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New Sensor Data Structuring for Deeper Feature Extraction in Human Activity Recognition.

Sensors (Basel, Switzerland)
For the effective application of thriving human-assistive technologies in healthcare services and human-robot collaborative tasks, computing devices must be aware of human movements. Developing a reliable real-time activity recognition method for the...

Artificial Intelligence Methods for Rapid Vascular Access Aneurysm Classification in Remote or In-Person Settings.

Blood purification
BACKGROUND: Innovations in artificial intelligence (AI) have proven to be effective contributors to high-quality health care. We examined the beneficial role AI can play in noninvasively grading vascular access aneurysms to reduce high-morbidity even...

A Deep Learning Approach to Diagnosing Multiple Sclerosis from Smartphone Data.

IEEE journal of biomedical and health informatics
Multiple sclerosis (MS) affects the central nervous system with a wide range of symptoms. MS can, for example, cause pain, changes in mood and fatigue, and may impair a person's movement, speech and visual functions. Diagnosis of MS typically involve...

Low-Cost and Device-Free Human Activity Recognition Based on Hierarchical Learning Model.

Sensors (Basel, Switzerland)
Human activity recognition (HAR) has been a vital human-computer interaction service in smart homes. It is still a challenging task due to the diversity and similarity of human actions. In this paper, a novel hierarchical deep learning-based methodol...

AI-based smartphone apps for risk assessment of skin cancer need more evaluation and better regulation.

British journal of cancer
Smartphone applications ("apps") with artificial intelligence (AI) algorithms are increasingly used in healthcare. Widespread adoption of these apps must be supported by a robust evidence-base and app manufacturers' claims appropriately regulated. Cu...

HOPES: An Integrative Digital Phenotyping Platform for Data Collection, Monitoring, and Machine Learning.

Journal of medical Internet research
The collection of data from a personal digital device to characterize current health conditions and behaviors that determine how an individual's health will evolve has been called digital phenotyping. In this paper, we describe the development of and...

Deep Neural Networks for Image-Based Dietary Assessment.

Journal of visualized experiments : JoVE
Due to the issues and costs associated with manual dietary assessment approaches, automated solutions are required to ease and speed up the work and increase its quality. Today, automated solutions are able to record a person's dietary intake in a mu...

Data-efficient and weakly supervised computational pathology on whole-slide images.

Nature biomedical engineering
Deep-learning methods for computational pathology require either manual annotation of gigapixel whole-slide images (WSIs) or large datasets of WSIs with slide-level labels and typically suffer from poor domain adaptation and interpretability. Here we...

LSTM Networks Using Smartphone Data for Sensor-Based Human Activity Recognition in Smart Homes.

Sensors (Basel, Switzerland)
Human Activity Recognition (HAR) employing inertial motion data has gained considerable momentum in recent years, both in research and industrial applications. From the abstract perspective, this has been driven by an acceleration in the building of ...

Deep learning identification for citizen science surveillance of tiger mosquitoes.

Scientific reports
Global monitoring of disease vectors is undoubtedly becoming an urgent need as the human population rises and becomes increasingly mobile, international commercial exchanges increase, and climate change expands the habitats of many vector species. Tr...