Mental health is influenced by the fast-paced nature of life. In this scenario, stressful events play an important role. Extensive research has been carried out to develop non-invasive devices for stress detection, which primarily use physiological data and, more recently, artificial intelligence algorithms. When developing either a new device or algorithm, tests in controlled environments are preferred, because of the supervision of possible confounding factors while running the experiments. However, because of the extremely subjective perception of stress, the characteristics of the investigated samples, and the conditions under which the experiment is conducted, the data may not be representative of a perceived stressful condition, leading to biases. Given the importance of reliable experimental protocols for stress induction, especially if cortisol level is not monitored, this work aims to present approaches for inducing and assessing acute mental stress in controlled conditions, analyzing the problem from engineering and psychological perspectives. All the phases of the experimental protocol are discussed, examining both the factors that could induce stress and the assessment tools, like questionnaires and physiological signals. The analysis of the latter will be focused on the exogenous factors that may compromise the measures, providing solutions for their mitigation. With this work, researchers with different backgrounds can improve the efficacy of their studies, limiting biases and misleading results.

Sustained attention is pivotal for tasks like studying and working for which focus and low distractions are necessary for peak productivity. This study explores the effectiveness of adaptive transcranial direct current stimulation (tDCS) in either the frontal or parietal region to enhance sustained attention. The research involved ten healthy university students performing the Continuous Performance Task-AX (AX-CPT) while receiving either frontal or parietal tDCS. The study comprised three phases. First, we acquired the electroencephalography (EEG) signal to identify the most suitable metrics related to attention states. Among different spectral and complexity metrics computed on 3 s epochs of EEG, the Fuzzy Entropy and Multiscale Sample Entropy Index of frontal channels were selected. Secondly, we assessed how tDCS at a fixed 1.0 mA current affects attentional performance. Finally, a real-time experiment involving continuous metric monitoring allowed personalized dynamic optimization of the current amplitude and stimulation site (frontal or parietal). The findings reveal statistically significant improvements in mean accuracy (94.04 vs. 90.82%) and reaction times (262.93 vs. 302.03 ms) with the adaptive tDCS compared to a non-stimulation condition. Average reaction times were statistically shorter during adaptive stimulation compared to a fixed current amplitude condition (262.93 vs. 283.56 ms), while mean accuracy stayed similar (94.04 vs. 93.36%, improvement not statistically significant). Despite the limited number of subjects, this work points out the promising potential of adaptive tDCS as a tailored treatment for enhancing sustained attention.

Objective: Crosstalk in surface electromyogram (EMG) is an important open problem and the common strategy of reducing it through spatial filters needs improvements. Methods: We evaluated experimentally the optimal spatio-temporal filter (OSTF), i.e., a recent approach that adapts to the subject, filtering different EMG channels both in time and space to emphasize the signal of a target muscle discarding that of adjacent ones. EMGs were recorded by a high-density recording system from pronator teres (target muscle) and flexor carpi radialis (crosstalk muscle) of 8 healthy subjects. OSTF was tested in different conditions, considering one channel per muscle (either single or double differential, SD and DD, respectively), changing the selectivity of detection (small electrodes close to each other, or large ones with higher inter-electrode distance), the force applied by the muscles (whose EMGs were summed to simulate different levels of crosstalk), and the duration of the signal to train the method. Results: OSTF was less affected by crosstalk than SD and DD filters. Statistically significant improvements were obtained in reducing the crosstalk-induced variations: for example, considering small electrodes, we obtained a percentage error of 157.30±57.11 % and 38.54±10.47 % (mean±std) in the estimation of the average rectified value (ARV), and an error of 23.57±3.92 % and 8.31±0.88 % in the estimation of the median frequency (MDF), for SD and OSTF, respectively. Conclusion: The OSTF can be applied in real-time, is easy to use, and is feasible even when using only few detection channels, as is customary in many applications.

