Wearable sensors system for an improved analysis of freezing of gait in Parkinson's disease using electromyography and inertial signals

We propose a wearable sensor system for automatic, continuous and ubiquitous analysis of Freezing of Gait (FOG), in patients affected by Parkinson's disease. FOG is an unpredictable gait disorder with different clinical manifestations, as the trembling and the shuffling-like phenotypes, whose underlying pathophysiology is not fully understood yet. Typical trembling-like subtype features are lack of postural adaptation and abrupt trunk inclination, which in general can increase the fall probability. The targets of this work are detecting the FOG episodes, distinguishing the phenotype and analyzing the muscle activity during and outside FOG, toward a deeper insight in the disorder pathophysiology and the assessment of the fall risk associated to the FOG subtype. To this aim, gyroscopes and surface electromyography integrated in wearable devices sense simultaneously movements and action potentials of antagonist leg muscles. Dedicated algorithms allow the timely detection of the FOG episode and, for the first time, the automatic distinction of the FOG phenotypes, which can enable associating a fall risk to the subtype. Thanks to the possibility of detecting muscles contractions and stretching exactly during FOG, a deeper insight into the pathophysiological underpinnings of the different phenotypes can be achieved, which is an innovative approach with respect to the state of art

Modeling waves in fluids flowing over and through poroelastic media

Multiscale homogenization represents a powerful tool to treat certain fluid-structure interaction problems involving porous, elastic, fibrous media. This is shown here for the case of the interaction between a Newtonian fluid and a poroelastic, microstructured material. Microscopic problems are set up to determine effective tensorial properties (elasticity, permeability, porosity, bulk compliance of the solid skeleton) of the homogenized medium, both in the interior and at its boundary with the fluid domain, and an extensive description is provided of such properties for varying porosity. The macroscopic equations which are derived by homogenization theory employ such effective properties thus permitting the computation of velocities and displacements within the poroelastic mixture for two representative configurations of standing and travelling waves

Stand-alone wearable system for ubiquitous real-time monitoring of muscle activation potentials

Wearable technology is attracting most attention in healthcare for the acquisition of physiological signals. We propose a stand-alone wearable surface ElectroMyoGraphy (sEMG) system for monitoring the muscle activity in real time. With respect to other wearable sEMG devices, the proposed system includes circuits for detecting the muscle activation potentials and it embeds the complete real-time data processing, without using any external device. The system is optimized with respect to power consumption, with a measured battery life that allows for monitoring the activity during the day. Thanks to its compactness and energy autonomy, it can be used outdoor and it provides a pathway to valuable diagnostic data sets for patients during their own day-life. Our system has performances that are comparable to state-of-art wired equipment in the detection of muscle contractions with the advantage of being wearable, compact, and ubiquitous

A penalization method to treat the interface between a free-fluid region and a fibrous porous medium

The coupling between the flow through a fibrous porous medium and that in a free-fluid region is studied. The flow dynamics inside the porous medium are described using the volume averaging method applied to the incompressible Navier−Stokes equations in the laminar regime. The two different flow domains are coupled via a penalization method that consists of varying the porous medium properties (porosity and permeability) continuously across the interface. This approach permits the use of the same set of the equations throughout the whole domain. The averaging method is validated against simulations which fully account for the presence of cylindrical fibers positioned at the bottom wall of a square driven cavity. Numerical experiments are carried out for two different Reynolds numbers, large enough to ensure that inertial effects inside the porous domain are not negligible. Good agreement is found when comparing the two approaches

Generalized slip condition over rough surfaces

A macroscopic boundary condition to be used when a fluid flows over a rough surface is derived. It provides the slip velocity uS on an equivalent (smooth) surface in the form uS =EL:E, where the dimensionless parameter E is a measure of the roughness amplitude, E denotes the strain-rate tensor associated with the outer flow in the vicinity of the surface and L is a third-order slip tensor arising from the microscopic geometry characterizing the rough surface. This boundary condition represents the tensorial generalization of the classical Navier slip condition. We derive this condition, in the limit of small microscopic Reynolds numbers, using a multi-scale technique that yields a closed system of equations, the solution of which allows the slip tensor to be univocally calculated, once the roughness geometry is specified. We validate this generalized slip condition by considering the flow about a rough sphere, the surface of which is covered with a hexagonal lattice of cylindrical protrusions. Comparisons with direct numerical simulations performed in both laminar and turbulent regimes allow us to assess the validity and limitations of this condition and of the mathematical model underlying the determination of the slip tensor L

Prediction of Freezing of Gait in Parkinson’s Disease using Wearables and Machine Learning

