Conference Agenda

Excessive noise in Neonatal Intensive Care Units (NICUs), mainly caused by clinical alarms, poses a risk to the neurological and auditory development of newborns and contributes to fatigue among healthcare personnel. This paper presents the design and preliminary validation of a wireless vibrotactile bracelet prototype intended to reduce noise exposure by transforming visual alarms into vibrotactile signals directly perceived by staff. The system comprises a transmitter module, based on an ESP32 microcontroller and a TCS3200 color sensor for alarm detection, and a receiver module in the bracelet, which generates vibrations via haptic motors. Experimental validation at the Honduras Medical Center NICU demonstrated a reduction of up to 21 dBA in equivalent sound pressure levels (Leq), highlighting the device’s potential as a complementary tool for neonatal safety and clinical ergonomics.

This study presents a non-invasive electrical approach for the real-time characterization of human dermal fibroblast (HDFa) monolayers using a capacitive sensor array combined with phasor analysis. Voltage and current signals were acquired under low-intensity alternating excitation, enabling the estimation of the effective capacitance of the culture system across a controlled frequency sweep. The results revealed frequency-dependent capacitance variations that differentiate the electrical behavior of the biological culture from that of a passive dielectric substrate. A first-order phenomenological model was introduced to interpret these variations in terms of macroscopic dielectric properties. Although no direct biological assays were performed, the proposed metric should be understood as an electrical indicator sensitive to dielectric changes rather than as a direct measurement of cell viability. These findings highlight the potential of capacitance-based phasorial analysis as a complementary strategy for non-invasive electrical monitoring of in vitro cell culture systems.

Colorectal cancer (CRC) remains a leading cause of cancer-related mortality worldwide, underscoring the urgent need for non-invasive and cost-effective screening strategies. This study evaluates the feasibility of using the Cyranose 320 electronic nose to discriminate between CRC patients and healthy controls through volatile organic compound (VOC) analysis of sweat samples. A total of 65 sweat samples (31 CRC, 34 controls) were analyzed. The data processing pipeline included Relative Difference (RD) feature extraction, Quantile Transformer scaling, Orthogonal Signal Correction (OSC), and Principal Component Analysis (PCA), followed by supervised machine learning classification. PCA revealed strong class separability, with the first three principal components explaining 95.72% of the total variance (PC1: 91.28%). Supervised classification using nested cross-validation demonstrated robust performance across seven algorithms. Random Forest achieved the best results, with 95.4% accuracy, 93.5% sensitivity, 97.1% specificity, and an AUC of 0.967. Decision Tree showed comparable performance, while all evaluated models exceeded an AUC of 0.90. Confusion matrix analysis confirmed high true positive rates and minimal false positives, particularly for tree-based ensemble methods. These findings demonstrate that sweat-derived VOC profiling using the Cyranose 320, combined with advanced data preprocessing and multivariate analysis, provides strong discriminative capability for CRC detection. The results support the potential of sweat-based electronic nose systems as a non-invasive, scalable, and patient-friendly screening approach, warranting validation in larger independent cohorts.

Population aging represents an increasing challenge for healthcare systems and society as a whole. This phenomenon is particularly relevant in low- and middle-income countries, where access to objective and scalable tools for functional assessment remains limited or nonexistent. Frailty in older adults is commonly assessed through clinical physical performance tests such as the Short Physical Performance Battery (SPPB). However, these evaluations rely on observer-based scoring, which may limit the detection of subtle changes in functional performance.
This study proposes the instrumentation of the SPPB using low-cost inertial measurement units (IMUs) to obtain objective gait and balance metrics. A cross-sectional observational study was conducted with 30 participants (mean age: 72.4 ± 6.8 years). Three portable IMUs were placed at the waist and both ankles during the execution of the SPPB subtests. Data processing included filtering, event-based segmentation, and parameter extraction.
The analyzed metrics included gait speed, cadence, and balance variability, estimated through root mean square (RMS) acceleration along the anteroposterior and mediolateral axes. Additionally, a temporal slope index was incorporated, calculated from the difference between the duration of the last and first repetitions of the chair-stand test and normalized by the number of intervals. The median gait speed was 0.384 m/s (IQR: 0.164–0.495). Participants with poorer performance exhibited lower gait speed and higher cadence, suggesting compensatory strategies.
These results demonstrate the feasibility of the proposed approach for extracting spatiotemporal gait parameters from low-cost IMUs as an objective support tool for frailty assessment in older adults.

Emotional valence refers to the positive or negative quality of affective experience and plays a central role in shaping political perceptions, judgments, and participation. However, its objective measurement in political contexts remains underexplored. This article presents a pilot study to evaluate the feasibility of estimating emotional valence from physiological signals elicited by visual stimuli with political content. Physiological data were collected from participants during exposure to images of Colombian political figures. Photoplethysmography (PPG) and galvanic skin response (GSR) signals were recorded using a non-invasive wearable device (EmotiBit). Several machine learning classifiers were evaluated to classify emotional valence (negative, neutral, and positive). Due to class imbalance, the F1 score was used as the primary evaluation metric. The best-performing model was the decision tree-based Extra Trees classifier, achieving 72.73% accuracy and an F1 score of 0.71 in a 3-class classification task. The results show that physiological signals can be used to measure emotional valence in political contexts, using portable, non-invasive wearable devices.