This paper presents a smart fiber Bragg grating (FBG) sensor system with an unobtrusive and easy-to-use FBG sensor bed, which automatically monitors the behavior of bedridden patients and their vital signs based on indicative spatio-temporal signature for adaptive intervention triggering and activity planning. We present the subtle design, fabrication, calibration, implementation and deployment issues of the FBG pressure sensors to be used in hospitals or nursing homes to prevent bedsore generation, patient falling out of the bed, and lifethreatening situations such as patient's heart rate weakening, breathing pattern change, etc. Through trials conducted in the laboratory for respiratory rate monitoring with a sample group of 10 subjects, the system showed maximum error of AE 1 breaths per minute as compared to manual counting.

No abstract

The objective was to investigate sensor measurement uncertainty for intracerebral probes inserted during neurosurgery and remaining in situ during neurocritical care. This describes a prospective observational study of two sensor types and including performance of the complete sensor-bedside monitoring and readout system. Sensors from 16 patients with severe traumatic brain injury (TBI) were obtained at the time of removal from the brain. When tested, 40% of sensors achieved the manufacturer temperature specification of 0.1 °C. Pressure sensors calibration differed from the manufacturers at all test pressures in 8/20 sensors. The largest pressure measurement error was in the intraparenchymal triple sensor. Measurement uncertainty is not influenced by duration in situ. User experiences reveal problems with sensor 'handling', alarms and firmware. Rigorous investigation of the performance of intracerebral sensors in the laboratory and at the bedside has established measurement uncertainty in the 'real world' setting of neurocritical care.

No abstract

Due to the decline in physical and cognitive abilities, many frail elderly may have to lie in the bed most of their time. It is not feasible to monitor them continuously through manual observations alone. This issue can be resolved by embedding a set of multimodal sensors into the bed and providing automated activity recognition intelligence. But it is important to design and develop such multimodal sensing intelligence system desirable to the demands made by the clinicians. This paper presents the comparison and evaluation of different sensing bed configurations to observe different granularities of patient's contexts and activities in and around the bed. Based on the achievements and lessons learned from the experimental analysis, we propose improved sensing bed hardware and software systems to meet the real needs of in and around the bed patient monitoring.