Graphical Pressure Mapping of a 2288 Sensing-Point Matrix Pressure Sensor Using Raspberry Pi

This work uses piezoresistive matrix pressure sensors to map the human body’s pressure profile in a sleeping position. This study aims to detect the area with the highest pressure, to visualize the pressure profile into a heatmap, and to reduce decubitus by alerting the subject to changes in position. This research combines ten matrix pressure sensors to read a larger area. This work uses a Raspberry Pi 4 Model B with 8 GB memory as the data processor, and every sensor sheet uses ATMEGA 2560 as the sensor controller for data acquisition. Sensor calibration is necessary because each output must have the same value for the same weight value; the accuracy between different sensors is around 95%. After the calibration process, the output data must be smoothed to make visual representations more distinguishable. The areas with the highest pressure are the heel, tailbone, back, and head. When the subject’s weight increases, pressure on the tailbone and back increases, but that on the heel and head does not. The results of this research can be used to monitor people’s sleeping positions so that they can reduce the risk of decubitus.

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Graphical Pressure Mapping of a 2288 Sensing-Point Matrix Pressure Sensor Using Raspberry Pi

Cited by 3 publications

References 22 publications

Raspberry Pi-based Decubitus Reducing Mattress with Air Pressure Monitoring System and Air Leaks Detector

Raspberry Pi-based Decubitus Reducing Mattress with Air Pressure Monitoring System and Air Leaks Detector

A New Design Method for Pressure Mapping

Combining 10 Matrix Pressure Sensor to Read Human Body’s Pressure in Sleeping Position in Relation with Decubitus Patients

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