Conference Agenda

This study combines advanced numerical methodologies with additive manufacturing (AM) to optimize the design and production of orthopedic insoles. The research aims at reducing material usage and production time, while maintaining the mechanical properties required for optimal performance. This is accomplished by merging experimental evidence into computational modelling.

In recent years Additive Manufacturing (AD) has been successfully integrated in the design and production of orthoses, to the benefit of a customizable correction of impaired foot biomechanics. As well known, 3D printed patterns (or “infills”) consist in an alternation of void and full material regions, intrinsically allowing to save on material consumption. In particular, this work analyzes the widely-used honeycomb infill pattern, which, while mechanically robust, is associated with longer printing times. Alternative patterns have been considered to identify a suitable replacement infill that is mechanically comparable but is printed faster.

As a second step, the mentioned material savings have been further optimized in the insole fabrication by distributing the replacement infill only where it is actually needed. To accomplish this, the research as leveraged the potentialities offered by topology optimization, a numerical technique that determines the optimal material distribution within a structure to minimize weight while ensuring structural integrity. Specifically, the optimization process is applied to the frontal region of the insole, where material reduction is achieved while considering mechanical stability under real-world loading conditions. These include bending and deformation caused by the insertion into the shoe.

By addressing both structural and printing optimization, the final insole registers significantly reduced production time and material usage. The proposed design is a trade-off between portability, comfort, and usability, and demonstrates the advantages of integrating advanced computational techniques with additive manufacturing for biomedical applications.

This work is part of the Spoke 2, FP4 (partner UCBM) Rome Technopole project, funded by the E.U. and the Italian Ministry of University and Research.

In recent years, wearable devices for monitoring physiological and biomechanical parameters have attracted growing interest, particularly in the assessment of individuals suffering from low back pain. This condition can influence parameters such as respiratory rate (RR) and heart rate (HR), making their continuous and non-invasive monitoring highly valuable for the implementation of pain mitigation strategies. In this context, Fiber Bragg Grating (FBG) sensors have emerged as particularly promising technology due to their small size, multiplexing capability, and immunity to electromagnetic interference. An innovative solution to overcome the intrinsic fragility of FBGs, while enhancing their strain response, consists of integrating the sensors into components manufactured by 3D printing, an approach that offers cost-effectiveness, reproducibility, and ease of fabrication.

This study proposes the design and fabrication of two wearable sensors (WSs), identical in geometry but differentiated by function, based on FBG sensors encapsulated into components fabricated using dual-extrusion 3D printing with Fused Deposition Modelling (FDM) technology. One WS is designed to be positioned horizontally on the subject’s thoracic cage to monitor cardio-respiratory activity, while the other is intended to be placed vertically in the lumbar region to detect flexion-extension movements of the back. In particular, the cyclic movements induced by respiration and cardiac activity at the thoracic level, as well as lumbar flexion-extension movements, generate periodic strain variations on the WSs, resulting in corresponding shifts in the reflected wavelength of the integrated FBGs. The analysis of such variations enables the extraction of physiological and biomechanical parameters such as RR, HR, and flexion-extension rate. The main innovation introduced in the two WSs lies in the combined use of two materials: thermoplastic polyurethane (TPU), used to fabricate a dog-bone-shaped element integrating a single 10 mm-long FBG at its central portion, and polylactic acid (PLA), employed to fabricate two lateral flanges designed to provide structural rigidity and allow the anchorage of the device to the subject’s body.

Following fabrication, a metrological characterization was carried out on one of the two WSs to evaluate its temperature sensitivity (S_T) and strain sensitivity (S_ε), as well as its dynamic behavior under cyclic loading conditions. The results showed a value of S_T equal to 0.034 nm/°C and a S_ε of 0.81 nm/mε, in line with values reported in the literature for similar sensors. Moreover, the device demonstrated good repeatability and adequate responsiveness to dynamic variations.

Finally, preliminary tests were conducted on eight healthy volunteers to assess the effectiveness of the two WSs in monitoring cardio-respiratory activity and lumbar flexion-extension movements. The acquired data were analyzed using custom algorithms developed in MATLAB. The results showed high accuracy in the estimation of RR and HR, with mean absolute errors lower than 0.5 breaths/min and 2.8 beats/min, and mean absolute percentage errors below 3% for both parameters. Furthermore, the analysis of movement sequences confirmed the system’s ability to effectively discriminate between different execution rates of flexion-extension movements.

Future developments will focus on device miniaturization, the integration of multi-sensor functionalities, and validation on clinical populations suffering from chronic low back pain.

In light of the escalating occurrence and severity of wildfires, this study explores the feasibility of deploying wearable devices to monitor cardiac, respiratory, physical, and environmental parameters during operational wildfire suppression tasks. Wildland firefighting strategies typically include direct attacks—where operators extinguish flames at close range—and indirect tactics such as debris removal and hose support, performed at varying distances from the fire front. While most research in this field is confined to laboratory simulations, the physiological burden experienced by firefighters (FFs) during live fire scenarios remains largely unexplored due to technical and logistical constraints. This study addresses this gap by conducting a comprehensive field-based analysis with two teams of six FFs from the Italian National Fire Corps, involved in a structured and controlled fire simulation protocol.

The protocol, designed to emulate real firefighting conditions, included six consecutive phases: baseline rest (Stop 1), two bouts of running (Run 1 and Run 2), intermediate rest (Stop 2), active fire suppression (Fire), and final recovery (Stop 3). FFs were equipped with commercial wearable chest straps capable of capturing ECG, respiratory signals, and accelerometry, as well as external temperature loggers. These devices allowed for high-frequency recording of physiological metrics including heart rate (HR), heart rate variability (HRV), respiratory frequency (fR), and vector magnitude units (VMU) of physical activity. Data were processed with filtering and artifact-removal algorithms applied to ensure accuracy.

Results revealed intense cardiovascular and respiratory strain during active phases. HR often exceeded 85% of the age-predicted maximum in many subjects, surpassing this threshold by over 40%, reflecting considerable physiological stress. HR recovery (HRR) after intense bouts was heterogeneous across subjects; while some FFs showed rapid decreases in HR (HRR >12 beats per minute (bpm) within 60 s, indicating good recovery), others—especially those in debris management roles—showed sustained elevations, hinting at incomplete recovery. HRV decreased significantly in post-exertion phases, suggesting elevated sympathetic activity and limited parasympathetic reactivation.

Respiratory analysis, performed using a power spectral density (PSD) approach to counter motion artifacts, highlighted consistent episodes of mild tachypnea (fR > 20 respirations per minute (rpm)) after active fire suppression, particularly in high-temperature and high-exertion roles. A general return to eupnea (12–20 rpm) was observed during final recovery, indicating prolonged respiratory activation.

Moreover, the physical activity load, quantified via VMU, confirmed role-dependent intensity. Debris management roles showed continuous high-intensity physical engagement (VMU > 0.8 g for more than 40% of the fire phase), while hose handlers and support personnel remained predominantly in moderate to low ranges.

These preliminary findings confirm the reliability of integrating wearable technologies to monitor the physiological and physical strain associated with wildfire suppression. Despite minor signal disruptions during high-exertion tasks, the system proved robust under harsh conditions, capturing critical physiological and physical metrics. These results highlight the importance of role-specific monitoring, as task intensity and physiological demands vary considerably. By identifying key stress indicators, this approach provides a solid foundation for optimizing training protocols, refining work-rest strategies, and developing tailored interventions to enhance FFs’ safety and operational performance.