A framework for integrating artificial intelligence for clinical care with continuous therapeutic monitoring
The complex relationships between continuously monitored health signals and therapeutic
regimens can be modelled via machine learning. However, the clinical implementation of …
regimens can be modelled via machine learning. However, the clinical implementation of …
A taxonomy on smart healthcare technologies: Security framework, case study, and future directions
S Chaudhary, R Kakkar, NK Jadav, A Nair… - Journal of …, 2022 - Wiley Online Library
There is a massive transformation in the traditional healthcare system from the specialist‐
centric approach to the patient‐centric approach by adopting modern and intelligent …
centric approach to the patient‐centric approach by adopting modern and intelligent …
PROS: an efficient pattern-driven compressive sensing framework for low-power biopotential-based wearables with on-chip intelligence
While the global healthcare market of wearable devices has been growing significantly in
recent years and is predicted to reach $60 billion by 2028, many important healthcare …
recent years and is predicted to reach $60 billion by 2028, many important healthcare …
Research on calibration method of MEMS gyroscope mounting error based on large-range autocollimator
T Feng, J Yan, L Liu, Y Huo, I Konyakhin… - IEEE Sensors …, 2023 - ieeexplore.ieee.org
A calibration and measurement method based on an autocollimator and a manual turntable
are proposed to calibrate the mounting error of a three-axis microelectromechanical system …
are proposed to calibrate the mounting error of a three-axis microelectromechanical system …
A systematic survey of research trends in technology usage for Parkinson's disease
Parkinson's disease (PD) is a neurological disorder with complicated and disabling motor
and non-motor symptoms. The complexity of PD pathology is amplified due to its …
and non-motor symptoms. The complexity of PD pathology is amplified due to its …
Trends in machine learning and electroencephalogram (EEG): a review for undergraduate researchers
This paper presents a systematic literature review on Brain-Computer Interfaces (BCIs) in
the context of Machine Learning. Our focus is on Electroencephalography (EEG) research …
the context of Machine Learning. Our focus is on Electroencephalography (EEG) research …
Intelligent Security System for Preventing DDoS Attacks for 6G Enabled WBSN using Improve Grey Wolf Optimization
AP Muniyandi, B Balusamy… - IEEE Transactions …, 2024 - ieeexplore.ieee.org
Wireless Body Sensor Network (WBSN) has gained increasing attention in health monitoring
and service sector for real-time health monitoring and providing high-quality service. 6G …
and service sector for real-time health monitoring and providing high-quality service. 6G …
Investigation of mmwave radar technology for non-contact vital sign monitoring
Non-contact vital sign monitoring has many advantages over conventional methods in being
comfortable, unobtrusive and without any risk of spreading infection. The use of millimeter …
comfortable, unobtrusive and without any risk of spreading infection. The use of millimeter …
A survey of embedded machine learning for smart and sustainable healthcare applications
Recent advances in machine learning algorithms (ML) and low-power edge devices enable
novel wearable applications. Embedded machine learning organically combines these …
novel wearable applications. Embedded machine learning organically combines these …
Time majority voting, a PC-based EEG classifier for non-expert users
Abstract Using Machine Learning and Deep Learning to predict cognitive tasks from
electroencephalography (EEG) signals is a rapidly advancing field in Brain-Computer …
electroencephalography (EEG) signals is a rapidly advancing field in Brain-Computer …