Treadmills with automatic speed adjustment offer a unique advantage: they can mimic outdoor conditions for specific training or testing protocols. This versatility makes them highly applicable in both sports and rehabilitation settings. This study presents a novel framework based on a feedback control loop system, conceived to control a treadmill belt speed using a non-invasive and low-cost sensor (Microsoft Kinect) to detect the users’ position and avoid object obstruction issues. The speed of the treadmill belt is regulated according to the user’s speed, by means of a proportional-integrative-derivative (PID) controller and a parabolic gain function. By tuning the gain function parameters, the user can customize the response of the treadmill. Position data collected during exercise using the Microsoft Kinect sensor was compared with that collected with a stereophotogrammetric motion capture system, showing promising results in terms of accuracy in position assessment. The comparison highlighted a 0.9 degree of correlation between the two systems during the running and cross-country indoor skiing tests performed. In addition, upon considering the relationship between the differences and averages of the two measures, no systematic bias was identified. The system proved to be functional for running and cross-country skiing, and it can therefore re-create similar typical characteristics of outdoor environments (e.g., speed and slope) thanks to the non-invasive user’s position detection and the customizable gain function.

Flexible pressure sensors have been increasingly proposed for clinical monitoring applications. However, the available evidence on the technical characteristics and the biomedical applications of these technologies remains fragmented. To fill this gap, this scoping review aimed to map the available literature (i) to identify the existing flexible pressure sensor matrices proposed for biomedical applications, their technical characteristics, and usage contexts, and (ii) to determine the systems integrated into bed-based support surfaces for clinical monitoring functions. The scoping review was conducted following the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews. PubMed, Scopus, and Web of Science databases were systematically searched to identify studies published between 2015 and 2025 that describe flexible pressure sensor matrices for biomedical monitoring and care applications. A total of 5021 records were screened, and 45 studies were included. Existing flexible pressure sensor matrices were mainly based on resistive and capacitive principles. Systems integrated into clinical support surfaces were primarily used for pressure distribution and posture monitoring, and spanned from experimental prototypes to commercially available technologies. A lack of technical specifications and relevant heterogeneity was observed among the studies. Flexible pressure sensors demonstrated potential for clinical monitoring, but standardized technological reporting and clinical validation protocols are needed to develop technically robust and clinically oriented pressure sensing solutions.

Objective: Stress recognition is a widely investigated and debated area in biomedical research. Physiological monitoring has gained increasing attention as one of the methodologies used to assess an individual’s stress level. In this study, we investigated the effectiveness of a novel normalization technique applied to multi-domain physiological data for the objective classification of stress levels using a feature extraction approach. Methods: Electrocardiographic (ECG) and respiratory data from a publicly available database, collected from drivers experiencing various stress levels, underwent a novel inter-subject normalization procedure. This method involved adjusting the time scale of the original data to a common scale across subjects according to fixed resting heart and respiratory rates. Subsequently, a feature-based stress state classification procedure was conducted using the Support Vector Machine (SVM) algorithm. The efficacy of this inter-subject normalization procedure was assessed by comparing the classification results obtained using features from the original signals with those obtained from the inter-subject-normalized signals. Additionally, the inter-subject normalization procedure was compared with two common feature normalization approaches: standardization and scaling. Results: Features derived from the subject-normalized signals yielded improved performance, significantly enhancing accuracy from 68% to 73%, as well as precision and sensitivity. Conclusions: The novel inter-subject normalization procedure proves to be an effective technique for highlighting differences in features among various stress states and for mitigating basal physiological variability across subjects. Significance: Using inter-subject normalization on multi-domain physiological signals holds promise as a method to improve multilevel stress classification through feature extraction, ensuring that the features maintain their correspondence even after the normalization process.

Objective: Stress recognition is a widely investigated and debated area in biomedical research. Physiological monitoring has gained increasing attention as one of the methodologies used to assess an individual's stress level. In this study, we investigated the effectiveness of a novel normalization technique applied to multi-domain physiological data for the objective classification of stress levels using a feature extraction approach. Methods: Electrocardiographic (ECG) and respiratory data from a publicly available database, collected from drivers experiencing various stress levels, underwent a novel inter-subject normalization procedure. This method involved adjusting the time scale of the original data to a common scale across subjects according to fixed resting heart and respiratory rates. Subsequently, a feature-based stress state classification procedure was conducted using the Support Vector Machine (SVM) algorithm. The efficacy of this inter-subject normalization procedure was assessed by comparing the classification results obtained using features from the original signals with those obtained from the inter-subject normalized signals. Additionally, the inter-subject normalization procedure was compared with two common feature normalization approaches: standardization and scaling. Results: Features derived from the subject-normalized signals yielded improved performance, significantly enhancing accuracy from 68% to 73% as well as precision and sensitivity. Conclusion: The novel inter-subject normalization procedure proves to be an effective technique for highlighting differences in features among various stress states and for mitigating basal physiological variability across subjects. Significance: Using inter-subject normalization on multi-domain physiological signals holds promise as a method to improve multilevel stress classification through feature extraction, ensuring that the features maintain their correspondence even after the normalization process.