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基于分布式无线网关的康复患者监测系统现场评估

On-site Evaluation of Rehabilitation Patients Monitoring System Using Distributed Wireless Gateways.

作者信息

Matsunaga Kenichi, Ogasawara Takayuki, Kodate Junichi, Mukaino Masahiko, Saitoh Eiichi

出版信息

Annu Int Conf IEEE Eng Med Biol Soc. 2019 Jul;2019:3195-3198. doi: 10.1109/EMBC.2019.8856963.

DOI:10.1109/EMBC.2019.8856963
PMID:31946567
Abstract

This paper presents a hands-free monitoring system for rehabilitation patients that uses wireless gateways to fully cover the floor of an inpatient ward. For stroke rehabilitation, 24-hour monitoring is recommended. Low-power wireless such as Bluetooth Low Energy (BLE) is suitable for this purpose because of its long battery life. However, most systems require smartphones or tablet computers to acquire patient data due to BLE's short communication range. Instead of using smartphones, we installed around fifty BLE gateways to implement a hands-free data acquisition system. The system was evaluated both quantitatively and qualitatively. The data acquisition rate of the system was found to be over 90% through 24-hour patient monitoring, which is almost the same as that for systems using smartphones. Questionnaires about usability administered to both medical staff and patients suggested that they felt the smartphone-less system was more comfortable than the smartphone system. These results suggest the possibility of using such a distributed data acquisition system in real medical wards and its benefits.

摘要

本文介绍了一种用于康复患者的免提监测系统,该系统使用无线网关来完全覆盖住院病房的地面。对于中风康复,建议进行24小时监测。低功耗无线技术,如蓝牙低功耗(BLE),因其电池续航时间长而适用于此目的。然而,由于BLE的通信范围较短,大多数系统需要智能手机或平板电脑来获取患者数据。我们没有使用智能手机,而是安装了大约五十个BLE网关来实现一个免提数据采集系统。对该系统进行了定量和定性评估。通过对患者进行24小时监测,发现该系统的数据采集率超过90%,这与使用智能手机的系统几乎相同。对医务人员和患者进行的关于可用性的问卷调查表明,他们觉得无智能手机系统比智能手机系统更舒适。这些结果表明了在实际医疗病房中使用这种分布式数据采集系统的可能性及其好处。

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引用本文的文献

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Human Factors, Human-Centered Design, and Usability of Sensor-Based Digital Health Technologies: Scoping Review.基于传感器的数字健康技术的人因工程、以人为中心的设计和可用性:范围综述。
J Med Internet Res. 2024 Nov 15;26:e57628. doi: 10.2196/57628.
2
Prediction of stroke patients' bedroom-stay duration: machine-learning approach using wearable sensor data.中风患者住院时间的预测:基于可穿戴传感器数据的机器学习方法。
Front Bioeng Biotechnol. 2024 Jan 3;11:1285945. doi: 10.3389/fbioe.2023.1285945. eCollection 2023.
3
Ensemble averaging for categorical variables: Validation study of imputing lost data in 24-h recorded postures of inpatients.
分类变量的总体均值:住院患者24小时记录姿势中缺失数据插补的验证研究。
Front Physiol. 2023 Jan 26;14:1094946. doi: 10.3389/fphys.2023.1094946. eCollection 2023.