Integration of the Mobile Robot and Internet of Things to Monitor Older People

Pinheiro, Plácido Rogério; Pinheiro, Pedro Gabriel Calíope Dantas; Filho, Raimir Holanda; Barrozo, João Paulo Agostinho; Rodrigues, Joel J. P. C.; Pinheiro, Luana Ibiapina C. C.; Pereira, Maria Lúcia Duarte

doi:10.1109/access.2020.3009167

Cited by 13 publications

(6 citation statements)

References 30 publications

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“…In addition, this study suggests that these older adults often need planning and structure or emotive assistance [8,20]. Preceding studies show that older adults with a need for planning and structure often require support in terms of structural monitoring and behavioral reminders, which is in concordance with this study [36,37]. In addition, this study shows that these older adults may also require support in terms of health technology as these technologies could aid older adults in self-managing their health state.…”

Section: Discussionsupporting

confidence: 85%

Determining the Appropriate Support for Older Adults with Different Levels of Vitality and Health-Related Quality of Life: An Explanatory Study

Broekharst,

Bloem,

Blok

et al. 2023

IJERPH

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Vitality and health-related quality of life are often assessed in older adults. However, these assessments do not provide guidance on support for older adults with different levels of vitality and health-related quality of life. This guidance can be established through segmentation. The Subjective Health Experience model segments individuals and indicates support for each segment. By examining how older adults with different levels of vitality and health-related quality of life correspond with each segment and by specifying the indicated support to older adults, guidance can be established. This was examined by administering a questionnaire to 904 older adults and interviewing 8. Analysis was performed using one-way ANOVA and the matrix method. In segment 1, older adults sustained higher levels of vitality and health-related quality of life relative to other segments. They need information and certainty. In segment 2, older adults sustained lower levels of vitality and health-related quality of life relative to segment 1, and higher levels relative to segment 3 or 4. They need planning and structure. In segment 3, older adults sustained lower levels of vitality and health-related quality of life relative to segment 1 or 2, and higher levels relative to segment 4. They need emotive assistance. In segment 4, older adults sustained lower levels of vitality and health-related quality of life relative to other segments. They need personal coaching. As levels of vitality and health-related quality of life correspond with the segments, deploying vitality and health-related quality of life measures together with the model might be beneficial.

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Section: Discussionsupporting

confidence: 85%

Determining the Appropriate Support for Older Adults with Different Levels of Vitality and Health-Related Quality of Life: An Explanatory Study

Broekharst,

Bloem,

Blok

et al. 2023

IJERPH

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show abstract

“…In the future, this work will be extended by introducing cloud computing for telecardiology. In future works, other mobile methodologies which aggregate monitoring and mapping of the environment will be performed using Wall-Following, Simultaneous Location and Mapping (SLAM) and Sensor Fusion techniques and can be applied to the problem in question [55].…”

Section: Discussionmentioning

confidence: 99%

Assessment of Compressed and Decompressed ECG Databases for Telecardiology Applying a Convolution Neural Network

Soni

Nagpal

Garg³

et al. 2022

Electronics

Self Cite

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Incalculable numbers of patients in hospitals as a result of COVID-19 made the screening of heart patients arduous. Patients who need regular heart monitoring were affected the most. Telecardiology is used for regular remote heart monitoring of such patients. However, the resultant huge electrocardiogram (ECG) data obtained through regular monitoring affects available storage space and transmission bandwidth. These signals can take less space if stored or sent in a compressed form. To recover them at the receiver end, they are decompressed. We have combined telecardiology with automatic ECG arrhythmia classification using CNN and proposed an algorithm named TELecardiology using a Deep Convolution Neural Network (TELDCNN). Discrete cosine transform (DCT), 16-bit quantization, and run length encoding (RLE) were used for compression, and a convolution neural network (CNN) was applied for classification. The database was formed by combining real-time signals (taken from a designed ECG device) with an online database from Physionet. Four kinds of databases were considered and classified. The attained compression ratio was 2.56, and the classification accuracies for compressed and decompressed databases were 0.966 and 0.990, respectively. Comparing the classification performance of compressed and decompressed databases shows that the decompressed signals can classify the arrhythmias more appropriately than their compressed-only form, although at the cost of increased computational time.

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“…In order to forecast when an elderly person may fall, Pinheiro et al [27] recommended using a mobile network robot equipped with mapping and positioning capabilities in an indoor context. It uses sensor fusion, simultaneous localization and mapping (SLAM), and wall-following to see and map its surroundings.…”

Section: Literature Reviewsmentioning

confidence: 99%

An Integrated Scalable Framework for Cloud and IoT Based Green Healthcare System

Islam

Bhuiyan

2023

IEEE Access

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Recent developments in IoT-enabled cloud computing and interactive applications have made researchers rethink how healthcare services are currently provided. The IoT-cloud-based systems facilitate remote monitoring and support for patients. However, in the existing area, much emphasis has not been given to making the healthcare systems green. So, in this paper, we present an integrated framework for green healthcare and use cutting-edge technology to make an interactive user interface. We have also ensured the system's scalability and performance ratio. This system interface has been designed and developed for patients and doctors, where patients can send their healthcare data using wearable sensors, and doctors can receive those data in real-time. For data identification and analysis, we have adopted Hierarchical Clustering Algorithms. Finally, we have come up with a solution for how to make the interactive healthcare experience better for everyone.

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Integration of the Mobile Robot and Internet of Things to Monitor Older People

Abstract: This study proposes to develop, build and implement the IoT of the mobile network robot integrated with features of mapping and location in an internal environment, to trace the best route and obtain a fast and

Cited by 13 publications

References 30 publications

Determining the Appropriate Support for Older Adults with Different Levels of Vitality and Health-Related Quality of Life: An Explanatory Study

Determining the Appropriate Support for Older Adults with Different Levels of Vitality and Health-Related Quality of Life: An Explanatory Study

Assessment of Compressed and Decompressed ECG Databases for Telecardiology Applying a Convolution Neural Network

An Integrated Scalable Framework for Cloud and IoT Based Green Healthcare System

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