A mHealth solution for contact-less self-monitoring of blood oxygen saturation

Casalino, Gabriella; Castellano, Giovanna; Zaza, Gianluca

doi:10.1109/iscc50000.2020.9219718

Cited by 45 publications

(50 citation statements)

References 30 publications

(33 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The mapping process involved three stages: identification of research questions; elaboration of the search process; and definition of the criteria for filtering the results. Using snowball sampling, more articles were included in the full list of reviewed works (Cooper 2016), and the related works were updated to include papers published in the last year, such as the work of Casalino et al (2020).…”

Section: Related Workmentioning

confidence: 99%

“…Pazienza et al (2020) investigated the most suitable machine learning (ML) technique for the prediction of clinical risk classes of a continuously monitored patient in a particular condition where a limited number of vital parameters are available. Casalino et al (2020) proposed a mobile health solution to self-measure the blood oxygen saturation through a smartphone camera. This work allow the users to monitor their blood oxygen saturation at home without the use of medical devices.…”

Section: Related Workmentioning

confidence: 99%

“…Among the papers found through systematic mapping, ten works that collected and analyzed health data were compared with the ODIN model (Furberg et al 2017;Habib et al 2017;Fernandes and Lucena 2017;Said et al 2018;Choi and Shin 2018;Hassan et al 2019;Swaroop et al 2019;Yin et al 2019;Pazienza et al 2020;Casalino et al 2020). The following aspects allowed the analysis of the related works: consider and collect non-physiological data from the user context; optimize the vital sign collection to reduce the effort to store the data; and save computational resources of the involved systems in the collection and analysis of vital signs.…”

Section: Related Workmentioning

confidence: 99%

“…Three papers partially fulfill the first aspect (Said et al 2018;Choi and Shin 2018;Casalino et al 2020). In these works, only contextual data were collected and these data were not correlated with physiological data.…”

Section: Related Workmentioning

confidence: 99%

See 3 more Smart Citations

A computational model for adaptive recording of vital signs through context histories

Aranda

Bavaresco

Carvalho

et al. 2021

J Ambient Intell Human Comput

View full text Add to dashboard Cite

Wearable devices emerged from the advancement of communication technology and the miniaturization of electronic components. These devices periodically monitor the user's vital signs and generally have a short battery life. This work introduces ODIN, a model for optimized vital signs collection based on adaptive rules. Analyzing vital sign values requires preciseness, so the adaption of these collected data allows a personalized analysis of the user's health condition. The comparison with related works indicates that ODIN is the only model that presents context-aware-adaptive vital signs collection. The implementation of a prototype allowed to perform three evaluations of ODIN. The first evaluation used simulations in different scenarios, with the adaptive approach increasing battery life by 119% through the analysis of input data compared to data collection without adaptivity. The second evaluation applied the prototype to a database of real physiologic data, which allowed reduced data collection when the user has regular vital signs. This reduction optimized battery consumption by 66% compared to collection without adaptivity. Finally, the third evaluation applied ODIN through an Arduino and a heart rate monitor (Polar H7). The average power saved across mobile devices was 21%. Consequently, the adaptive strategy presented in this work allows the optimization of computational resources during the collection and analysis of vital signs. This optimization occurs because of the reduction in energy expenditure and the reduction in the amount of data that needs to be collected and stored.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

A computational model for adaptive recording of vital signs through context histories

Aranda

Bavaresco

Carvalho

et al. 2021

J Ambient Intell Human Comput

View full text Add to dashboard Cite

show abstract

“…Since these devices need to be placed over the index finger correctly and have proper contact with the skin to work, elderly people may require the assistance of health professionals. Casalino et al (2020) proposed a contact free RGB camera based strategy that processes video frames of a person's face to find the changes in the absorption of light, and hence the SpO2. It is both contact-free and easy to use and can run on any mobile device with a camera such as a smartphone or laptop.…”

Section: Oxygen Saturationmentioning

confidence: 99%

A Review on Wearable and Contactless Sensing for COVID-19 With Policy Challenges

et al. 2021

View full text Add to dashboard Cite

The COVID-19 pandemic has affected more than 100 million people worldwide, with around 500,000 cases reported daily. This has led to the overwhelming of healthcare systems even in developed countries such as the US, UK, etc. Remote monitoring of COVID-19 patients with non-serious symptoms can help reduce the burden on healthcare facilities and make them available for high risk groups and the seriously affected. The pandemic has accelerated the demand for the remote patient monitoring (RPM) technologies, and the market is expected to reach 2.14 billion in 2027 from the value of 786.4 million in 2019. In RPM programs, there are two types of sensors that can be used: wearable and contactless. The former, which is currently more widely used, is not only more obtrusive and uncomfortable, but can also lead to cross-infection through patient contact. These two types of technologies are discussed and compared for each vital sign. In the respiratory system, the vital signs are the respiratory rate (RR) and oxygen saturation (SpO2), while for the latter, they are the heart rate/rhythm and the blood pressure (BP). Then, the discussion is broadened to policy level changes needed to expedite the use of such technologies for remote patient monitoring (RPM) in the world. Around 80% of countries' RPM programs are either informal or in a pilot phase, and thus lack policies and an established regulatory framework to implement their programs. The various policies needed to initiate, deliver, and reimburse RPM programs during emergency situations and outbreaks are discussed. Finally, technologies such as contactless systems, robotics, and Internet-of-things (IoT) that will revolutionize healthcare in the future by reducing the interaction between physicians and patients and cross-infection are discussed.

show abstract

Self‐care for coronavirus disease through electronic health technologies: A scoping review

Asadi

Rahimi

Ghaderkhany

et al. 2023

Health Science Reports

View full text Add to dashboard Cite

Background and Aims Considering the rapid spread and transmission of COVID‐19 and its high mortality rate, self‐care practices are of special importance during this pandemic to prevent and control the spread of the virus. In this regard, electronic health systems can play a major role in improving self‐care practices related to coronavirus disease. This study aimed to review the electronic health technologies used in each of the constituent elements of the self‐care (self‐care maintenance, self‐care monitoring, and self‐care management) during the COVID‐19 pandemic. Methods This scoping review was conducted based on Arksey and O'Malley's framework. In this study, the specific keywords related to “electronic health,” “self‐care,” and “COVID‐19” were searched on PubMed, Web of Science, Scopus, and Google. Results Of the 47 articles reviewed, most articles (27 articles) were about self‐care monitoring and aimed to monitor the vital signs of patients. The results showed that the use of electronic health tools mainly focuses on training in the control and prevention of coronavirus disease during this pandemic, in the field of self‐care maintenance, and medication management, communication, and consultation with healthcare providers, in the field of self‐care management. Moreover, the most commonly used electronic health technologies were mobile web applications, smart vital signs monitoring devices, and social networks, respectively. Conclusion The study findings suggested that the use of electronic health technologies, such as mobile web applications and social networks, can effectively improve self‐care practices for coronavirus disease. In addition, such technologies can be applied by health policymakers and disease control and prevention centers to better manage the COVID‐19 pandemic.

show abstract

A mHealth solution for contact-less self-monitoring of blood oxygen saturation

Cited by 45 publications

References 30 publications

A computational model for adaptive recording of vital signs through context histories

A computational model for adaptive recording of vital signs through context histories

A Review on Wearable and Contactless Sensing for COVID-19 With Policy Challenges

Self‐care for coronavirus disease through electronic health technologies: A scoping review

Contact Info

Product

Resources

About