{"title":"结合10矩阵压力传感器读取与卧位患者有关的人体压力","authors":"H. Pranjoto, Andrew Febrian Miyata, L. Agustine","doi":"10.3390/jsan11010016","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":288992,"journal":{"name":"J. Sens. Actuator Networks","volume":"3 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Combining 10 Matrix Pressure Sensor to Read Human Body's Pressure in Sleeping Position in Relation with Decubitus Patients\",\"authors\":\"H. Pranjoto, Andrew Febrian Miyata, L. Agustine\",\"doi\":\"10.3390/jsan11010016\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"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.\",\"PeriodicalId\":288992,\"journal\":{\"name\":\"J. Sens. Actuator Networks\",\"volume\":\"3 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-02-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"J. Sens. Actuator Networks\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3390/jsan11010016\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"J. Sens. Actuator Networks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3390/jsan11010016","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
摘要
这项工作使用压阻式矩阵压力传感器来绘制人体在睡眠状态下的压力分布图。这项研究旨在检测压力最高的区域,将压力剖面可视化为热图,并通过提醒受试者位置的变化来减少卧位。这项研究结合了十个矩阵压力传感器来读取更大的区域。本工作采用8gb内存的树莓派4 Model B作为数据处理器,每个传感器片采用ATMEGA 2560作为传感器控制器进行数据采集。传感器校准是必要的,因为对于相同的权重值,每个输出必须具有相同的值;不同传感器之间的精度在95%左右。校准过程后,输出数据必须平滑,使视觉表示更容易区分。压力最大的部位是脚后跟、尾骨、背部和头部。当受试者体重增加时,对尾骨和背部的压力增加,但对脚跟和头部的压力没有增加。这项研究的结果可以用来监测人们的睡眠姿势,从而降低患卧姿的风险。
Combining 10 Matrix Pressure Sensor to Read Human Body's Pressure in Sleeping Position in Relation with Decubitus Patients
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.