Freezing of gait (FOG) is one of the most troublesome symptoms of Parkinson’s disease, affecting more than 50% of patients in advanced stages of the disease. Wearable technology has been widely used for its automatic detection, and some papers have been recently published in the direction of its prediction. Such predictions may be used for the administration of cues, in order to prevent the occurrence of gait freezing. The aim of the present study was to propose a wearable system able to catch the typical degradation of the walking pattern preceding FOG episodes, to achieve reliable FOG prediction using machine learning algorithms and verify whether dopaminergic therapy affects the ability of our system to detect and predict FOG. Methods: A cohort of 11 Parkinson’s disease patients receiving (on) and not receiving (off) dopaminergic therapy was equipped with two inertial sensors placed on each shin, and asked to perform a timed up and go test. We performed a step-to-step segmentation of the angular velocity signals and subsequent feature extraction from both time and frequency domains. We employed a wrapper approach for feature selection and optimized different machine learning classifiers in order to catch FOG and pre-FOG episodes. Results: The implemented FOG detection algorithm achieved excellent performance in a leave-one-subject-out validation, in patients both on and off therapy. As for pre-FOG detection, the implemented classification algorithm achieved 84.1% (85.5%) sensitivity, 85.9% (86.3%) specificity and 85.5% (86.1%) accuracy in leave-onesubject- out validation, in patients on (off) therapy. When the classification model was trained with data from patients on (off) and tested on patients off (on), we found 84.0% (56.6%) sensitivity, 88.3% (92.5%) specificity and 87.4% (86.3%) accuracy. Conclusions: Machine learning models are capable of predicting FOG before its actual occurrence with adequate accuracy. The dopaminergic therapy affects pre-FOG gait patterns, thereby influencing the algorithm’s effectiveness

Peritoneal Hydatidosis

Secondary peritoneal hydatidosis is caused by spontaneous or iatrogenic rupture of hepatic echinococcal cysts. We describe the case of a 65-year-old Tunisian male patient with previous history of liver hydatidosis who presented to our attention with subocclusive status. Imaging revealed a retrovesical hydatid cyst, adherent to the sigmoid colon. The treatment of choice was surgical removal of the cyst and the sigmoid colon. The patient is now being closely followed up

Compliant riblets: problem formulation and effective macrostructural properties

Small-scale, deformable riblets, embedded within the viscous sublayer of a turbulent boundary layer and capable to adapt to the overlying motion, are studied. The wall micro-grooves are made of a linearly elastic material and can undergo small deformations; their collective behavior is assumed to occur over a large, elastic wavelength. The presence of two length scales allows for the use of a multiscale homogenization approach yielding microscopic problems for convolution kernels and parameters, which must then be employed in macroscopic boundary conditions to be enforced at a virtual wall through the riblets. The results found suggest that, in analogy to the case of rigid riblets, compliant, blade-like wall corrugations are more effective than triangular riblets in reducing skin friction drag, provided the spanwise periodicity of the indentations is sufficiently small for the creeping flow approximation to be tenable

Four Days Are Enough to Provide a Reliable Daily Step Count in Mild to Moderate Parkinson’s Disease through a Commercial Smartwatch

Daily steps could be a valuable indicator of real-world ambulation in Parkinson’s disease (PD). Nonetheless, no study to date has investigated the minimum number of days required to reliably estimate the average daily steps through commercial smartwatches in people with PD. Fifty-six patients were monitored through a commercial smartwatch for 5 consecutive days. The total daily steps for each day was recorded and the average daily steps was calculated as well as the working and weekend days average steps. The intraclass correlation coefficient (ICC) (3,k), standard error of measurement (SEM), Bland–Altman statistics, and minimum detectable change (MDC) were used to evaluate the reliability of the step count for every combination of 2–5 days. The threshold for acceptability was set at an ICC ≥ 0.8 with a lower bound of CI 95% ≥ 0.75 and a SAM < 10%. ANOVA and Mann–Whitney tests were used to compare steps across the days and between the working and weekend days, respectively. Four days were needed to achieve an acceptable reliability (ICC range: 0.84–0.90; SAM range: 7.8–9.4%). In addition, daily steps did not significantly differ across the days and between the working and weekend days. These findings could support the use of step count as a walking activity index and could be relevant to developing monitoring, preventive, and rehabilitation strategies for people with PD

Fifteen years of wireless sensors for balance assessment in neurological disorders

Balance impairment is a major mechanism behind falling along with environmental hazards. Under physiological conditions, ageing leads to a progressive decline in balance control per se. Moreover, various neurological disorders further increase the risk of falls by deteriorating specific nervous system functions contributing to balance. Over the last 15 years, significant advancements in technology have provided wearable solutions for balance evaluation and the management of postural instability in patients with neurological disorders. This narrative review aims to address the topic of balance and wireless sensors in several neurological disorders, including Alzheimer's disease, Parkinson's disease, multiple sclerosis, stroke, and other neurodegenerative and acute clinical syndromes. The review discusses the physiological and pathophysiological bases of balance in neurological disorders as well as the traditional and innovative instruments currently available for balance assessment. The technical and clinical perspectives of wearable technologies, as well as current challenges in the field of teleneurology, are also examined