Conference Agenda

It has been recognized in the field of Psychiatry that different disorders might share some features, in terms of symptomatology. The advent of affordable genetic sequencing, and the use of statistical learning on large datasets, revealed that there are strong correlations between certain disorders; and the idea of the general neuropsychiatric risk factor that may increase an individual's vulnerability to developing specific disorders was reinvented and garnered a lot of attention recently.

One of the most striking comorbidities confirmed to have underlying common features are depression and anxiety. Clinicians who tried to elucidate this ‘couple’ sometimes claim that they are difficult to differentiate. Indeed, early-onset anxiety in youth often correlates with an elevated risk of developing depression later in life, and the inverse holds true as well. This overlap presents a significant clinical challenge, as it complicates therapeutic strategies at a time when rates of depression and anxiety are consistently rising in last decade, particularly among younger populations.

From the perspective of Physiological Complexity (pathological decomplexification of electrophysiological signals in human physiology), there is yet another common feature characteristic for this ‘couple’: electroencephalograms (EEG) and electrocardiograms (ECG) recorded in persons diagnosed with depression and/or anxiety are exhibiting characteristic levels of complexity different than in healthy controls (HC). In existing literature on this topic many researchers repeatedly demonstrated that based on fractal and nonlinear measures signals recorded in patients are clearly separable from HC. However, these signal patterns reflect deeper dysregulations; aberrated dynamics of the autonomous nervous system (ANS). Such dysregulation manifests as impaired capacity for emotional and physiological self-regulation, such as the inability to disengage from negative stimuli. The disruption of regulatory feedback mechanisms—particularly the interplay between cardiac activity and respiration—produces measurable physiological outputs that can serve as biomarkers for detection, disease severity monitoring, and assessment of recovery potential. Among consequences of this characteristic aberration of ANS function, might be an increased cardiovascular risk (CVDr). That can be observed in particular in women who hold higher risk of depression, and whose CVDr are often underdiagnosed leading to bleak statistics of majority of women dying after the first adverse cardiac event.

The primary objective of this research is to integrate diverse candidate biomarkers—including ECG-derived complexity measures (via wearable devices), accelerometer-based activity metrics, and validated levels of non-ceruloplasmin copper in serum (also measurable using innovative portable technology)—into a unified fuzzy logic model. This model aims to characterize a general risk factor encompassing both heightened cardiovascular vulnerability and the severity of comorbid depressive and anxious states. Through mathematical modelling, we seek to isolate clinically relevant variables that accurately reflect the individual’s affective state and stage of illness, providing actionable insights for clinicians engaged in diagnosis and personalized treatment planning.

Wearable sensors for pulse wave monitoring have gained significant attention in recent years due to their potential for continuous, non-invasive cardiovascular assessment. Despite advancements in this field, existing technologies such as photoplethysmography (PPG) face notable limitations, including susceptibility to light intensity fluctuations, skin tone variability, and dependency on sensor positioning. This work presents the design, development, and metrological characterization of a novel magnetic-based soft sensor for pulse wave monitoring that addresses these challenges while enabling standardized measurement protocols.

The proposed sensing system consists of a Hall effect sensor and a deformable silicone matrix encapsulating a permanent magnet. Its operating principle relies on detecting magnetic field variations caused by arterial pulsations, which displace the magnet relative to the sensor. Unlike optical approaches, this mechanism directly measures mechanical deformations while simultaneously monitoring contact force during sensor placement, a critical parameter often overlooked in conventional systems.

Metrological characterization showed a mean sensitivity of 0.36 V×mm^-1 across the measurement range, increasing to 0.68 V×mm^-1 within the 4 mm - 5 mm compression displacement range, where the response is nearly linear. In this last range, dynamic testing under simulated heart rate conditions (60 bpm -120 bpm) validated the sensor's robustness during cyclic loading, with hysteresis errors ranging from 5.2% to 7.4% depending on loading rate. The minimum detection limit was established at 0.05 mm, indicating the sensor's ability to detect subtle arterial pulsations.

