An Automated Machine Learning Pipeline for Monitoring and Forecasting Mobile Health Data

Bonaquist, Anna; Grehan, Meredith; Haines, Owen; Keogh, Joseph; Mullick, Tahsin; Singh, Neil; Shaaban, Sam; Radovic, Ana; Doryab, Afsaneh

doi:10.1109/sieds52267.2021.9483755

Cited by 4 publications

(3 citation statements)

References 24 publications

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“…These data must be processed by a technology capable of quickly and accurately processing a large amount of data acquired in real time. Thus, this study used InfluxDB to store biosignal data in real time, using a time-series database [38,39]. Among the currently available time-series databases, InfluxDB is the most suitable for data storage because it is stable and can process data at a high speed.…”

Section: Edge Node System Designmentioning

confidence: 99%

A Patient Management System Using an Edge Computing-Based IoT Pulse Oximeter

Joo,

Kang,

Kang

et al. 2024

Applied Sciences

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Edge computing can provide core functions such as data collection and analysis without connecting to a centralized server. The convergence of edge computing and IoT devices has enabled medical institutions to collect patient data in real time, improving the efficiency of short- and long-term patient management. Medical equipment measures a large amount of biosignal data for analyzing diseases and patient health conditions. However, analyzing and monitoring biosignal data using a centralized server or cloud limit the medical institutions’ ability to analyze patients’ conditions in real time, preventing prompt treatment. Therefore, edge computing can enhance the efficiency of patient biosignal data collection and analysis for patient management systems. Analyzing biosignals using edge computing can eliminate the wait time present in cloud computing. Hence, this study aims to develop an IoT pulse oximeter using edge computing for medical institutions and proposes an architecture for providing a real-time monitoring service. The proposed system utilizes five types of raw (IR AC, IR DC, red AC, red DC, AMB), pulse, and SpO2 data measured using IoT pulse oximeters. Edge nodes are installed in every hospital ward to collect, analyze, and monitor patient biosignal data through a wireless network. The collected biosignal data are transmitted to the cloud for managing and monitoring the data of all patients. This system enables medical institutions to collect and analyze raw biosignal data in real time, by which an integrated management system can be established by connecting various types of IoT-based medical equipment.

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Section: Edge Node System Designmentioning

confidence: 99%

A Patient Management System Using an Edge Computing-Based IoT Pulse Oximeter

Joo,

Kang,

Kang

et al. 2024

Applied Sciences

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show abstract

“…After this step, researchers can use such features to create the desired statistical and machine learning models in their favorite programming language. Real world deployments of RAPIDS have been used for predicting depression symptoms ( 9 , 39 ), perioperative symptom burden estimation ( 40 ), creating individual signatures linking brain, behavior, and mood ( 41 ) and as a part of a machine learning pipeline for monitoring and forecasting mobile health data ( 42 ).…”

Section: Introductionmentioning

confidence: 99%

Reproducible Analysis Pipeline for Data Streams: Open-Source Software to Process Data Collected With Mobile Devices

Vega

Lian

Aguillera

et al. 2021

Front. Digit. Health

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Smartphone and wearable devices are widely used in behavioral and clinical research to collect longitudinal data that, along with ground truth data, are used to create models of human behavior. Mobile sensing researchers often program data processing and analysis code from scratch even though many research teams collect data from similar mobile sensors, platforms, and devices. This leads to significant inefficiency in not being able to replicate and build on others' work, inconsistency in quality of code and results, and lack of transparency when code is not shared alongside publications. We provide an overview of Reproducible Analysis Pipeline for Data Streams (RAPIDS), a reproducible pipeline to standardize the preprocessing, feature extraction, analysis, visualization, and reporting of data streams coming from mobile sensors. RAPIDS is formed by a group of R and Python scripts that are executed on top of reproducible virtual environments, orchestrated by a workflow management system, and organized following a consistent file structure for data science projects. We share open source, documented, extensible and tested code to preprocess, extract, and visualize behavioral features from data collected with any Android or iOS smartphone sensing app as well as Fitbit and Empatica wearable devices. RAPIDS allows researchers to process mobile sensor data in a rigorous and reproducible way. This saves time and effort during the data analysis phase of a project and facilitates sharing analysis workflows alongside publications.

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“…These data must be processed by a technology capable of quickly and accurately processing a large amount of data acquired in real time. Thus, this study used In uxDB to store biosignal data in real time using a time-series database[25][26]. Among currently available time-series databases, In uxDB is the most suitable for data storage because it is stable and can process data at a high speed.…”

mentioning

confidence: 99%

Real-time patient management monitoring system based on edge computing using IoT pulse oximeter

Joo

Kang

et al. 2023

Preprint

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Edge computing can provide core functions such as data collection and analysis without connecting to a centralized server. The convergence of edge computing and IoT devices has enabled medical institutions to collect patient data in real-time, which improved the efficiency of short- and long-term patient management. Medical equipment measures a large amount of biosignal data for analyzing diseases and patient health conditions. However, analyzing and monitoring biosignal data using a centralized server or cloud limit the medical institutions’ ability to analyze patients’ conditions in real time, preventing prompt treatment. Therefore, edge computing can enhance the efficiency of patient biosignal data collection and analysis for patient management systems. Analyzing biosignals using edge computing can eliminate the waiting time present in cloud computing. Hence, this study aims to develop an IoT pulse oximeter to use edge computing at medical institutions and proposes an architecture for providing a real-time monitoring service. The proposed system utilizes five types of raw (IR AC, IR DC, red AC, red DC, AMB), pulse, and SpO2 data measured using IoT pulse oximeters. Edge nodes are installed at every ward to collect, analyze, and monitor patient biosignal data through a wireless network. The collected biosignal data are transmitted to the cloud for managing and monitoring the data of all patients. This system enables medical institutions to collect and analyze raw biosignal data in real time, where an integrated management system can be established by connecting various IoT-based medical equipment.

show abstract

An Automated Machine Learning Pipeline for Monitoring and Forecasting Mobile Health Data

Cited by 4 publications

References 24 publications

A Patient Management System Using an Edge Computing-Based IoT Pulse Oximeter

A Patient Management System Using an Edge Computing-Based IoT Pulse Oximeter

Reproducible Analysis Pipeline for Data Streams: Open-Source Software to Process Data Collected With Mobile Devices

Real-time patient management monitoring system based on edge computing using IoT pulse oximeter

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