We present the development of a novel time-domain near-infrared spectroscopy (TD-NIRS) system capable of parallel acquisition at seven distinct wavelengths and two source-detector distances, enabling the quantification of biological chromophores such as oxy- and deoxy-hemoglobin, water, lipids, and cytochrome c-oxidase. The proposed system integrates a commercial supercontinuum laser source (475–2400 nm) with an Acousto-Optic Tunable Filter (AOTF) for simultaneous selection of seven wavelengths within the 640–1100 nm range. Detection is achieved through a Silicon Photo-Multiplier (SiPM) array, each detector being coupled with a narrow-band interference filter to isolate specific wavelengths. The photon arrival times are recorded by means of a 16-channel Time-Correlated Single Photon Counting (TCSPC) system. Finally, a custom software with a graphical user interface enables the dynamic control over system operations, including the optimization of acquisition parameters, such as wavelength selection and time resolution. The system performance was assessed using the MEDPHOT protocol, that is based on calibrated tissue-simulating phantoms, verifying the instrument's capability to decouple absorption and scattering characteristics of diffuse samples. Results confirm the applicability of this system to physiological investigations, such as brain or muscle studies, offering high temporal resolution, multi-distance capability, and multi-wavelength sensitivity.

Twisted and Coiled Artificial Muscles (TCAMs) have emerged as innovative structural actuators, widely recognized for their outstanding performance characteristics. Fabricated from nylon or other polymeric fibers, TCAMs offer a unique combination of low cost, substantial displacement, high power-to-weight ratio, and adaptability, rendering them highly suitable for integration into robotics and smart textile applications. Despite their promising potential, several challenges remain, particularly concerning the full understanding of their behavior and optimization for real-world implementation.

This study presents a comprehensive investigation into the thermo-mechanical behavior of TCAMs produced using three types of silver-coated nylon precursor fibers. Experimental tests were conducted to evaluate how fabrication parameters—such as rotational speed, applied loads, and annealing conditions—affect actuator performance, including contraction capabilities and mechanical stability. Among the tested materials, TCAMs made from Shieldex 235/36x4 HCB fibers displayed superior displacement and load-bearing characteristics.

To complement the experimental observations, a physics-based thermo-electro-mechanical model was developed, integrating macro- and micromechanical principles. The model incorporates Castigliano’s Theorem to predict axial displacement and considers temperature-dependent material properties. Model predictions show strong agreement with experimental results across various conditions, reinforcing their validity and robustness.

The combined experimental and theoretical analysis highlights the critical role of controlled manufacturing and provides a framework for further enhancement of TCAM performance. This work significantly advances the understanding of TCAMs and supports their development for integration into advanced robotic and bioinspired systems.

The global increase in the older adult population and the associated age-related cognitive decline highlight the urgent need for accessible and non-invasive interventions to support healthy aging and preserve quality of life. Brain-Computer Interfaces (BCI), implementing neurofeedback protocols, offer a promising approach by enabling users to self-regulate their brain activity through real-time feedback, which can help counteract brain changes linked to aging. Among various neurofeedback strategies, motor imagery (MI)-based protocols have demonstrated encouraging effects on intellectual and memory functions in older adults. This study presents the design and ongoing proof-of-concept evaluation of an electroencephalography (EEG)-based neurofeedback prototype specifically tailored for older users. The system leverages recent advancements in EEG signal processing, classification, and MI paradigms to deliver kinesthetic MI training focused on left- and right-hand MI. Real-time visual feedback, based on sensorimotor rhythm modulation detected from EEG signals, is provided through an ecological and user-friendly interface. A water-based EEG cap is employed to facilitate ease of use and comfort, increasing the system’s suitability for deployment beyond laboratory settings and for repeated training sessions. The ongoing proof-of-concept study aims to assess the technical feasibility, usability, and overall coherence of the training workflow in a controlled environment with healthy older adults. Preliminary results support the system’s potential as an accessible BCI solution for cognitive training in aging populations, paving the way for future clinical trials focused on mitigating cognitive decline.

The rising incidence of neurodegenerative diseases such as Amyotrophic Lateral Sclerosis (ALS), Multiple Sclerosis (MS), and Parkinson’s disease, driven by global population aging, highlights the urgent need for effective assistive technologies.
To provide personalized support, objective and quantitative methods are required to assess residual motor abilities and standardize selection metrics for assistive solutions.
This work proposes and validates an experimental protocol to collect key performance indicators (KPIs) during activities of daily living (ADLs).
The ultimate goal is to enable a data-driven framework for classifying motor stages and evaluating the effectiveness of wearable robotic devices in supporting upper limb function.
The protocol was tested and validated on five healthy young individuals and two ALS patients over 65 using a passive upper limb exoskeleton, with EMG and IMU data showing reduced muscular effort in healthy subjects and stage-related benefits in ALS patients, thus supporting its validity and guiding further refinements.

In the field of motion assistance and rehabilitation robotics, accurately understanding and interpreting a user's muscle activity is essential for optimizing therapeutic outcomes. Conventional methods, such as Electromyography (EMG), though widely used, often suffer from signal noise, ambiguity, and dependency on detailed knowledge of the neuromuscular system. To address these limitations, this work presents a novel Virtual Sensing framework that integrates multibody (MB) modeling of the human upper-limb musculoskeletal system with an Extended Kalman Filter (EKF) for indirect estimation of muscle forces during robotic-assisted motion. The proposed methodology models the upper limb as a system of rigid bone segments characterized by Cartesian coordinates and Euler parameters. This setup leads to the formulation of a set of Differential Algebraic Equations (DAE) that describe system dynamics. To simplify the modeling process, muscles are represented using idealized rope-like structures, significantly reducing the number of parameters required. The EKF is employed to estimate both joint states and muscle input forces, allowing real-time force estimation without reliance on EMG. The framework was evaluated using a simulated rehabilitation exercise in the OpenSim platform. Results demonstrate accurate muscle force estimation, validating the effectiveness of the proposed method. A key advantage lies in the absence of assumptions regarding muscle-tendon force elements, streamlining the parameter identification process. Though improvements are still possible, this Virtual Sensing approach shows strong potential to enhance robotic control, therapy progress tracking, and clinical outcome evaluation in rehabilitation settings, once confirmed through physical experimentation.

Wearable devices such as smartwatches and bracelets have become essential for workplace safety, fitness tracking, and healthcare monitoring, reducing costs and improving clinical care. A key application is the monitoring of cardiovascular and respiratory diseases using non-invasive techniques like electrocardiogram (ECG) and photoplethysmography (PPG). However, only a few devices have received official accreditation. This study presents a prototype combining ECG and PPG sensors and evaluates its accuracy through metrological characterization.

ECG captures cardiac electrical signals with distinct peaks and intervals (P, QRS, T) important for diagnosing arrhythmias. PPG measures blood volume changes in tissues by detecting light intensity variations, showing systolic and diastolic peaks. The prototype uses the MAX30003 module for single-lead ECG and MAX30102 for PPG, both managed by an ESP32 microcontroller with integrated filters to improve signal quality.

The study involved 39 participants (average age 65±18 years), acquiring 15-second ECG and PPG recordings. A neural network algorithm implemented in MATLAB segmented the vital signals, achieving a 95% detection rate for ECG peaks and 90% for PPG peaks, maintaining high precision even in noisy conditions. The PPG calibration showed a strong correlation (R²=0.997) with certified SpO2 values, with a maximum error of 3.7%.

This integrated approach offers promising results for accurate cardio-respiratory monitoring, potentially aiding early detection and management of related pathologies.