A Smartwatch-based Framework for Real-time and Online Assessment and Mobility Monitoring

Overview

Journal J Biomed Inform

Publisher Elsevier

Specialty Medical Informatics

Date 2018 Nov 11

PMID 30414474

Citations 33

Authors

Matin Kheirkhahan

Sanjay Nair

Anis Davoudi

Parisa Rashidi

Amal A Wanigatunga

Duane B Corbett

Tonatiuh Mendoza

Todd M Manini

Sanjay Ranka

Affiliations

Soon will be listed here.

Abstract

Smartphone and smartwatch technology is changing the transmission and monitoring landscape for patients and research participants to communicate their healthcare information in real time. Flexible, bidirectional and real-time control of communication allows development of a rich set of healthcare applications that can provide interactivity with the participant and adapt dynamically to their changing environment. Additionally, smartwatches have a variety of sensors suitable for collecting physical activity and location data. The combination of all these features makes it possible to transmit the collected data to a remote server, and thus, to monitor physical activity and potentially social activity in real time. As smartwatches exhibit high user acceptability and increasing popularity, they are ideal devices for monitoring activities for extended periods of time to investigate the physical activity patterns in free-living condition and their relationship with the seemingly random occurring illnesses, which have remained a challenge in the current literature. Therefore, the purpose of this study was to develop a smartwatch-based framework for real-time and online assessment and mobility monitoring (ROAMM). The proposed ROAMM framework will include a smartwatch application and server. The smartwatch application will be used to collect and preprocess data. The server will be used to store and retrieve data, remote monitor, and for other administrative purposes. With the integration of sensor-based and user-reported data collection, the ROAMM framework allows for data visualization and summary statistics in real-time.

Citing Articles

Accuracy of the Huawei GT2 Smartwatch for Measuring Physical Activity and Sleep Among Adults During Daily Life: Instrument Validation Study.

Mei L, He Z, Hu L JMIR Form Res. 2024; 8:e59521.

PMID: 39727144 PMC: 11683742. DOI: 10.2196/59521.

AI-Aided Gait Analysis with a Wearable Device Featuring a Hydrogel Sensor.

Hasan S, Dauria B, Mahmud M, Adams S, Long J, Kong L Sensors (Basel). 2024; 24(22).

PMID: 39599145 PMC: 11598565. DOI: 10.3390/s24227370.

Noninvasive biometric monitoring technologies for patients with heart failure.

Arriola-Montenegro J, Mutirangura P, Akram H, Tsangaris A, Koukousaki D, Tschida M Heart Fail Rev. 2024; .

PMID: 39436486 DOI: 10.1007/s10741-024-10441-7.

The Value of Smartwatches in the Health Care Sector for Monitoring, Nudging, and Predicting: Viewpoint on 25 Years of Research.

Kohler C, Bartschke A, Furstenau D, Schaaf T, Salgado-Baez E J Med Internet Res. 2024; 26:e58936.

PMID: 39356287 PMC: 11549588. DOI: 10.2196/58936.

Smartphone-Based Hand Function Assessment: Systematic Review.

Fu Y, Zhang Y, Ye B, Babineau J, Zhao Y, Gao Z J Med Internet Res. 2024; 26:e51564.

PMID: 39283676 PMC: 11443181. DOI: 10.2196/51564.

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