Visualization of multidimensional clinical data from wearables on the web and on apps

Frink, Travis; Gyllinsky, Joshua V.; Mankodiya, Kunal

doi:10.1109/urtc.2017.8284217

Cited by 9 publications

(4 citation statements)

References 12 publications

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“…The clinical staff initially requested only a few values per day (challenge Sec. 2.3.4); however, we concluded that hourly values were beneficial since they depict daytime and nighttime patterns well, e.g., circadian rhythms (similar to [19,6]).…”

Section: Data Aggregationmentioning

confidence: 80%

“…To visualize wearable health data over the course of a day, Frink et al [19] implemented hourly circular pie charts for health data recorded from mobile applications, resulting in a circular form of data visualization similar to SpiralGraphs. Another wearable health visualization study [6] developed a visual dashboard depicting aggregated patient activity as hours per days (and minutes per hours) using color-coded upward pointing bar charts.…”

Section: Intuitive Visualization Of Wearable Health Datamentioning

confidence: 99%

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Visualization and Analysis of Wearable Health Data From COVID-19 Patients

Suter¹,

Spinner²,

Hoelz³

et al. 2022

Preprint

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Effective visualizations were evaluated to reveal relevant health patterns from multi-sensor realtime wearable devices that recorded vital signs from patients admitted to hospital with COVID-19. Furthermore, specific challenges associated with wearable health data visualizations, such as fluctuating data quality resulting from compliance problems, time needed to charge the device and technical problems are described. As a primary use case, we examined the detection and communication of relevant health patterns visible in the vital signs acquired by the technology. Customized heat maps and bar charts were used to specifically highlight medically relevant patterns in vital signs. A survey of two medical doctors, one clinical project manager and seven health data science researchers was conducted to evaluate the visualization methods. From a dataset of 84 hospitalized COVID-19 patients, we extracted one typical COVID-19 patient history and based on the visualizations showcased the health history of two noteworthy patients. The visualizations were shown to be effective, simple and intuitive in deducing the health status of patients. For clinical staff who are time-constrained and responsible for numerous patients, such visualization methods can be an effective tool to enable continuous acquisition and monitoring of patients' health statuses -even remotely.

show abstract

Section: Data Aggregationmentioning

confidence: 80%

Section: Intuitive Visualization Of Wearable Health Datamentioning

confidence: 99%

Visualization and Analysis of Wearable Health Data From COVID-19 Patients

Suter¹,

Spinner²,

Hoelz³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Frink et al proposed a new architecture of multidimensional clinical data visualization on the basis of mobile platforms and web, where clinical data is reorganized into a time-based visualization [14]. In different time periods, data can be interpolated over longer frames and finally presented in various colors or shapes for visual result.…”

Section: Realted Workmentioning

confidence: 99%

An IoT-Based Framework of Webvr Visualization for Medical Big Data in Connected Health

Lan

Zhou

et al. 2019

IEEE Access

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Recently, telemedicine has been widely applied in remote diagnosis, treatment and counseling, where the Internet of Things (IoT) technology plays an important role. In the process of telemedicine, data are collected from remote medical equipment, such as CT machine and MRI machine, and then transmitted and reconstructed locally in three-dimensions. Due to the large amount of data to be transmitted in the reconstructed model and the small storage capacity, data need to be compressed progressively before transmission. On this basis, we proposed a lightweight progressive transmission algorithm based on large data visualization in telemedicine to improve transmission efficiency and achieve lossless transmission of original data. Moreover, a novel four-layer system architecture based on IoT has been introduced, including the sensing layer, analysis layer, network layer and application layer. In this way, the three-dimensional reconstructed data at the local end is compressed and transmitted to the remote end, and then visualized at the remote end to show reconstructed 3D models. Thus, it is conducive to doctors in remote real-time diagnosis and treatment, and then realize the data processing and transmission between doctors, patients and medical equipment.INDEX TERMS Internet of Thing (IoT), telemedicine, lightweight progressive transmission, medical big data, visualization.

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“…Healthcare providers have also recognized this technology's potential in preventive care [9,10]. Computational analysis of large datasets generated by smartwatches to detect anomalies, extract meaning, and present information in a visually appealing manner benefits healthcare professionals by optimizing clinical decision-making for the early detection and diagnosis of possible health disorders [11][12][13][14]. Integrating smartwatch data into clinical care and with Electronic Medical Records (EMRs) may also provide a more comprehensive view of a person's health and complement routine clinical data, leading to better patient/client engagement in self-care and increased healthcare efficiency [15].…”

Section: Introductionmentioning

confidence: 99%

Co-Design, Development, and Evaluation of a Health Monitoring Tool Using Smartwatch Data: A Proof-of-Concept Study

2023

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Computational analysis and integration of smartwatch data with Electronic Medical Records (EMR) present potential uses in preventing, diagnosing, and managing chronic diseases. One of the key requirements for the successful clinical application of smartwatch data is understanding healthcare professional (HCP) perspectives on whether these devices can play a role in preventive care. Gaining insights from the vast amount of smartwatch data is a challenge for HCPs, thus tools are needed to support HCPs when integrating personalized health monitoring devices with EMR. This study aimed to develop and evaluate an application prototype, co-designed with HCPs and employing design science research methodology and diffusion of innovation frameworks to identify the potential for clinical integration. A machine learning algorithm was developed to detect possible health anomalies in smartwatch data, and these were presented visually to HCPs in a web-based platform. HCPs completed a usability questionnaire to evaluate the prototype, and over 60% of HCPs scored positively on usability. This preliminary study tested the proposed research to solve the practical challenges of HCP in interpreting smartwatch data before fully integrating smartwatches into the EMR. The findings provide design directions for future applications that use smartwatch data to improve clinical decision-making and reduce HCP workloads.

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Visualization of multidimensional clinical data from wearables on the web and on apps

Cited by 9 publications

References 12 publications

Visualization and Analysis of Wearable Health Data From COVID-19 Patients

Visualization and Analysis of Wearable Health Data From COVID-19 Patients

An IoT-Based Framework of Webvr Visualization for Medical Big Data in Connected Health

Co-Design, Development, and Evaluation of a Health Monitoring Tool Using Smartwatch Data: A Proof-of-Concept Study

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