Leader: Alessio Tamburrano (Sapienza); Other collaborator(s): Maggi, Noale (CNR), Ferrarese, Bellelli, Franchi (UNIMIB), Pilotto, Brattico, De Luca, Gesualdo, Solfrizzi (UNIBA), RTD-A UNIBA
Investigation of new cost-effective solutions based on novel non-invasive highly sensitive physical sensors integrated on textiles and porous structures by the deposition of graphene nanoplatelets based inks through screen printing techniques or infiltration methods. Guaranteeing perspiration, washability and comfort the piezoresistive and conducting properties of nanostructured coatings will be exploited, using electromechanical tests and electrical resistance tomography techniques with dedicated machine learning algorithms for strain/pressure patterns acquisition and reconstruction. The idea is to realise a sensor system capable of managing the strain and pressure measurement over large areas, and to be included for example in mattress pads or bedspreads. This system is meant for elderly adults and bedridden people, keeping track of the bed occupancy and improving the fall prevention, the nocturnal sleep-wake rhythms and avoiding bedsores by identifying the bodyweight distribution.
Brief description of the activities and of the intermediate results
The partners of Task 5.4 focused on developing and utilizing new piezoresistive coatings based on graphene-modified paints/inks as pressure sensors, with a particular emphasis on their application in healthcare. These systems aim to measure deformation and pressure on large surfaces, such as mattress covers or blankets, with the ultimate goal of monitoring the elderly, especially during bed rest, to mitigate issues like bedsores and improve fall prevention and nighttime sleep-wake patterns. Three different configuration have been identified.
The most promising configuration will serve as the basis for developing the sensorized mat/bedspread prototype. The partners started by developing a smaller system to explore its potential, and successfully fabricated a small piezoresistive foam mat. This prototype underwent morphological, electrical, and piezoresistive characterization through various experimental investigations, including scanning electron microscopy, voltamperometric measurements, and electromechanical tests. ERT was implemented to identify different deformation scenarios caused by fingertip pressure on the soft mat sensor. Data processing was performed using MATLAB scripts, with algorithms (Support Vector Machine, Artificial Neural Network, K-Nearest Neighbors, and Decision Tree) trained and validated on experimental measurements.
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Building on the experience gained, researchers have nearly completed the development of a larger sensorized surface (80 cm x 50 cm), covered with a screen-printed fitted sheet using piezoresistive graphene-based ink. This continuous piezoresistive coating has been successfully tested, enabling detailed pressure mapping through Electrical Resistance Tomography (ERT) and machine learning (ML) algorithms. The system has demonstrated its capability to monitor pressure and recognize sleeping postures with high accuracy, achieving 96.3% accuracy in detecting compressed regions and 94.4% in posture classification.The next phase will focus on studying the robustness of the data collected from this prototype, ensuring its reliability. To obtain more realistic data, partners are evaluating the use of a neonatal dummy, improving upon the previous tests that used wooden blocks as surrogates for body pressure. Upon completion of this phase, the system will be scaled to a larger configuration, such as a standard bed, to extend its application for monitoring bedridden elderly patients.In parallel, the unit is working on assessing the piezoresistive coefficients of the coatings, which will be used to build a mathematical model for Finite Element Method (FEM) simulations. Moreover, an alternative measurement system based on impedance tomography rather than resistance tomography is also under development. This ongoing work will help optimize the sensor's design by accurately simulating its mechanical and electrical behaviour under different conditions, ultimately improving the performance of future, larger-scale implementations.
The first prototype of a sensorized mattress, measuring 80 x 50 cm, was successfully completed and tested. This work was presented at the IEEE Sensors 2024 international conference held in Kobe, Japan, from October 20–23. Following these results, piezoresistive coefficients were experimentally obtained, enabling the development of a finite element method (FEM) simulation model to replicate the piezoresistive characteristics of a sensorized crib-size mattress. This simulation model also incorporated the application of the electrical resistance tomography (ERT)-inspired methodology. The data generated through these simulations were used as training inputs for a machine learning (ML) algorithm, with the primary objective of achieving precise pressure detection. To simulate real-world conditions, a neonatal dummy weighing approximately 5 kg was used to evaluate the mattress's ability to differentiate between various sleeping positions. Special attention was given to detecting potentially dangerous positions for a baby. The tests were conducted both with and without an additional blanket on the mattress to verify system functionality under different conditions. The ML classification algorithm achieved an accuracy of 91% without a blanket and 86% with the blanket. Additionally, system robustness was assessed by simulating electrode malfunctions, which yielded strong performance with an accuracy of 89%. Furthermore, an alternative measurement system based on impedance tomography, rather than resistance tomography, is under development. This system has been installed and configured on the existing prototype and will be incorporated into the final one. Currently, work is underway to realize the final prototype. Upon completion, the sensing system will be scaled to larger configurations, such as standard-sized beds, to extend its applications for monitoring bedridden elderly patients.