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

IF 2.3 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC IEEE journal of radio frequency identification Pub Date : 2024-02-06 DOI:10.1109/JRFID.2024.3363074
Petar Šolic;Riccardo Colella;Giuseppe Grassi;Toni Perković;Carlo Giacomo Leo;Ana Čulić;Vladimir Pleština;Saverio Sabina;Luca Catarinucci
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引用次数: 0

摘要

物联网(IoT)框架改变了传感器数据的利用方式,开创了将传感器集成到现代环境各个方面的新时代。可穿戴技术领域的一个紧迫问题是高效的电源管理,包括低功耗和减少电池充电时间。本研究介绍了一种配备蓝牙 5.1 低能耗(BLE)模块的电子设备,它能够以一致的时间间隔检测、收集、汇总和传输加速度读数的平方根和法(RSS)。这款多频无线控制器可在 1 和 2.4 GHz 以下带宽工作,通过动态多协议管理器 (DMM) 接口支持蓝牙 5.1 低能耗标准和各种无线模式。为便于演示,BMI160 经过编程,可在内部管理三个轴的加速度分析,从而减少数据传输并最大限度地缩短连接时间。该设备与个人/患者的其他生理参数监测系统集成后,有助于将这些参数的任何变化与运动量联系起来。集成其他传感器可以提高生理指标评估的精确度,扩大此类系统在医疗保健和福祉等领域的潜在应用。
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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.
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