Feasibility assessment on eight healthy volunteers confirmed the system's capability to detect systolic, diastolic, and dicrotic peaks compared to a reference PPG sensor. Analysis of peak detection accuracy revealed promising performance, with systolic peaks showing only 0.10% false positives and 0.20% false negatives compared to the reference. The dicrotic peak demonstrated similarly strong results (0.08% false positives, 0.26% false negatives), while diastolic peak detection presented the greatest challenge with 2.79% false positives and 0.67% false negatives. Using the detected systolic peaks, beat-to-beat heart rate was calculated and compared with that derived from the reference PPG sensor, achieving a mean absolute error of 0.6 bpm across all subjects.

A key advantage of our sensor is its ability to measure contact force during positioning, enabling standardized signal acquisition and improved reproducibility. Tests evaluating the effect of sensor tightness confirmed that appropriate contact pressure is critical for high-quality pulse wave measurements, with signal degradation observed under loose placement conditions.

The proposed magnetic-based sensor represents a promising alternative to conventional pulse monitoring technologies. It combines high sensitivity, low hysteresis, and contact force measurement capabilities in a compact, wearable design. These features address well-known limitations in pulse wave monitoring, particularly standardization issues in signal acquisition related to sensor positioning and contact pressure variations. Future work will explore applications in diverse populations with different vascular conditions and assess performance during motion.

The increasing digitalization of work models and the widespread adoption of remote and hybrid work have profoundly transformed workers’ habits, leading to greater sedentary behavior and prolonged exposure to video terminals. This evolution has made the adoption of physiological monitoring strategies increasingly urgent to prevent conditions of stress, fatigue, and related pathologies. In this context, an alternative approach involves the development of nearable devices, namely solutions integrated into everyday objects that enable continuous physiological monitoring without compromising user comfort and freedom of movement. Smart systems such as chairs, cushions, and mattresses thus represent a non-invasive alternative to traditional wearable devices.

This work proposes the development of an innovative nearable device based on Fiber Bragg Grating (FBG) technology, integrated into a structure fabricated via 3D printing through Fused Deposition Modelling (FDM) using thermoplastic polyurethane (TPU), and designed for the continuous monitoring of respiratory rate (RR) and heart rate (HR) in video terminal workers. The device consists of a sensing element composed of a circular base (40 mm diameter, 2 mm thickness), on which a dome-shaped extrusion (20 mm diameter, 2 mm height) was realized, specifically designed to optimize the transmission of mechanical deformations generated by the expansion and contraction of the rib cage during the respiratory cycle to the FBG sensor (10 mm length, λ_B = 1533.06 nm, 96.83% reflectivity, acrylate recoating). The fiber integration into the structure was achieved during the printing process through a controlled print-pause procedure, ensuring correct positioning and preserving the optical and mechanical properties of the system.

Following fabrication, the device underwent metrological characterization to evaluate its strain sensitivity (S_ε). Controlled compression tests were performed, simulating thoracic load with up to 5% deformation applied to the dome of the sensing element. Data analysis revealed an S_ε value of 0.028 nm/%. Subsequently, the system was preliminarily validated on two healthy volunteers subjected to controlled breathing protocols (quiet breathing and fast breathing). The signals acquired through an optical interrogator were processed using custom-developed algorithms in the MATLAB environment, enabling the extraction of physiological parameters through spectral analysis.

Experimental results demonstrated high accuracy in estimating RR, with mean absolute errors lower than 0.74 breaths/min and percentage errors below 3.5% under both tested conditions. HR estimation was also satisfactory, although an increase in error was observed under fast breathing conditions, consistent with evidence reported in the literature for nearable devices based on FBG technology.

The developed device represents a promising technological solution for non-invasive and continuous physiological monitoring in work environments. Future developments will include the optimization of the sensing geometry, the integration of multi-sensor architectures, and validation on a larger sample of subjects to consolidate the system’s effectiveness in real operational scenarios.