Antonio Lanzotti

Objective: Falls among the elderly constitute a major global public health issue. Research has therefore focused on two complementary areas: fall risk prevention and fall detection. Existing solutions mainly rely on body-attached sensors confined to laboratory settings, whereas recent research has shifted toward wearable e-textiles with non-invasive, long-term-wear sensors. In this context, the study presents a wearable sensor platform to both predict fall risk and detect falls, based on sensorized clothing integrating inertial measurement units and surface electromyography sensors. Methods: Fall risk was estimated from gait parameters extracted during a 10 m walking test as the probability of belonging to a faller (vs. non-faller) group, using a logistic regression model trained on the G-STRIDE dataset, complemented by neuromuscular parameters extracted from sEMG and associated with fall risk. Fall detection, focused on improving pre-impact identification, was framed as a binary classification between activities of daily living and falls, using a reduced Spatio-Temporal Attention Network trained on the FallTL dataset and refined with our own platform’s data. Results: The obtained results were: fall-risk assessment, Area Under the Curve = 77.8%, Accuracy = 68.7%; fall detection, Accuracy = 96.7%, F1 Score = 77.3%, lead time = 390 ms. Conclusions: As a single-subject proof of concept, these results support the feasibility of a unified platform integrating objective fall-risk screening and fall event identification, with a predicted time before impact suitable for protective systems intervention; validation on a larger, representative cohort is required before any clinical claim can be made.

This paper describes the design and preliminary testing of a soft exoskeleton for the shoulder which supports the adbuction/adduction movements. The soft exoskeleton is based on a single soft bellow actuator, a pneumatically actuated system which is composed by multiple consecutive chambers that expands when compressed air is inflated till providing a desired motion. In this work we present the design, analysis, prototyping and testing of the soft bellow actuator as well as its preliminary integration into a first version of bellow-based soft exoskeleton for the shoulder.

Evaluations of injuries and the assessment of residual dexterity is carried out by clinicians by means of standardized tests. Several dexterity tests have been developed and all of them require the test administrator to sit in front of the subject and visually assess the motions performed. Automatic tools can reduce the time needed for the evaluation and can provide additional useful information. For many of the available dexterity tests, automated evaluation methods have been developed. However, for the Minnesota dexterity test, despite being widely used, just one automated solution based on multiple depth sensor cameras was developed. This study aims to provide a wearable and flexible method to automatically evaluate the outcomes of Minnesota dexterity tests. The proposed methodology consists of a parametric model that is capable of processing acceleration data collected with an IMU attached to the centre of mass of the subject’s dominant hand. The developed model was successfully validated against a subject study with ten participants.

Background/Objectives: One of the most serious complications following implant placement in the atrophic posterior mandible is injury to the inferior alveolar nerve (IAN), which can also happen during occlusal loading of the implants. This study investigates the effects of 4 mm implant stress transmission to the inferior alveolar nerve during occlusal loading in cases of severe posterior mandibular atrophy. Methods: The computer-aided design (CAD) model was created and modified through Direct Modeling techniques. The structure of cortical and trabecular bones was simplified, and it was modeled as a cylinder block. Finite element analysis (FEA) was carried out in 3D to investigate the pressure distribution over the IAN at different implant-to-nerve distances (1.5 mm, 0.5 mm, and 0.1 mm), and stress and strain deformations were simulated in the mandibular model. Results: The results of the pressure analysis on the inferior alveolar nerve indicate that the pressure distribution at different implant-to-nerve distances (1.5 mm, 0.5 mm, and 0.1 mm) is consistently below 0.026 MPa, which corresponds to the maximum pressure range that may block nerve impulses. This occurs even at the theoretical and simulated distance of 0.1 mm, suggesting that cortical bone stiffness plays a crucial role in mitigating stress at reduced implant-to-nerve proximities. Conclusions: Within the limits of this study, ultra-short implants can be placed even less than 0.5 mm (up to 0.1 mm under the 3D-FEA hypothesis) above the inferior alveolar nerve under the 3D-FEA hypothesis, while maintaining pressure below the threshold value. This is due to the rigidity of the cortical bone, which helps to reduce pressure transmission to the nerve. These findings may expand the indications for ultra-short implants, even in mandibles with a residual bone height of just 4 mm.