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.

Variability is actively regulated in speech

Although movement variability is often attributed to unwanted noise in the motor system, recent work has demonstrated that variability may be actively controlled. To date, research on regulation of motor variability has relied on relatively simple, laboratory-specific reaching tasks. It is not clear how these results translate to complex, well-practiced and real-world tasks. Here, we test how variability is regulated during speech production, a complex, highly over-practiced and natural motor behavior that relies on auditory and somatosensory feedback. Specifically, in a series of four experiments, we assessed the effects of auditory feedback manipulations that modulate perceived speech variability, shifting every production either towards (inward-pushing) or away from (outward-pushing) the center of the distribution for each vowel. Participants exposed to the inward-pushing perturbation (Experiment 1) increased produced variability while the perturbation was applied as well as after it was removed. Unexpectedly, the outward-pushing perturbation (Experiment 2) also increased produced variability during exposure, but variability returned to near baseline levels when the perturbation was removed. Outward-pushing perturbations failed to reduce participants' produced variability both with larger perturbation magnitude (Experiment 3) or after their variability had increased above baseline levels as a result of the inward-pushing perturbation (Experiment 4). Simulations of the applied perturbations using a state space model of motor behavior suggest that the increases in produced variability in response to the two types of perturbations may arise through distinct mechanisms: an increase in controlled variability in response to the inward-pushing perturbation, and an increase in sensitivity to auditory errors in response to the outward-pushing perturbation. Together, these results suggest that motor variability is actively regulated even in complex and well-practiced behaviors, such as speech.

Numerical Prediction of the Phase Difference in Tube Bundles Experiencing Streamwise Fluidelastic Instability

Recent experimental investigations have shown that tube arrays can become unstable in the streamwise direction. This is contrary to the long-held notion that fluidelastic instability is only a concern in the direction transverse to the flow. The possibility of streamwise fluidelastic instability (FEI) as a potential threat to the integrity of tube bundles was confirmed by the recent failures of newly installed replacement steam generators. A number of investigations were conducted to uncover the nature of this mechanism. A theoretical framework was developed by Hassan and Weaver [1] to model streamwise fluidelastic instability in a bundle of flexible tubes. The model utilized a simple time lag expression for the flow channel area perturbation. The current work aims at developing a numerical model to precisely predict the flow perturbation characteristics in a tube bundle due to streamwise tube motion. Flow simulations were carried out for single phase fluid flow in a parallel triangle tube bundle array with 1.2, 1.5 and 1.7 pitch to diameter ratios. The numerical model was benchmarked against numerical and experimental results available in the FEI literature. Simulations were carried out for a range of reduced flow velocities. The model results showed that the upstream flow perturbation magnitude and phase are different from those obtained in the downstream of the moving tube. The obtained flow perturbation characteristics were implemented in the Hassan and Weaver [1] model and the streamwise FEI threshold was predicted.

MSAP: Multi-Step Adversarial Perturbations on Recommender Systems Embeddings

Recommender systems (RSs) have attained exceptional performance in learning users' preferences and finding the most suitable products. Recent advances in adversarial machine learning (AML) in computer vision have raised interests in recommenders' security.It has been demonstrated that widely adopted model-based recommenders, e.g., BPR-MF, are not robust to adversarial perturbations added on the learned parameters, e.g., users' embeddings, which can cause drastic reduction of recommendation accuracy.However, the state-of-the-art adversarial method, named fast gradient sign method (FGSM), builds the perturbation with a single-step procedure. In this work, we extend the FGSM method proposing multi-step adversarial perturbation (MSAP) procedures to study the recommenders' robustness under powerful methods. Letting fixed the perturbation magnitude, we illustrate that MSAP is much more harmful than FGSM in corrupting the recommendation performance of BPR-MF. Then, we assess the MSAP efficacy on a robustified version of BPR-MF, i.e., AMF. Finally, we analyze the variations of fairness measurements on each perturbed recommender. Code and data are available at https://github.com/sisinflab/MSAP.

The effect of perturbation magnitude on lower limb muscle activity during reactive stepping using functional data analysis

This study aimed to determine the effect of perturbation magnitude on stance and stepping limb muscle activation during reactive stepping using functional data analysis. Nineteen healthy, young adults responded to 6 small and 6 large perturbations using an anterior lean-and-release system, evoking a single reactive step. Muscle activity from surface electromyography was compared between the two conditions for medial gastrocnemius, biceps femoris, tibialis anterior, and vastus lateralis of the stance and stepping limb using functional data analysis. Stance limb medial gastrocnemius and biceps femoris activation increased in the large compared to small perturbation condition immediately prior to foot-off and at foot contact. In the stepping limb, significant increases in medial gastrocnemius, biceps femoris, and tibialis anterior activity occurred immediately prior to foot-off during the large perturbations. Similar to the stance limb, medial gastrocnemius and biceps femoris activity significantly increased during and following foot contact in the large, compared to small, perturbation condition. Lastly, vastus lateralis activity significantly increased for large, compared to small, perturbations during foot-off and immediately following foot contact. These findings highlight lower limb muscle activity modulation associated with perturbation magnitude throughout reactive stepping and the additional benefit of implementing functional data analysis to study reactive balance control.

Compensatory Responses to Formant Perturbations Proportionally Decrease as Perturbations Increase

Purpose We continuously monitor our speech output to detect potential errors in our productions. When we encounter errors, we rapidly change our speech output to compensate for the errors. However, it remains unclear whether we adjust the magnitude of our compensatory responses based on the characteristics of errors. Method Participants ( N = 30 adults) produced monosyllabic words containing /ɛ/ (/hɛp/, /hɛd/, /hɛk/) while receiving perturbed or unperturbed auditory feedback. In the perturbed trials, we applied two different types of formant perturbations: (a) the F1 shift, in which the first formant of /ɛ/ was increased, and (b) the F1–F2 shift, in which the first formant was increased and the second formant was decreased to make a participant's /ɛ/ sound like his or her /æ/. In each perturbation condition, we applied three participant-specific perturbation magnitudes (0.5, 1.0, and 1.5 ɛ–æ distance). Results Compensatory responses to perturbations with the magnitude of 1.5 ɛ–æ were proportionally smaller than responses to perturbation magnitudes of 0.5 ɛ–æ. Responses to the F1–F2 shift were larger than responses to the F1 shift regardless of the perturbation magnitude. Additionally, compensatory responses for /hɛd/ were smaller than responses for /hɛp/ and /hɛk/. Conclusions Overall, these results suggest that the brain uses its error evaluation to determine the extent of compensatory responses. The brain may also consider categorical errors and phonemic environments (e.g., articulatory configurations of the following phoneme) to determine the magnitude of its compensatory responses to auditory errors.