A multiple warning and smart monitoring system using wearable devices for home care

Hung, Lun Ping; Lin, Chun-Cheng

doi:10.1016/j.ijhcs.2019.102381

Cited by 6 publications

(7 citation statements)

References 29 publications

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“…To sum up, it can be seen that the currently constructed intelligent analysis models of college students' mental health cannot efficiently complete the targeted treatment analysis of college students, and different model data need to be classified. Therefore, there are improvements in convenience and universality [14][15][16]. On the other hand, there are few research results combined with computer monitoring technology [17][18][19].…”

Section: State Of the Artmentioning

confidence: 99%

Model Analysis of Applying Computer Monitoring to College Students’ Mental Health

Mao

Liu

2022

Journal of Sensors

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In the past 20 years, although there are many achievements in the model analysis and research, there are still problems of low data utilization and low accuracy. This paper analyzes the mental health level of college students based on chaotic algorithm. At the same time, the application of computer monitoring algorithm to students’ real life psychology is discussed. According to different types of mental health analysis models, the high-precision matching analysis of different students is realized. At the same time, according to the personality characteristics and psychological changes of different students, the model is established and analyzed. Finally, an experiment is designed to carry out practical application and data analysis of the mental health analysis model. The results show that the intelligent analysis model based on computer chaos algorithm has better classification effect. In addition, the algorithm can also make different evaluation strategies according to the different personality of students and can carry out multidimensional classification for college students of different majors. It has effectively increased the proportion of college students’ mental health groups. Compared with the current mainstream algorithms, the algorithm used in this study can adaptively classify college students of different majors. The accuracy of the experimental results is improved by at least 37% compared with the traditional method, and the error is low.

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Section: State Of the Artmentioning

confidence: 99%

Model Analysis of Applying Computer Monitoring to College Students’ Mental Health

Mao

Liu

2022

Journal of Sensors

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“…A smart framework which assists people in self-caring in their home itself is proposed in [15]. The focus of this framework is on the analysis procedure of patient real-time data.…”

Section: Related Workmentioning

confidence: 99%

“…The focus of this framework is on the analysis procedure of patient real-time data. Electrocardiogram (EKG) is proposed in [15] to monitor the health of patients who have undergone cardiovascular operation. Smart modules are designed by deploying the private cloud.…”

Section: Related Workmentioning

confidence: 99%

Prediction of heart abnormalities using deep learning model and wearabledevices in smart health homes

Shafi

Obaidat

Krishna

et al. 2021

Multimed Tools Appl

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The prediction of abnormality in the heart functionality at an early stage increases the chances of saving the life of people. Thus, this paper proposes a technique which predicts the abnormality in the functionality of the heart using heart rate in the form of beats per minute using wearable devices and deep learning model. The devices used are wrist strap and devices that can be fixed near the chest of the person or back of the person where heart beat can be detected. The proposed system is divided into 3 modules: (1) data collection and processing module, (2) prediction module and (3) communication module. First module is used to collect data and process, while prediction module predicts the abnormal functionality of the heart using deep learning model. One of the advantage of the proposed work in this paper is communication module as the communication is given to the doctor who can perform analysis before the patient reaches the hospital. A message is sent to the ambulance so that it reaches the destination on time. The message related to first aid is sent to two dear ones and the patient such that appropriate measures can be taken. The proposed technique is evaluated in terms of sensitivity, specificity, F 1 -Score, time and ROC curve metrics. It is also compared with the Two Stage Neural Network and TSNN and proved to be performing better.

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“…However, commercial services for remote monitoring of biometric parameters for the elderly population are still incipient, in part due to the low usability of current systems, given the particular needs and skills of the elderly. Remote monitoring can play an important role in supporting medical diagnosis, surveillance of patients in home hospitalization during subacute periods [11] or for continuous monitoring of people with chronic diseases, complementing patients' self-management at home [12], for example, through monitoring of weight, heart rate, oxygen saturation, blood pressure and glycaemia [13]. Yet, services to transfer these data to remote systems mostly depend on the use of smartphone or tablet applications (mHealth scenarios) [14], which represents a barrier to their use by the majority of older adults [15].…”

Section: Introductionmentioning

confidence: 99%

Usability of a telehealth solution based on TV interaction for the elderly: the VITASENIOR-MT case study

Pires

Lopes

Correia

et al. 2022

Univ Access Inf Soc

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Remote monitoring of biometric data in the elderly population is an important asset for improving the quality of life and level of independence of elderly people living alone. However, the design and implementation of health technological solutions often disregard the elderly physiological and psychological abilities, leading to low adoption of these technologies. We evaluate the usability of a remote patient monitoring solution, VITASENIOR-MT, which is based on the interaction with a television set. Twenty senior participants (over 64 years) and a control group of 20 participants underwent systematic tests with the health platform and assessed its usability through several questionnaires. Elderly participants scored high on the usability of the platform, very close to the evaluation of the control group. Sensory, motor and cognitive limitations were the issues that most contributed to the difference in usability assessment between the elderly group and the control group. The solution showed high usability and acceptance regardless of age, digital literacy, education and impairments (sensory, motor and cognitive), which shows its effective viability for use and implementation as a consumer product in the senior market.

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A multiple warning and smart monitoring system using wearable devices for home care

Cited by 6 publications

References 29 publications

Model Analysis of Applying Computer Monitoring to College Students’ Mental Health

Model Analysis of Applying Computer Monitoring to College Students’ Mental Health

Prediction of heart abnormalities using deep learning model and wearabledevices in smart health homes

Usability of a telehealth solution based on TV interaction for the elderly: the VITASENIOR-MT case study

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