Development and Evaluation of BenchBalance: A System for Benchmarking Balance Capabilities of Wearable Robots and Their Users.

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.

A simple tool to measure spasticity in spinal cord injury subjects.

This work presents a wearable device and the algorithms for quantitative modelling of joint spasticity and its application in a pilot group of subjects with different levels of spinal cord injury. The device comprises light-weight instrumented handles to measure the interaction force between the subject and the physical therapist performing the tests, EMG sensors and inertial measurement units to measure muscle activity and joint kinematics. Experimental tests included the passive movement of different body segments, where the spasticity was expected, at different velocities. Tonic stretch reflex thresholds and their velocity modulation factor are computed, as a quantitative index of spasticity, by using the kinematics data at the onset of spasm detected through thresholding the EMG data. This technique was applied to two spinal cord injury subjects. The proposed method allowed the analysis of spasticity at muscle and joint levels. The obtained results are in line with the expert diagnosis and qualitative spasticity characterisation on each individual.

An Adaptive Neuromuscular Controller for Assistive Lower-Limb Exoskeletons: A Preliminary Study on Subjects with Spinal Cord Injury.

Versatility is important for a wearable exoskeleton controller to be responsive to both the user and the environment. These characteristics are especially important for subjects with spinal cord injury (SCI), where active recruitment of their own neuromuscular system could promote motor recovery. Here we demonstrate the capability of a novel, biologically-inspired neuromuscular controller (NMC) which uses dynamical models of lower limb muscles to assist the gait of SCI subjects. Advantages of this controller include robustness, modularity, and adaptability. The controller requires very few inputs (i.e., joint angles, stance, and swing detection), can be decomposed into relevant control modules (e.g., only knee or hip control), and can generate walking at different speeds and terrains in simulation. We performed a preliminary evaluation of this controller on a lower-limb knee and hip robotic gait trainer with seven subjects (N = 7, four with complete paraplegia, two incomplete, one healthy) to determine if the NMC could enable normal-like walking. During the experiment, SCI subjects walked with body weight support on a treadmill and could use the handrails. With controller assistance, subjects were able to walk at fast walking speeds for ambulatory SCI subjects-from 0.6 to 1.4 m/s. Measured joint angles and NMC-provided joint torques agreed reasonably well with kinematics and biological joint torques of a healthy subject in shod walking. Some differences were found between the torques, such as the lack of knee flexion near mid-stance, but joint angle trajectories did not seem greatly affected. The NMC also adjusted its torque output to provide more joint work at faster speeds and thus greater joint angles and step length. We also found that the optimal speed-step length curve observed in healthy humans emerged for most of the subjects, albeit with relatively longer step length at faster speeds. Therefore, with very few sensors and no predefined settings for multiple walking speeds or adjustments for subjects of differing anthropometry and walking ability, NMC enabled SCI subjects to walk at several speeds, including near healthy speeds, in a healthy-like manner. These preliminary results are promising for future implementation of neuromuscular controllers on wearable prototypes for real-world walking conditions.