support and decreasing the reliance on crutches. However, appropriate methods to quantify the stability of these exoskeletons (and their users) are still under development. A reliable and reproducible balance assessment is critical to enrich exoskeletons’ performance and their interaction with humans. In this work, we present the BenchBalance system, which is a benchmarking solution to conduct reproducible balance assessments of exoskeletons and their users. Integrating two key elements, i.e., a hand-held perturbator and a smart garment, BenchBalance is a portable and low-cost system that provides a quantitative assessment related to the reaction and capacity of wearable exoskeletons and their users to respond to controlled external perturbations. A software interface is used to guide the experimenter throughout a predefined protocol of measurable perturbations, taking into account antero-posterior and mediolateral responses. In total, the protocol is composed of sixteen perturbation conditions, which vary in magnitude and location while still controlling their orientation. The data acquired by the interface are classified and saved for a subsequent analysis based on synthetic metrics. In this paper, we present a proof of principle of the BenchBalance system with a healthy user in two scenarios: subject not wearing and subject wearing the H2 lower-limb exoskeleton. After a brief training period, the experimenter was able to provide the manual perturbations of the protocol in a consistent and reproducible way. The balance metrics defined within the BenchBalance framework were able to detect differences in performance depending on the perturbation magnitude, location, and the presence or not of the exoskeleton. The BenchBalance system will be integrated at EUROBENCH facilities to benchmark the balance capabilities of wearable exoskeletons and their users.
Highlights
IntroductionWearable robotic devices such as lower-limb exoskeletons have attracted extensive interest in the last decades, demonstrating the ability to support people with motor impairments in standing and walking
Quantify the disturbance applied in terms of force magnitude and orientation; Quantify where a perturbation is applied on the human body, since balance recovery strategies might differ depending on the location of such perturbation; Ensure an appropriate synchronization of the perturbation with the user’s response; Provide real-time feedback to the experimenter to augment the ability of providing perturbations in a consistent way; Calculate outcome indicators to quantify the balance response by using kinematic data collected either with any motion capture system (Mocap) or with the on-board exoskeleton sensors
The BenchBalance testbed (Figure 1) is composed of the following: (1) a portable hand-held perturbator equipped with force and orientation sensors, which is used to provide and quantify well-defined pushes to the human upper body during both standing and walking conditions; (2) a position system detector, which is used to determine the location of the generated perturbation in relation to the human wearing the exoskeleton; and (3) an analysis unit that combines the information of the provided force with joint angle data from the wearable exoskeleton or Mocap system and generates the Balance Indicators (BIs), i.e., metrics to complete the assessment
Summary
Wearable robotic devices such as lower-limb exoskeletons have attracted extensive interest in the last decades, demonstrating the ability to support people with motor impairments in standing and walking. Recent research on these devices is moving toward the development of controllers to assist balance during standing and/or walking. These controllers may involve different degrees of freedom (DOFs) to improve exoskeleton-user stability and may vary in complexity, using (1) control strategies derived from humanoids applications [1,2,3,4], (2) bio-inspired approaches [5,6,7], or (3) simple heuristic methods [8,9,10].
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