Unobtrusive Sensors For Patient Monitoring

By Durgalakshmi.S

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Researchers at University of South Australia have developed a Multimode Optic Fiber (MMF) sensor to monitor vitals of patients.

Tiny optical fiber sensors attached to the upper surface of a hospital mattress to detect movement. Image Credits: University of South Australia.

The sensors are embedded in the upper portion of the patient’s mattress. This reduces the chance of bedsore, prevents falling, monitors patient’s heart rates and also reduces the need for manual monitoring.

The smart bed sensor is combined with seven MMF cables, bundled at the input and output locations of the sensor. The sensor is then integrated with a single laser light source and a camera through the mounted ends. A visible single light of wavelength 633 nm is generated from the laser source, which is collimated to yield a beam of 3.4mm diameter approximately. This beam is then directed to the end face of the MMF bundle which distributes the beam equally across all seven fibers. The other end of the bundle is imaged onto a CCD camera.

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The coherent light generates a speckle output while propagating. Any change in the fiber such as strain, change in the length of MMF modes and bleeding causes change in the output. The change in the patterns are quantified with an algorithm to identify the physical perturbation.

“Respiration rates are often the first sign that a patient is deteriorating. This normally requires devices to be attached to the patient, either on the chest, as a mask on the face, or ventilator. These can be restrictive and sometimes inappropriate in an aged care setting,” Warren-Smith, a researcher said. “Monitoring vital signs continuously, unobtrusively, and cheaply via the mattress-embedded sensors is a far better solution for both patient and nurse.”

The smart bed sensor makes a difference as it does not require any additional sensor elements to function. Also the sensitivity and spatial radiation of the sensor might be increased without any additional cost. It can be achieved through multiplexing and machine learning algorithms.


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