AIMC Topic: Monitoring, Physiologic

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AI-Aided Gait Analysis with a Wearable Device Featuring a Hydrogel Sensor.

Sensors (Basel, Switzerland)
Wearable devices have revolutionized real-time health monitoring, yet challenges persist in enhancing their flexibility, weight, and accuracy. This paper presents the development of a wearable device employing a conductive polyacrylamide-lithium chlo...

The Promise of Remote Patient Monitoring.

Telemedicine journal and e-health : the official journal of the American Telemedicine Association
The promise of remote patient monitoring (RPM) lies in its ability to revolutionize health care delivery by enabling continuous, real-time tracking of patient health outside traditional clinical settings. The COVID-19 pandemic accelerated the adoptio...

Implementing AI-Driven Bed Sensors: Perspectives from Interdisciplinary Teams in Geriatric Care.

Sensors (Basel, Switzerland)
Sleep is a crucial aspect of geriatric assessment for hospitalized older adults, and implementing AI-driven technology for sleep monitoring can significantly enhance the rehabilitation process. Sleepsense, an AI-driven sleep-tracking device, provides...

Noninvasive biometric monitoring technologies for patients with heart failure.

Heart failure reviews
Heart failure remains one of the leading causes of mortality and hospitalizations in the US that not only impacts quality of life but also poses a significant public health burden. The majority of affected patients are admitted with signs and symptom...

Machine Learning Applied to Edge Computing and Wearable Devices for Healthcare: Systematic Mapping of the Literature.

Sensors (Basel, Switzerland)
The integration of machine learning (ML) with edge computing and wearable devices is rapidly advancing healthcare applications. This study systematically maps the literature in this emerging field, analyzing 171 studies and focusing on 28 key article...

Controlled and Real-Life Investigation of Optical Tracking Sensors in Smart Glasses for Monitoring Eating Behavior Using Deep Learning: Cross-Sectional Study.

JMIR mHealth and uHealth
BACKGROUND: The increasing prevalence of obesity necessitates innovative approaches to better understand this health crisis, particularly given its strong connection to chronic diseases such as diabetes, cancer, and cardiovascular conditions. Monitor...

Artificial Intelligence for the Evaluation of Postures Using Radar Technology: A Case Study.

Sensors (Basel, Switzerland)
In the last few decades, major progress has been made in the medical field; in particular, new treatments and advanced health technologies allow for considerable improvements in life expectancy and, more broadly, in quality of life. As a consequence,...

A review of machine learning methods for non-invasive blood pressure estimation.

Journal of clinical monitoring and computing
Blood pressure is a very important clinical measurement, offering valuable insights into the hemodynamic status of patients. Regular monitoring is crucial for early detection, prevention, and treatment of conditions like hypotension and hypertension,...

CardioGuard: AI-driven ECG authentication hybrid neural network for predictive health monitoring in telehealth systems.

SLAS technology
The increasing integration of telehealth systems underscores the importance of robust and secure methods for patient data management. Traditional authentication methods, such as passwords and PINs, are prone to breaches, underscoring the need for mor...

Future of service member monitoring: the intersection of biology, wearables and artificial intelligence.

BMJ military health
While substantial investment has been made in the early identification of mental and behavioural health disorders in service members, rates of depression, substance abuse and suicidality continue to climb. Objective and persistent measures are needed...