Multimode optical fiber specklegram smart bed sensor array

Significance: Monitoring the movement and vital signs of patients in hospitals and other healthcare environments is a significant burden on healthcare staff. Early warning systems using smart bed sensors hold promise to relieve this burden and improve patient outcomes.We propose a scalable and cost-effective optical fiber sensor array that can be embedded into a mattress to detect movement, both sensitively and spatially. Aim: Proof-of-concept demonstration that a multimode optical fiber (MMF) specklegram sensor array can be used to detect and image movement on a bed. Approach: Seven MMFs are attached to the upper surface of a mattress such that they cross in a 3 × 4 array. The specklegram output is monitored using a single laser and single camera and movement on the fibers is monitored by calculating a rolling zero-normalized cross-correlation. A 3 × 4 image is formed by comparing the signal at each crossing point between two fibers. Results: The MMF sensor array can detect and image movement on a bed, including getting on and off the bed, rolling on the bed, and breathing. Conclusions: The sensor array shows a high sensitivity to movement, which can be used for monitoring physiological parameters and patient movement for potential applications in healthcare settings.Stephen C. Warren-Smith, Adam D. Kilpatrick, Kabish Wisal, and Linh V. Nguye

Instalación domótica basada en OPENHAB Y RASPBERRY PI

El presente trabajo pretende aportar una solución tecnológica viable al cuidado de una población anciana cada vez más numerosa, integrando para ello un sensor que ha sido específicamente diseñado para ser capaz de detectar la ocupación o no de una cama, un sensor de presencia comercial y un controlador central conformado por una Raspberry Pi y OpenHAB como software de control. Todo ello, forma un sistema inalámbrico utilizando un dispositivo RFXCOM que aporta una comunicación efectiva entre los elementos que conforman el sistema y un robot antropomorfo externo.Departamento de Ingeniería de Sistemas y AutomáticaMáster en Ingeniería Industria

Evaluation of Pressure Bed Sensor for Automatic SAHS Screening

We evaluate the performance of an unobtrusive sleep monitoring system in the detection of the sleep apnea-hypopnea syndrome (SAHS). The proposed system is a pressure bed sensor (PBS) that incorporates multiple pressure sensors into a bed mattress to measure several physiological signals of the sleeping subject: respiration; heart rate; and body movements. An automatic algorithm is developed to calculate a respiratory event index (REI). The recordings of 24 patients with suspected sleep problems are analyzed, and the results are compared with the gold standard methods; first with manual scoring of polysomnography to calculate the apnea-hypopnea index (AHI), and second with automatic detection of REI from the respiratory inductive plethysmography belts. The correlation coefficient between AHI and REI from PBS is up to 0.93. Evaluating the ability of PBS in the diagnosis of pathologic (AHI ≥ 5) and nonpathologic (AHI <5) subjects, we obtained a sensitivity, specificity, and accuracy of 100%, 92%, and 96%, respectively. To diagnose three levels of SAHS, mild, moderate, and severe, the Cohen's kappa value is 0.76. These findings support that PBS recording could provide a simple and unobtrusive method for detection of SAHS in home monitoring