Topics

A cross-dataset deep learning-based classifier for people fall detection and identification.

07:00 EST 7th December 2019 | BioPortfolio

Summary of "A cross-dataset deep learning-based classifier for people fall detection and identification."

Fall detection is an important problem for vulnerable sectors of the population such as elderly people, who frequently live alone. Note that a fall can be very dangerous for them if they cannot ask for help. Hence, in those situations, an automatic system that detected and informed to emergency services about the fall and subject identity could help to save lives. This way, they would know not only when but also who to help. Thus, our objective is to develop a new approach, based on deep learning, for fall detection and people identification that can be used in different datasets without any fine-tuning of the model parameters.

Affiliation

Journal Details

This article was published in the following journal.

Name: Computer methods and programs in biomedicine
ISSN: 1872-7565
Pages: 105265

Links

DeepDyve research library

PubMed Articles [26864 Associated PubMed Articles listed on BioPortfolio]

A vision-based approach for fall detection using multiple cameras and convolutional neural networks: A case study using the UP-Fall detection dataset.

The automatic recognition of human falls is currently an important topic of research for the computer vision and artificial intelligence communities. In image analysis, it is common to use a vision-ba...

GoogLeNet based Ensemble FCNet Classifier for Focal Liver Lesion Diagnosis.

Transfer learning techniques are recently preferred for the computer aided diagnosis (CAD) of variety of diseases, as it makes the classification feasible from limited training dataset. In this work, ...

Diagnosing Glaucoma with Spectral-domain Optical Coherence Tomography Using Deep Learning Classifier.

PRéCIS:: An SD-OCT based deep learning system detected glaucomatous structural change with high sensitivity and specificity. It outperformed the clinical diagnostic parameters in discriminating glauc...

Fish-Pak: Fish species dataset from Pakistan for visual features based classification.

Fishes are most diverse group of vertebrates with more than 33000 species. These are identified based on several visual characters including their shape, color and head. It is difficult for the common...

TAP: A static analysis model for PHP vulnerabilities based on token and deep learning technology.

With the widespread usage of Web applications, the security issues of source code are increasing. The exposed vulnerabilities seriously endanger the interests of service providers and customers. There...

Clinical Trials [12567 Associated Clinical Trials listed on BioPortfolio]

Diagnostic Performance of Deep Learning for Angle Closre

Primary angle closure diseases (PACD) are commonly seen in Asia. In clinical practice, gonioscopy is the gold standard for angle width classification in PACD patietns. However, gonioscopy ...

Association of Eye With Hepatobiliary Disorders :Qualitative Analysis Via Deep Learning

Artificial Intelligence and Machine Learning techniques may provide insight into exploring the potential covert association behind and reveal some early ocular architecture changes in indi...

Deep-learning Based Location of Spine CT

CT-guided epidural steroid injection (ESI) thrives among interventional radiologists. Although X-ray fluoroscopy still remains as the gold standard to guide ESI, CT guidance has the merits...

Diagnostic Yield of Deep Learning Based Denoising MRI in Cushing's Disease

Negative MRI findings may occur in up to 40% of cases of ACTH producing microadenomas. The aim of the study is to evaluate if detection of ACTH producing microadenomas can be increased usi...

Generalizability and Spread of an Evidenced-based Fall Prevention Toolkit: Fall TIPS

The goal of our project is to evaluate the effectiveness of the Fall TIPS program with regard to inpatient falls and fall-related injuries.

Medical and Biotech [MESH] Definitions

Teaching strategy of shared learning based cross-discipline experiences and placements.

Process in which individuals take the initiative, in diagnosing their learning needs, formulating learning goals, identifying resources for learning, choosing and implementing learning strategies and evaluating learning outcomes (Knowles, 1975)

International collective of humanitarian organizations led by volunteers and guided by its Congressional Charter and the Fundamental Principles of the International Red Cross Movement, to provide relief to victims of disaster and help people prevent, prepare for, and respond to emergencies.

A MACHINE LEARNING paradigm used to make predictions about future instances based on a given set of labeled paired input-output training (sample) data.

A MACHINE LEARNING paradigm used to make predictions about future instances based on a given set of unlabeled paired input-output training (sample) data.

Quick Search


DeepDyve research library

Searches Linking to this Article