AIMC Topic: Monitoring, Physiologic

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Smartphone as a monitoring tool for bipolar disorder: a systematic review including data analysis, machine learning algorithms and predictive modelling.

International journal of medical informatics
BACKGROUND: Bipolar disorder (BD) is a chronic illness with a high recurrence rate. Smartphones can be a useful tool for detecting prodromal symptoms of episode recurrence (through real-time monitoring) and providing options for early intervention be...

Margin-Based Deep Learning Networks for Human Activity Recognition.

Sensors (Basel, Switzerland)
Human activity recognition (HAR) is a popular and challenging research topic, driven by a variety of applications. More recently, with significant progress in the development of deep learning networks for classification tasks, many researchers have m...

A Smartphone Lightweight Method for Human Activity Recognition Based on Information Theory.

Sensors (Basel, Switzerland)
Smartphones have emerged as a revolutionary technology for monitoring everyday life, and they have played an important role in Human Activity Recognition (HAR) due to its ubiquity. The sensors embedded in these devices allows recognizing human behavi...

Human activity recognition using magnetic induction-based motion signals and deep recurrent neural networks.

Nature communications
Recognizing human physical activities using wireless sensor networks has attracted significant research interest due to its broad range of applications, such as healthcare, rehabilitation, athletics, and senior monitoring. There are critical challeng...

Predicting Optimal Hypertension Treatment Pathways Using Recurrent Neural Networks.

International journal of medical informatics
BACKGROUND: In ambulatory care settings, physicians largely rely on clinical guidelines and guideline-based clinical decision support (CDS) systems to make decisions on hypertension treatment. However, current clinical evidence, which is the knowledg...

Fuzzy support vector machine-based personalizing method to address the inter-subject variance problem of physiological signals in a driver monitoring system.

Artificial intelligence in medicine
Physiological signals can be utilized to monitor conditions of a driver, but the inter-subject variance of physiological signals can degrade the classification accuracy of the monitoring system. Personalization of the system using the data of a teste...

Human respiration monitoring using infrared thermography and artificial intelligence.

Biomedical physics & engineering express
The respiration rate (RR) is the most vital parameter used for the determination of human health. The most widely adopted techniques, used to monitor the RR are contact in nature and face many drawbacks. This paper reports the use of Infrared Thermog...

A Noninvasive Glucose Monitoring SoC Based on Single Wavelength Photoplethysmography.

IEEE transactions on biomedical circuits and systems
Conventional glucose monitoring methods for the growing numbers of diabetic patients around the world are invasive, painful, costly and, time-consuming. Complications aroused due to the abnormal blood sugar levels in diabetic patients have created th...

Development of Invisible Sensors and a Machine-Learning-Based Recognition System Used for Early Prediction of Discontinuous Bed-Leaving Behavior Patterns.

Sensors (Basel, Switzerland)
This paper presents a novel bed-leaving sensor system for real-time recognition of bed-leaving behavior patterns. The proposed system comprises five pad sensors installed on a bed, a rail sensor inserted in a safety rail, and a behavior pattern recog...