Wearable systems are increasingly adopted for health monitoring and wellness promotion. Among the most relevant biosignals, the electrocardiogram (ECG) plays a key role; however, in wearable settings (e.g., during physical activity), it is often corrupted by electromyogram (EMG) interference. This study presents a novel adaptive algorithm, template masking (TM), which integrates the stationary wavelet transform (SWT) with template matching for denoising the ECG from EMG. The method identifies the optimal wavelet and decomposition level to maximise detail sparsity. To mitigate EMG interference, after alignment in the SWT domain with a template, the detail coefficients are multiplied by a binary mask and smoothed. TM was compared with soft and hard thresholding on (1) simulations combining clinical ECGs (MIT-BIH database) and synthetic EMGs with different signal-to-noise ratios (SNRs), and (2) experimental signals including ECGs acquired with dry electrodes corrupted by EMGs (SimEMG database, also varying SNRs), as a potential wearable scenario. In both cases, TM yielded significantly lower reconstruction errors at SNRs below 5 dB ($p<0.01$) and significantly outperformed thresholding in the sensitivity of R-peaks estimation ($p<0.001$). These results demonstrate the potential of TM, highlighting the value of adaptive denoising algorithms.

Stress significantly impacts our society, making strategies for its mitigation necessary. A possible approach may involve binaural beats (BBs), i.e., an auditory stimulation obtained by presenting pure tones with slightly different frequencies to the user’s ears, resulting in a third phantom beat $\left(f_{BB}\right)$. While studies in the literature investigate the effects of BBs at a constant stimulation frequency, with this pilot study, we present an innovative approach that adapts the beat frequency in real time within the theta range (4.0–8.0 Hz) to reduce acute mental stress. A stress index, obtained from the predictions of a random forest regressor, was considered to adjust the stimulation. The regressor considered features from an electrocardiogram (ECG) and the ECG-derived respiratory signal. Thirteen healthy subjects underwent a stressful protocol involving multiple mental arithmetic tasks during which constant (CBB) or adapted (ABB) stimulation occurred. Task performances like accuracy and reaction times were recorded. The results show that ABBs significantly lowered the average stress index ($p<0.05$) and heart rate ($p<0.05$) compared to CBBs. No statistically significant differences were detected in task performance. The results support the importance of adaptive and personalized approaches for mitigating stress. Future research is necessary to assess the goodness of our proposal, considering a larger sample, different stressors, and an objective and external assessment of stress (e.g., cortisol levels).

Aim: Accurate assessment of human hydration status is crucial for maintaining physiological homeostasis and optimising performance. Traditional assessment methods require invasive blood sampling or time-consuming laboratory procedures for urine analysis. This study explores whether intermittent inferior vena cava (IVC) ultrasound (US) measurements respond to exercise-associated body mass loss and subsequent water intake. Methods: A cohort of 33 healthy young adults (17 females, 16 males) underwent a controlled experimental protocol comprising moderate-intensity exercise to induce dehydration, followed by a rehydration phase. Longitudinal US scans of the IVC were acquired at 10-min intervals during dehydration (five times) and at 2-min intervals after rehydration (three times). Quantitative physiological parameters, IVC diameter (D), caval index (CI), cardiac caval index (CCI), and respiratory caval index (RCI), were extracted from US data. Body mass was recorded at each dehydration sub-phase, with equivalent water volumes administered during rehydration to match mass loss. Results: At the group level, IVC diameter was lower after 10 min of exercise than at baseline and higher 4 min after water intake than at the end of exercise (both p < 0.01). Moreover, a Random Forest (RF) classifier achieved 76% leave-one-out accuracy in categorising subjects according to rehydration rate, utilising biometric data and IVC features obtained at the 10-min dehydration checkpoint. Conclusion: This investigation demonstrates that IVC US imaging offers a promising, non-invasive methodology for rapid hydration assessment. If validated in an extended cohort, it could have significant implications for personalised monitoring in sports medicine.