Towards a physical rehabilitation system using a telemedicine approach

Ruiz, Iván; Contreras, Juan David; García, J.

doi:10.1080/21681163.2020.1795929

Cited by 4 publications

(3 citation statements)

References 26 publications

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“…Paper [31] introduced a proposal to integrate an information system that has a robotic device in its control system. The proposal is aimed at accessing the use of telemedicine for physiotherapy rehabilitation.…”

Section: Telemedicine and Soamentioning

confidence: 99%

Tele-COVID: A Telemedicine SOA-based Architectural Design for COVID-9 Patients

AlReshan¹,

Asiri²,

Sulaiman³

et al. 2021

Computers, Materials &Amp; Continua

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In Wuhan, China, a novel Corona Virus (COVID-19) was detected in December 2019; it has changed the entire world and to date, the number of diagnosed cases is 38,756,289 1 and 1,095,216 1 people have died. This happened because a large number of people got affected and there is a lack of hospitals for COVID-19 patients. One of the precautionary measures for COVID-19 patients is isolation. To support this, there is an urgent need for a platform that makes treatment possible from a distance. Telemedicine systems have been drastically increasing in number and size over recent years. This increasing number intensi es the extensive need for telemedicine for the national healthcare system. In this paper, we present Tele-COVID which is a telemedicine application to treat COVID-19 patients from a distance. Tele-COVID is uniquely designed and implemented in Service-Oriented Architecture (SOA) to avoid the problem of interoperability, vendor lock-in, and data interchange. With the help of Tele-COVID, the treatment of patients at a distance is possible without the need for them to visit hospitals; in case of emergency, necessary services can also be provided.

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Section: Telemedicine and Soamentioning

confidence: 99%

Tele-COVID: A Telemedicine SOA-based Architectural Design for COVID-9 Patients

AlReshan¹,

Asiri²,

Sulaiman³

et al. 2021

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

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“…Silva et al used Petri nets to process the reliability and availability of wireless sensor networks in a smart hospital [ 29 ]. Ruiz et al proposed a tele-rehabilitation system to assist with physical rehabilitation during the COVID-19 pandemic [ 30 ].…”

Section: Introductionmentioning

confidence: 99%

Using a Hybrid Neural Network and a Regularized Extreme Learning Machine for Human Activity Recognition with Smartphone and Smartwatch

Tan

Shih

Liu

et al. 2023

Sensors

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Mobile health (mHealth) utilizes mobile devices, mobile communication techniques, and the Internet of Things (IoT) to improve not only traditional telemedicine and monitoring and alerting systems, but also fitness and medical information awareness in daily life. In the last decade, human activity recognition (HAR) has been extensively studied because of the strong correlation between people’s activities and their physical and mental health. HAR can also be used to care for elderly people in their daily lives. This study proposes an HAR system for classifying 18 types of physical activity using data from sensors embedded in smartphones and smartwatches. The recognition process consists of two parts: feature extraction and HAR. To extract features, a hybrid structure consisting of a convolutional neural network (CNN) and a bidirectional gated recurrent unit GRU (BiGRU) was used. For activity recognition, a single-hidden-layer feedforward neural network (SLFN) with a regularized extreme machine learning (RELM) algorithm was used. The experimental results show an average precision of 98.3%, recall of 98.4%, an F1-score of 98.4%, and accuracy of 98.3%, which results are superior to those of existing schemes.

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“…Many benefits exist within switching to remote care: increased access to healthcare, reduction in overall cost, increased interaction with providers and patient engagement, the ability to provide both synchronous and asynchronous treatment, and the eventual generation of large datasets for broader scientific investigation and impact. Though many approaches to telemedicine currently exist ( Ruiz et al, 2020 ; Seshadri et al, 2020 ), proper telemedicine for use in motor rehabilitation requires more functional components in combination with a computational framework that can systematically quantify specific aspects of motor performance, such as balance control skills.…”

Section: Introductionmentioning

confidence: 99%

A Computational Framework Towards the Tele-Rehabilitation of Balance Control Skills

Akbaş

Mummolo

2021

Front. Robot. AI

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Mobility has been one of the most impacted aspects of human life due to the spread of the COVID-19 pandemic. Home confinement, the lack of access to physical rehabilitation, and prolonged immobilization of COVID-19-positive patients within hospitals are three major factors that affected the mobility of the general population world-wide. Balance is one key indicator to monitor the possible movement disorders that may arise both during the COVID-19 pandemic and in the coming future post-COVID-19. A systematic quantification of the balance performance in the general population is essential for preventing the appearance and progression of certain diseases (e.g., cardiovascular, neurodegenerative, and musculoskeletal), as well as for assessing the therapeutic outcomes of prescribed physical exercises for elderly and pathological patients. Current research on clinical exercises and associated outcome measures of balance is still far from reaching a consensus on a “golden standard” practice. Moreover, patients are often reluctant or unable to follow prescribed exercises, because of overcrowded facilities, lack of reliable and safe transportation, or stay-at-home orders due to the current pandemic. A novel balance assessment methodology, in combination with a home-care technology, can overcome these limitations. This paper presents a computational framework for the in-home quantitative assessment of balance control skills. Novel outcome measures of balance performance are implemented in the design of rehabilitation exercises with customized and quantifiable training goals. Using this framework in conjunction with a portable technology, physicians can treat and diagnose patients remotely, with reduced time and costs and a highly customized approach. The methodology proposed in this research can support the development of innovative technologies for smart and connected home-care solutions for physical therapy rehabilitation.

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Towards a physical rehabilitation system using a telemedicine approach

Cited by 4 publications

References 26 publications

Tele-COVID: A Telemedicine SOA-based Architectural Design for COVID-9 Patients

Tele-COVID: A Telemedicine SOA-based Architectural Design for COVID-9 Patients

Using a Hybrid Neural Network and a Regularized Extreme Learning Machine for Human Activity Recognition with Smartphone and Smartwatch

A Computational Framework Towards the Tele-Rehabilitation of Balance Control Skills

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