Zobrazeno 1 - 10
of 242
pro vyhledávání: '"Brendan O'Flynn"'
Publikováno v:
Scientific Data, Vol 11, Iss 1, Pp 1-14 (2024)
Abstract Wearable sensors have recently been extensively used in sports science, physical rehabilitation, and industry providing feedback on physical fatigue. Information obtained from wearable sensors can be analyzed by predictive analytics methods,
Externí odkaz:
https://doaj.org/article/4ee293eaae0b478e8577a7a59545212f
Autor:
Colum Crowe, Marco Sica, Lorna Kenny, Brendan O'Flynn, David Scott Mueller, Suzanne Timmons, John Barton, Salvatore Tedesco
Publikováno v:
IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol 32, Pp 3828-3836 (2024)
Motor symptoms such as tremor and bradykinesia can develop concurrently in Parkinson’s disease; thus, the ideal home monitoring system should be capable of tracking symptoms continuously despite background noise from daily activities. The goal of t
Externí odkaz:
https://doaj.org/article/d9ee9092576948aa8397f419fbd1c22b
Autor:
Sanjeev Kumar, Gholamhosein Moloudian, Roy B. V. B. Simorangkir, Dinesh R. Gawade, Brendan O'Flynn, John L. Buckley
Publikováno v:
IEEE Open Journal of Antennas and Propagation, Vol 5, Iss 5, Pp 1258-1281 (2024)
With recent advances in wearable wrist-worn wireless sensing applications, the demand for smartwatches and wristbands is rapidly increasing due to their widespread adoption in applications such as smart health monitoring, security, and fitness tracki
Externí odkaz:
https://doaj.org/article/51f7a5da75ca447ca5ff564c92c79b8b
Autor:
Marco Sica, Omid Talebi Varnosfaderani, Colum Crowe, Lorna Kenny, Andrea Bocchino, Brendan O'Flynn, David Scott Mueller, Salvatore Tedesco, Suzanne Timmons, John Barton
Publikováno v:
IEEE Access, Vol 12, Pp 38436-38455 (2024)
Parkinson’s disease is a degenerative neurological disorder that impairs motor functions and is accompanied by a wide range of non-motor symptoms, such as sleep problems. Parkinsonism is assessed during clinical evaluations and via self-administere
Externí odkaz:
https://doaj.org/article/99c12cba12f34b63bed8d1ee10ae9d5b
Publikováno v:
IEEE Access, Vol 12, Pp 39186-39203 (2024)
The rapidly evolving landscape of artificial intelligence (AI) and machine learning has placed data at the forefront of healthcare innovation. Electroencephalography (EEG) has gained significant attention for its potential to revolutionize healthcare
Externí odkaz:
https://doaj.org/article/db860d36cf0547a6ac380b8d7c3d8c79
Autor:
Patricia O'Sullivan, Matteo Menolotto, Andrea Visentin, Brendan O'Flynn, Dimitrios-Sokratis Komaris
Publikováno v:
IEEE Access, Vol 12, Pp 21347-21357 (2024)
Pressure insoles allow for the collection of real time pressure data inside and outside a laboratory setting as they are non-intrusive and can be simply integrated into industrial environments for occupational health and safety monitoring purposes. A
Externí odkaz:
https://doaj.org/article/9082c21205d24c07895c1ed34e69957f
Publikováno v:
Sensors, Vol 24, Iss 11, p 3697 (2024)
Comprehending the regulatory mechanisms influencing blood pressure control is pivotal for continuous monitoring of this parameter. Implementing a personalized machine learning model, utilizing data-driven features, presents an opportunity to facilita
Externí odkaz:
https://doaj.org/article/40ff3e90885747329072d36c1b0713a7
Autor:
Gholamhosein Moloudian, Sepehr Soltani, Sirous Bahrami, John L. Buckley, Brendan O’Flynn, Ali Lalbakhsh
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-15 (2023)
Abstract Conventional Wilkinson power dividers (WPDs) can provide acceptable performance close to the nominal center frequency. However, these WPDs can also exhibit poor out-of-band performance while requiring a large footprint. In order to improve o
Externí odkaz:
https://doaj.org/article/c46012260cea4f568b6da4ff817a1144
Publikováno v:
BMC Sports Science, Medicine and Rehabilitation, Vol 14, Iss 1, Pp 1-12 (2022)
Abstract Background The benefits to be obtained from home-based physical therapy programmes are dependent on the proper execution of physiotherapy exercises during unsupervised treatment. Wearable sensors and appropriate movement-related metrics may
Externí odkaz:
https://doaj.org/article/52a2f022397141b5919cc60f3ffdeb68
Autor:
Salvatore Tedesco, Oscar Manzano Torre, Marco Belcastro, Pasqualino Torchia, Davide Alfieri, Liudmila Khokhlova, Sokratis Dimitrios Komaris, Brendan O'flynn
Publikováno v:
IEEE Access, Vol 10, Pp 98309-98328 (2022)
Smart wearables are a promising tool for the objective and quantifiable monitoring of patients’ capabilities during remote at-home assessments. A novel platform for the remote assessment of patients undergoing knee rehabilitation has been presented
Externí odkaz:
https://doaj.org/article/fd34fdd15b4747b69c93fa3c7a9c438c