The Prospect of a New Smart Healthcare System: A Wearable Device-Based Complex Structure of Position Detecting and Location Recognition System

Chung, Kyung Jin; Kim, Jayoung; Whangbo, Taeg Keun; Kim, Khae Hawn

doi:10.5213/inj.19381534.077

Cited by 10 publications

(8 citation statements)

References 6 publications

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“…Moreover, the possibility of a smart band with a recognition system was tested in a previous pilot study ( Chung et al, 2019 ). Based on these previous studies, a smart band for real users was developed, and the accuracy of this technology was tested in the present study.…”

Section: Discussionmentioning

confidence: 99%

Development of a voiding diary using urination recognition technology in mobile environment

2020

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Section: Discussionmentioning

confidence: 99%

Development of a voiding diary using urination recognition technology in mobile environment

2020

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“…The data from the Arduino into the cloud gets loaded with the help of Wi-Fi module and Ethernet shield on the transport layer. However, because an Arduino Uno was utilized, several sensors were unable to be appropriately processed [ 19 ]. To track smart houses and heartbeats, a wireless sensor network (WSN) was created.…”

Section: Methodsmentioning

confidence: 99%

Patient Behavioral Analysis with Smart Healthcare and IoT

Dhiman

Iesa

Alsarhan

et al. 2021

Behavioural Neurology

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Patient behavioral analysis is the key factor for providing treatment to patients who may suffer from various difficulties including neurological disease, head trauma, and mental disease. Analyzing the patient’s behavior helps in determining the root cause of the disease. In traditional healthcare, patient behavioral analysis has lots of challenges that were much more difficult. The patient behavior can be easily analyzed with the development of smart healthcare. Information technology plays a key role in understanding the concept of smart healthcare. A new generation of information technologies including IoT and cloud computing is used for changing the traditional healthcare system in all ways. Using Internet of Things in the healthcare institution enhances the effectiveness as well as makes it more personalized and convenient to the patients. The first thing that will be discussed in the article is the technologies that have been used to support the smart class, and further, there will be a discussion on the existing problems with the smart healthcare system and how these problems can be solved. This study can provide essential information about the role of smart healthcare and IoT in maintaining behavior of patent. Various biomarkers are maintained properly with the help of these technologies. This study can provide effective information about importance of smart health system. This smart healthcare is conducted with the involvement of proper architecture. This is treated as effective energy efficiency architecture. Artificial intelligence is used increasingly in healthcare to maintain diagnosis and other important factors of healthcare. This application is also used to maintain patient engagement, which is also included in this study. Major hardware components are also included in this technology such as CO sensor and CO2 sensor.

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“…There are several studies using a mobile app for writing sleep diaries. Although studies such as theirs have identified the need to use technologies such as mobile apps for the efficient and reliable collection of data, current sensor-based studies for analyzing sleep quality focus on self-awareness of sleep activity and the amount of activity during the day rather than on lifestyle [27]- [29]. The efficient and secure method for EMA can achieve immediate awareness of responses, as well as monitoring compliance daily.…”

Section: B Sleep Quality Prediction Using Mobile Devicesmentioning

confidence: 99%

Assessing Sleep Quality Using Mobile EMAs: Opportunities, Practical Consideration, and Challenges

et al. 2022

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Sleep is one of the most important factors in maintaining both physical and mental health. There are many causes of sleep problems, it is generally necessary to maintain a healthy lifestyle to avoid them. In the medical field, information related to sleep problems including lifestyle information is obtained through interviews, but this approach is limited because it is dependent on the patient's memory. Thus, there are many studies adopting ecological momentary assessments (EMAs) to collect patient's lifestyles. Some of them also use smart devices to collect data effectively. However, these studies focused on specific factors such as smoking, exercising so that they have limits to reflect complex narrative of lifestyle patterns. Therefore, we proposed indicators consist of EMAs data for assessing everyday sleep quality and these indicators contain the complex lifestyle contexts in a quantitative manner. First, we collected real-life data using a smartphone through a 4-week data collection experiment. Second, we develop a method of generating daily indexes reflecting geospatial and social habits, social condition, activity level, and emotional condition using self-report data. Third, we evaluated daily indexes whether could use to supplement indicators comprising features using EMAs from conventional sleep questionnaires. The goal of analysis consists of five metrics of sleep quality that explain perceived sleep quality. The result of analysis indicates that features using both daily indexes and sleep questionnaires lead to better prediction of sleep quality. Additionally, it also shows the potential to generate indicators identifying complex human behaviors with the help of mobile devices and EMAs. Further research on user-friendly data acquisition methods and more diverse lifestyle information should be useful to support behavior decisions for better sleep in wellbeing services and in specialized medical fields.

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The Prospect of a New Smart Healthcare System: A Wearable Device-Based Complex Structure of Position Detecting and Location Recognition System

Cited by 10 publications

References 6 publications

Development of a voiding diary using urination recognition technology in mobile environment

Development of a voiding diary using urination recognition technology in mobile environment

Patient Behavioral Analysis with Smart Healthcare and IoT

Assessing Sleep Quality Using Mobile EMAs: Opportunities, Practical Consideration, and Challenges

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