This research presents a machine learning modeling process for detecting mental fatigue using three physiological signals: electrodermal activity, electrocardiogram, and respiration. It follows the conventional machine learning modeling pipeline, while emphasizing the significant contribution of the feature selection process, resulting in, not only a high-performance model, but also a relevant one. The employed feature selection process considers both statistical and contextual aspects of feature relevance. Statistical relevance was assessed through variance and correlation analyses between independent features and the dependent variable (fatigue state). A contextual analysis was based on insights derived from the experimental design and feature characteristics. Additionally, feature sequencing and set conversion techniques were employed to incorporate the temporal aspects of physiological signals into the training of machine learning models based on random forest, decision tree, support vector machine, k-nearest neighbors, and gradient boosting. An evaluation was conducted using a dataset acquired from a wearable electronic system (in third-party research) with physiological data from three subjects undergoing a series of tests and fatigue stages. A total of 18 tests were performed by the 3 subjects in 3 mental fatigue states. Fatigue assessment was based on subjective measures and reaction time tests, and fatigue induction was performed through mental arithmetic operations. The results showed the highest performance when using random forest, achieving an average accuracy and F1-score of 96% in classifying three levels of mental fatigue.

The skin, the outermost organ of the human body, is vital for sensing and responding to stimuli through mechanotransduction. It is constantly exposed to mechanical stress. Consequently, various mechanical therapies, including compression, massage, and microneedling, have become routine practices for skin healing and regeneration. However, these traditional methods require direct skin contact, restricting their applicability. To address this constraint, we developed shear wave stimulation (SWS), a contactless mechanical stimulation technique. The effectiveness of SWS was compared with that of a commercial compression bioreactor used on reconstructed skin at various stages of maturity. Despite the distinct stimulus conditions applied by the two methods, SWS yielded remarkable outcomes, similar to the effects of the compression bioreactor. It significantly increased the shear modulus of tissue-engineered skin, heightened the density of collagen and elastin fibers, and resulted in an augmentation of fibroblasts in terms of their number and length. Notably, SWS exhibited diverse effects in the low- and high-frequency modes, highlighting the importance of fine-tuning the stimulus intensity. These results unequivocally demonstrated the capability of SWS to enhance the mechanical functions of the skin in vitro, making it a promising option for addressing wound healing and stretch mark recovery.

Infrared thermography is a non-invasive measurement method that can accurately describe immediate temperature changes of an object. In the case of continuous in vivo hand measurements, extracting correct thermal data requires a first step of image segmentation to identify regions of interest. This step can be difficult due to parasitic hand movements. It is therefore necessary to regularly readjust the segmented areas throughout the recording. This process is time-consuming and presents a particular obstacle to studying a large number of areas of the hand and long duration sequences. In this work, we propose an automated segmentation methodology that can automatically detect these regions on the hand. This method differs from previous literature because it uses a secondary visual camera and a combination of computer vision and machine learning feature identification. The obtained segmentation models were compared to models segmented by two human operators via Dice and Intersection-over-Union coefficients. The results obtained are very positive: we were able to decompose the images acquired via IRT with our developed algorithms, regardless of the temperature variation, and this with processing times of less than a second. Thus, this technology can be used to study the long-term thermal kinetics of the human hand by automatic feature detection, even in situations where the hand temperature experiences a significant variation.

Exposure to mechanical stimuli such as pressure and stretching prompts the skin to undergo physiological adaptations to accommodate and distribute applied forces, a process known as mechanotransduction. Mechanotherapy, which leverages mechanotransduction, shows significant promise across various medical disciplines. Traditional methods, such as massage and compression therapy, effectively promote skin healing by utilizing this mechanism, although they require direct skin contact. This study introduces a novel contactless modality, Shear Wave Stimulation (SWS), and evaluates its efficacy compared to traditional massage in eliciting responses from human skin and fascia. Fifteen healthy volunteers received SWS, while another fifteen volunteers received massage. Tests of skin mechanical properties revealed significant enhancements in skin shear modulus for both methods, showing an increase of approximately 20%. Additionally, deformation analysis of ultrasound images showed distinct responses of the skin and fascia to the two stimuli. SWS induced extension in the dermis (∼18%), hypodermis (∼16%), and fascia (∼22%) along the X and Y axes. In contrast, massage compressed the skin layers, reducing the dermis by around 15% and the hypodermis by about 8%, while simultaneously stretching the superficial fascia by approximately 8%. The observed extension across the entire skin with SWS highlights its potential as a groundbreaking contactless approach for promoting skin healing. Furthermore, the differing responses in blood flow reaffirm the distinct stimulation modes of SWS and massage. These findings establish a foundation for future innovative skin therapy modalities.