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Background/Objectives: Falls represent a major health concern for stroke survivors, necessitating effective risk assessment tools. This study proposes the Instrumented Fall Risk Assessment (IFRA) scale, a novel screening tool derived from Instrumented Timed Up and Go (ITUG) test data, designed to capture mobility measures often missed by traditional scales. Methods: We employed a two-step machine learning approach to develop the IFRA scale: first, identifying predictive mobility features from ITUG data and, second, creating a stratification strategy to classify patients into low-, medium-, or high-fall-risk categories. This study included 142 participants, who were divided into training (including synthetic cases), validation, and testing sets (comprising 22 non-fallers and 10 fallers). IFRA’s performance was compared against traditional clinical scales (e.g., standard TUG and Mini-BESTest) using Fisher’s Exact test. Results: Machine learning analysis identified specific features as key predictors, namely vertical and medio-lateral acceleration, and angular velocity during walking and sit-to-walk transitions. IFRA demonstrated a statistically significant association with fall status (Fisher’s Exact test p = 0.004) and was the only scale to assign more than half of the actual fallers to the high-risk category, outperforming the comparative clinical scales in this dataset. Conclusions: This proof-of-concept study demonstrates IFRA’s potential as an automated, complementary approach for fall risk stratification in post-stroke patients. While IFRA shows promising discriminative capability, particularly for identifying high-risk individuals, these preliminary findings require validation in larger cohorts before clinical implementation.

Background/Objectives: Falls represent a major health concern for stroke survivors, necessitating effective risk assessment tools. This study proposes the Instrumented Fall Risk Assessment (IFRA) scale, a novel screening tool derived from Instrumented Timed Up and Go (ITUG) test data, designed to capture mobility measures often missed by traditional scales. Methods: We employed a two-step machine learning approach to develop the IFRA scale: first, identifying predictive mobility features from ITUG data and, second, creating a stratification strategy to classify patients into low-, medium-, or high-fall-risk categories. This study included 142 participants, who were divided into training (including synthetic cases), validation, and testing sets (comprising 22 non-fallers and 10 fallers). IFRA's performance was compared against traditional clinical scales (e.g., standard TUG and Mini-BESTest) using Fisher's Exact test. Results: Machine learning analysis identified specific features as key predictors, namely vertical and medio-lateral acceleration, and angular velocity during walking and sit-to-walk transitions. IFRA demonstrated a statistically significant association with fall status (Fisher's Exact test p = 0.004) and was the only scale to assign more than half of the actual fallers to the high-risk category, outperforming the comparative clinical scales in this dataset. Conclusions: This proof-of-concept study demonstrates IFRA's potential as an automated, complementary approach for fall risk stratification in post-stroke patients. While IFRA shows promising discriminative capability, particularly for identifying high-risk individuals, these preliminary findings require validation in larger cohorts before clinical implementation.

Humans engaged in collaborative activities are naturally able to convey their intentions to teammates through multi-modal communication, which is made up of explicit and implicit cues. Similarly, a more natural form of human-robot collaboration may be achieved by enabling robots to convey their intentions to human teammates via multiple communication channels. In this paper, we postulate that a better communication may take place should collaborative robots be able to anticipate their movements to human teammates in an intuitive way. In order to support such a claim, we propose a robot system's architecture through which robots can communicate planned motions to human teammates leveraging a Mixed Reality interface powered by modern head-mounted displays. Specifically, the robot's hologram, which is superimposed to the real robot in the human teammate's point of view, shows the robot's future movements, allowing the human to understand them in advance, and possibly react to them in an appropriate way. We conduct a preliminary user study to evaluate the effectiveness of the proposed anticipatory visualization during a complex collaborative task. The experimental results suggest that an improved and more natural collaboration can be achieved by employing this anticipatory communication mode.

This article presents an open-source architecture for conveying robots' intentions to human teammates using Mixed Reality and Head-Mounted Displays. The architecture has been developed focusing on its modularity and re-usability aspects. Both binaries and source code are available, enabling researchers and companies to adopt the proposed architecture as a standalone solution or to integrate it in more comprehensive implementations. Due to its scalability, the proposed architecture can be easily employed to develop shared Mixed Reality experiences involving multiple robots and human teammates in complex collaborative scenarios.

Close human-robot interaction (HRI), especially in industrial scenarios, has been vastly investigated for the advantages of combining human and robot skills. For an effective HRI, the validity of currently available human-machine communication media or tools should be questioned, and new communication modalities should be explored. This article proposes a modular architecture allowing human operators to interact with robots through different modalities. In particular, we implemented the architecture to handle gestural and touchscreen input, respectively, using a smartwatch and a tablet. Finally, we performed a comparative user experience study between these two modalities.

Plantar intrinsic muscles play a pivotal role in posture control and gait dynamics. They help maintain the longitudinal and transverse arches of the foot, and they regulate the degree and velocity of arch deformation during walking or running. Consequently, pathologies affecting the plantar intrinsic muscles (for instance, acquired and inherited neuropathies) lead to foot deformity, gait disorders, and painful syndromes. Intrinsic muscle malfunctioning is also associated with multifactorial overuse or degenerative conditions such as pes planus, hallux valgus, and plantar fasciitis. As the clinical examination of each intrinsic muscle is challenging, ultrasound is gaining a growing interest as an imaging tool to investigate the trophism of these muscular structures and the pattern of their alterations, and potentially to follow up on the effects of dedicated rehabilitation protocols. The ten plantar intrinsic muscles can be dived into three groups (medial, central and lateral) and four layers. Here, we propose a regional and landmark-based approach to the complex sonoanatomy of the plantar intrinsic muscles in order to facilitate the correct identification of each muscle from the superficial to the deepest layer. We also summarize the pathological ultrasound findings that can be encountered when scanning the plantar muscles, pointing out the patterns of alterations specific to certain conditions, such as plantar nerves mononeuropathies.

