Showing papers by "Eduardo Palermo published in 2021"

Estimation of Human Center of Mass Position through the Inertial Sensors-Based Methods in Postural Tasks: An Accuracy Evaluation

Abstract: The estimation of the body’s center of mass (CoM) trajectory is typically obtained using force platforms, or optoelectronic systems (OS), bounding the assessment inside a laboratory setting. The use of magneto-inertial measurement units (MIMUs) allows for more ecological evaluations, and previous studies proposed methods based on either a single sensor or a sensors’ network. In this study, we compared the accuracy of two methods based on MIMUs. Body CoM was estimated during six postural tasks performed by 15 healthy subjects, using data collected by a single sensor on the pelvis (Strapdown Integration Method, SDI), and seven sensors on the pelvis and lower limbs (Biomechanical Model, BM). The accuracy of the two methods was compared in terms of RMSE and estimation of posturographic parameters, using an OS as reference. The RMSE of the SDI was lower in tasks with little or no oscillations, while the BM outperformed in tasks with greater CoM displacement. Moreover, higher correlation coefficients were obtained between the posturographic parameters obtained with the BM and the OS. Our findings showed that the estimation of CoM displacement based on MIMU was reasonably accurate, and the use of the inertial sensors network methods should be preferred to estimate the kinematic parameters.

Microwave Characterization of 3D Printed PLA and PLA/CNT Composites

Abstract: The aim of this paper is to characterize dielectric properties of polylactic acid, one of the most commonly used materials for 3D-printing, at microwave frequencies, specifically at X-band. The knowledge of the complex permittivity of this plastic material is important for many applications. The manufacturing process of the printed object also requires to investigate the possible material anisotropy. Moreover, using polylactic acid doped with carbon nanotubes it is possible to manufacture conductive materials that can be used to realize electronic devices and microwave components. 3D-printed samples, based on the polylactic acid filament and polylactic acid filament doped with carbon nanotubes, were designed and manufactured by fused deposition modeling along three different axes, in order to characterize the material and examine also its possible anisotropic effect at microwave frequencies. Complex permittivity results were obtained from the scattering parameter data using the transmission line method and a suitable measurement model.

Soft large area FBG tactile sensors for exteroception and proprioception in a collaborative robotic manipulator

Abstract: Interest in tactile sensing technologies is advancing due to the growing adoption of robots in daily life activities. Human-machine interaction has thus to be safe, and collaborative robotics is becoming increasingly important. The present work features the design, development and preliminary validation of a soft large area sensor for tactile and proprioceptive sensing in a collaborative robotic manipulator. Such a manipulator is shaped to resemble the human hand and within this paper we focused on the index finger. The finger architecture has a design which allows setting up a structured 3D model, with flexible parametrization and fast prototyping. An optical fiber embedding 12 Fiber Bragg Gratings (FBGs) has been integrated in a soft polymeric matrix to mimic human sense of touch abilities of a whole finger. In order to assess the sensorized robotic manipulator, a mechatronic validation platform has been developed and employed. Preliminary results show a mechanical decoupling between exteroceptive and proprioceptive functions, and among the spatially distributed outputs of the sensor array. These results demonstrate the potential of the proposed approach towards achieving dexterous and fine capabilities in the manipulation of objects.

Measuring the Effect of Rhythmic Auditory Stimuli on Parkinsonian Gait in Challenging Settings

Abstract: Rhythmic auditory stimuli (RAS) improve the disabling motor symptom of Parkinson’s disease patients. In the large majority of studies, the effect of RAS has been assessed during common activities such as walking and turning. However, how RAS modulates parkinsonian behaviors in more challenging settings of daily living and whether a machine learning algorithm could classify them remains unclear. Eleven patients with idiopathic PD (age 72±7 years) were asked to walk under four conditions: straight walking, walking over an irregular surface, walking within a narrow pathway, and walking along a curving path (eight-shaped), with and without external stimulation. RAS pace was set at 110% of the normal cadence and spatio-temporal gait parameters were measured through two inertial measurement units placed on feet. k-Nearest Neighbor (k-NN) algorithm, with and without principal component analysis (PCA) as feature selector, was used for the classification of walking conditions. Cadence, gait speed, and gait time improved during RAS walking, regardless of challenging walking conditions. On the contrary, stride length increased only in straight walking, while gait speed showed improvement also in walking over an irregular surface and walking within narrow pathway conditions. k-NN algorithm reported higher accuracy (72.4%) in the classification of eight- shaped curving path both considering the overall feature set and a reduced one. These results open to the possibility of measuring RAS-induced effects on PD mobility in an ecological scenario and improving their administration based on the actual motor activity.