According to current trends in healthcare sensing technologies, we describe a textile-based pressure sensing matrix that can be integrated in the mattress of a smart bed to characterize sleeping posture/movement of a subject and to extract breathing activity. The pressure mapping layer is developed as a matrix of 195 piezoresistive sensors, it is entirely made of textile materials, and it is the basic component of a smart bed that can perform sleep analysis, can extract physiological parameters, and can detect environmental data related to subject’s health. In this paper, we show the principle of the pressure mapping layer and the architecture of the dedicated electronic system that we developed for signal acquisition. In addition, we describe the algorithms for posture/movement classification (dedicated artificial neural network) and for extraction of the breathing rate (frequency domain analysis). We also perform validation of the system to quantify the accuracy/precision of the posture classification and the statistical analysis to compare our breathing rate estimation with the gold standard.

Breast cancer is the most commonly diagnosed cancer in women worldwide. Although targeted screening programs using mammography have facilitated earlier detection and improved treatment has resulted in a significant reduction in mortality, some negative aspects related to cost, the availability of trained staff, the duration of the procedure, and its non-generalizability to all women must be taken into consideration. Breast palpation is a simple non-invasive procedure that can be performed by lay individuals for detecting possible malignant nodules in the breast. It is a simple test, based on the haptic perception of different stiffness between healthy and abnormal tissues. According to a survey we carried out, despite being safe and simple, breast self-examination is not carried by women because they are not confident of their ability to detect a lump. In this study, a non-invasive wearable device designed to mimic the process of breast self-examination using pressure sensing textiles and thus increase the confidence and self-awareness of women is proposed. Combined with other screening methods, the device can increase the odds of early detection for better prognosis. Here, we present the physical implementation of the device and a finite element analysis of the mechanics underlying its working principle. Characterization of the device using models of large and medium breast phantoms with rigid inclusions demonstrates that it can detect nodules in much the same way as does the human hand during breast self-examination.

We describe the development and preliminary evaluation of an innovative low-cost wearable device for gait analysis. We have developed a sensorized sock equipped with 32 piezoresistive textile-based sensors integrated in the heel and metatarsal areas for the detection of signals associated with the contact pressures generated during walking phases. To build the sock, we applied a sensing patch on a commercially available sock. The sensing patch is a stretchable circuit based on the resistive matrix method, in which conductive stripes, based on conductive inks, are coupled with piezoresistive fabrics to form sensing elements. In our sensorized sock, we introduced many relevant improvements to overcome the limitations of the classical resistive matrix method. We preliminary evaluated the sensorized sock on five healthy subjects by performing a total of 80 walking tasks at different speeds for a known distance. Comparison of step count and step-to-step frequency versus reference measurements showed a high correlation between the estimated measure and the real one.

Extracting sleep-related parameters is essential for assessing health and detecting sleep disorders. However, traditional methods are invasive, costly, and impractical for daily use. To address these limitations, we propose the smart mattress cover system (SMCS), a noninvasive and scalable solution for real-world sleep monitoring. The SMCS includes two components: the smart mattress cover (SMC), a compact low-density textile-based pressure matrix (PM) with embedded accelerometers (ACCs), and the bedroom box hub (BBH), which manages data collection and integrates environmental sensors. The SMC acquires key patient-related parameters (PPs) as heart rate (HR), breathing rate (BR), movements, and on-/off-bed status, while the BBH records environmental parameters (EPs). We present the SMCS design, a systematic requirement analysis, and custom algorithms to extract PP. Results showed a low HR RMSE of 2.02 bpm and a good BR RMSE of 4.29 act/min across sleep positions and respiratory frequencies. On-/off-bed detection and movement classification algorithm achieved an overall accuracy of 98.4%. Algorithms were validated across multiple SMCS prototypes, bed types, positions, and breathing patterns, confirming robustness. The SMCS demonstrates scalability for home use, with over 80 units deployed in the TOLIFE project. To the best of our knowledge, the SMCS is the most compact low-density textile-based system capable of extracting all core PP simultaneously.