{"title":"用于远程医疗监控的板载计算蓝牙低能耗无线传感器的电路设计、实现和测试","authors":"Petar Šolic;Riccardo Colella;Giuseppe Grassi;Toni Perković;Carlo Giacomo Leo;Ana Čulić;Vladimir Pleština;Saverio Sabina;Luca Catarinucci","doi":"10.1109/JRFID.2024.3363074","DOIUrl":null,"url":null,"abstract":"The Internet of Things (IoT) framework has transformed sensor data utilization, ushering in a new era of sensors integrated into various aspects of modern environment. A pressing concern in the realm of wearable technology is efficient power management, encompassing low power consumption and reducing battery recharging times. This study introduces an electronic device equipped with a Bluetooth 5.1 Low Energy (BLE) module, capable of detecting, collecting, aggregating and transmitting the Root Sum of Squares Method (RSS) of acceleration readings at consistent time intervals. This multi-frequency wireless controller functions at both sub-1 and 2.4 GHz bandwidths, endorsing the Bluetooth® 5.1 low energy standard and diverse wireless modalities via a Dynamic MultiProtocol Manager (DMM) interface. For demonstration purposes, the BMI160 is has been programmed to internally manage acceleration analyses across three axes, reducing data transmission, and minimizing connection times. This device, integrated with other physiological parameter monitoring systems of an individual/patient, can help correlate any variation in these parameters with the amount of motion. The integration of additional sensors can refine the precision of physiological metric evaluation, broadening the potential applications of such systems in sectors like healthcare and well-being.","PeriodicalId":73291,"journal":{"name":"IEEE journal of radio frequency identification","volume":"8 ","pages":"105-113"},"PeriodicalIF":2.3000,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Circuit Design, Realization, and Test of a Bluetooth Low Energy Wireless Sensor With On-Board Computation for Remote Healthcare Monitoring\",\"authors\":\"Petar Šolic;Riccardo Colella;Giuseppe Grassi;Toni Perković;Carlo Giacomo Leo;Ana Čulić;Vladimir Pleština;Saverio Sabina;Luca Catarinucci\",\"doi\":\"10.1109/JRFID.2024.3363074\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The Internet of Things (IoT) framework has transformed sensor data utilization, ushering in a new era of sensors integrated into various aspects of modern environment. A pressing concern in the realm of wearable technology is efficient power management, encompassing low power consumption and reducing battery recharging times. This study introduces an electronic device equipped with a Bluetooth 5.1 Low Energy (BLE) module, capable of detecting, collecting, aggregating and transmitting the Root Sum of Squares Method (RSS) of acceleration readings at consistent time intervals. This multi-frequency wireless controller functions at both sub-1 and 2.4 GHz bandwidths, endorsing the Bluetooth® 5.1 low energy standard and diverse wireless modalities via a Dynamic MultiProtocol Manager (DMM) interface. For demonstration purposes, the BMI160 is has been programmed to internally manage acceleration analyses across three axes, reducing data transmission, and minimizing connection times. This device, integrated with other physiological parameter monitoring systems of an individual/patient, can help correlate any variation in these parameters with the amount of motion. The integration of additional sensors can refine the precision of physiological metric evaluation, broadening the potential applications of such systems in sectors like healthcare and well-being.\",\"PeriodicalId\":73291,\"journal\":{\"name\":\"IEEE journal of radio frequency identification\",\"volume\":\"8 \",\"pages\":\"105-113\"},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2024-02-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE journal of radio frequency identification\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10423374/\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE journal of radio frequency identification","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10423374/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
Circuit Design, Realization, and Test of a Bluetooth Low Energy Wireless Sensor With On-Board Computation for Remote Healthcare Monitoring
The Internet of Things (IoT) framework has transformed sensor data utilization, ushering in a new era of sensors integrated into various aspects of modern environment. A pressing concern in the realm of wearable technology is efficient power management, encompassing low power consumption and reducing battery recharging times. This study introduces an electronic device equipped with a Bluetooth 5.1 Low Energy (BLE) module, capable of detecting, collecting, aggregating and transmitting the Root Sum of Squares Method (RSS) of acceleration readings at consistent time intervals. This multi-frequency wireless controller functions at both sub-1 and 2.4 GHz bandwidths, endorsing the Bluetooth® 5.1 low energy standard and diverse wireless modalities via a Dynamic MultiProtocol Manager (DMM) interface. For demonstration purposes, the BMI160 is has been programmed to internally manage acceleration analyses across three axes, reducing data transmission, and minimizing connection times. This device, integrated with other physiological parameter monitoring systems of an individual/patient, can help correlate any variation in these parameters with the amount of motion. The integration of additional sensors can refine the precision of physiological metric evaluation, broadening the potential applications of such systems in sectors like healthcare and well-being.