Measuring on Physiological Parameters and Its Applications: A Review
DOI:
https://doi.org/10.26555/jiteki.v10i2.28767Keywords:
Data Acquisition, Deep Learning, Machine Learning, Physiological Parameter, Portable Device, Wearable DeviceAbstract
In providing patient care, it is essential to know the patient’s status to avoid incorrect treatment. Patient status includes various physiological parameters such as heart rate, blood oxygen saturation, blood pressure, body temperature, and respiratory rate. Measuring each physiological parameter requires data collection and analysis. Data acquisition in measuring physiological parameters can be categorized into contact methods, non-contact methods, invasive methods, and non-invasive methods. After data collection, it is crucial to analyze the collected data to ensure accurate and reliable measurements. This analysis can utilize RF signals, PPG signals, machine learning, and deep learning, depending on the specific needs and objectives of the study. This paper aims to identify studies based on types of data acquisition and analysis methods developed. These studies will be reviewed to understand the limitations of the data acquisition methods and analysis methods used. Additionally, this paper will discuss and classify the types of applications developed in these studies over the last five years, focusing on functionality, device design, and body-to-device connectivity. This review will identify whether the studies developed wearable or portable, wired or wireless devices, and their purpose whether for diagnosis, monitoring, or both. This review will also highlight the limitations and provide a brief perspective on future developments.Downloads
Published
2024-07-10
Issue
Section
Articles
License
Authors who publish with JITEKI agree to the following terms:
- Authors retain copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC BY-SA 4.0) that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
This work is licensed under a Creative Commons Attribution 4.0 International License