Intrinsic hand muscles play a fundamental role in tuning the fine motricity of the hand and may be affected by several pathologic conditions, including traumatic injuries, atrophic changes induced by denervation, and space-occupying masses. Modern hand surgery techniques allow to target several hand muscle pathologies and, as a direct consequence, requests for hand imaging now carry increasingly complex diagnostic questions. The progressive refinement of ultrasound technology and the current availability of high and ultra-high frequency linear transducers that allow the investigation of intrinsic hand muscles and tendons with incomparable resolution have made this modality an essential tool for the evaluation of pathological processes involving these tiny structures. Indeed, intrinsic hand muscles lie in a superficial position and are amenable to investigation by means of transducers with frequency bands superior to 20 MHz, offering clear advantages in terms of resolution and costs compared to magnetic resonance imaging. In addition, ultrasound allows to perform dynamic maneuvers that can critically enhance its diagnostic power, by examining the questioned structure during stress tests that simulate the conditions eliciting clinical symptoms. The present article aims to review the anatomy, the ultrasound scanning technique, and the clinical application of thenar, hypothenar, lumbricals and interossei muscles imaging, also showing some examples of pathology involving these structures.

We present a cosmological hydrodynamical simulation of a representative volume of the Universe, as part of the Making Galaxies in a Cosmological Context (MaGICC) project. MaGICC uses a thermal implementation for supernova and early stellar feedback. This work tests the feedback model at lower resolution across a range of galaxy masses, morphologies and merger histories. The simulated sample compares well with observations of high redshift galaxies (\\(z \\ge 2\\)) including the stellar mass - halo mass (\\(M_\\star - M_h\\) ) relation, the Galaxy Stellar Mass Function (GSMF) at low masses (\\(M_\\star \\lt 5 \\times 10^{10} M_\\odot\\) ) and the number density evolution of low mass galaxies. The poor match of \\(M_\\star - M_h\\) and the GSMF at high masses (\\(M_\\star \\ge 5 \\times 10^{10} M_\\odot\\) ) indicates supernova feedback is insufficient to limit star formation in these haloes. At \\(z = 0\\), our model produces too many stars in massive galaxies and slightly underpredicts the stellar mass around \\(L_\\star\\) mass galaxy. Altogether our results suggest that early stellar feedback, in conjunction with supernovae feedback, plays a major role in regulating the properties of low mass galaxies at high redshift.

Recent studies have found a dramatic difference between the observed number density evolution of low mass galaxies and that predicted by semi-analytic models. While models accurately reproduce the z=0 number density, they require that the evolution occurs rapidly at early times, which is incompatible with the strong late evolution found observationally. We report here the same discrepancy in two state-of-the-art cosmological hydrodynamical simulations, which is evidence that the problem is fundamental. We search for the underlying cause of this problem using two complementary methods. Firstly, we look for evidence of a different history of today's low mass galaxies in models and observations and we find that the models yield too few young, strongly star-forming galaxies. Secondly, we construct a toy model to link the observed evolution of specific star formation rates (sSFR) with the evolution of the galaxy stellar mass function. We infer from this model that a key problem in both semi-analytic and hydrodynamical models is the presence of a positive instead of a negative correlation between sSFR and stellar mass. A similar positive correlation is found between the specific dark matter halo accretion rate and the halo mass, indicating that model galaxies are growing in a way that follows the growth of their host haloes too closely. It therefore appears necessary to find a mechanism that decouples the growth of low mass galaxies, which occurs primarily at late times, from the growth of their host haloes, which occurs primarily at early times. We argue that the current form of star-formation driven feedback implemented in most galaxy formation models is unlikely to achieve this goal, owing to its fundamental dependence on host halo mass and time. [Abridged]

The reionization of the Universe has profound effects on the way galaxies form and on their observed properties at later times. Of particular importance is the relative timing of the reionization history of a region and its halo assembly history, which can affect the nature of the first stars formed in that region, the properties and radial distribution of its stellar halo, globular cluster population and its satellite galaxies. We distinguish two basic cases for the reionization of a halo - internal reionization, whereby the stars forming in situ reionize their host galaxy, and external reionization, whereby the progenitor of a galaxy is reionized by external radiation before its own stars are able to form in sufficient numbers. We use a set of large-scale radiative transfer and structure formation simulations, based on cosmologies derived from both WMAP 1-year and WMAP 3-year data, to evaluate the mean reionization redshifts and the probability of internal/external reionization for Local Group-like systems, galaxies in the field and central cD galaxies in clusters. We find that these probabilities are strongly dependent on the underlying cosmology and the efficiency of photon production, but also on the halo mass. There is a rapid transition between predominantly external and predominantly internal reionization at a mass scale of 1.0e12 Msun (corresponding roughly to L*galaxies), with haloes less massive than this being reionized preferentially from distant sources. We provide a fit for the reionization redshift as a function of halo mass, which could be helpful to parameterize reionization in semi-analytical models of galaxy formation on cosmological scales. We find no statistical correlation between the reionization history of field galaxies and their environment.