当前位置： SCI文献检索 > IEEE Journal of Biomedical and Health Informatics期刊下所有文献 > Deep Learning for Fall Detection: Three-Dimensional CNN Combined With LSTM on Video Kinematic Data.

Deep Learning for Fall Detection: Three-Dimensional CNN Combined With LSTM on Video Kinematic Data.

Abstract：

:Fall detection is an important public healthcare problem. Timely detection could enable instant delivery of medical service to the injured. A popular nonintrusive solution for fall detection is based on videos obtained through ambient camera, and the corresponding methods usually require a large dataset to train a classifier and are inclined to be influenced by the image quality. However, it is hard to collect fall data and instead simulated falls are recorded to construct the training dataset, which is restricted to limited quantity. To address these problems, a three-dimensional convolutional neural network (3-D CNN) based method for fall detection is developed, which only uses video kinematic data to train an automatic feature extractor and could circumvent the requirement for large fall dataset of deep learning solution. 2-D CNN could only encode spatial information, and the employed 3-D convolution could extract motion feature from temporal sequence, which is important for fall detection. To further locate the region of interest in each frame, a long short-term memory (LSTM) based spatial visual attention scheme is incorporated. Sports dataset Sports-1 M with no fall examples is employed to train the 3-D CNN, which is then combined with LSTM to train a classifier with fall dataset. Experiments have verified the proposed scheme on fall detection benchmark with high accuracy as 100%. Superior performance has also been obtained on other activity databases.

journal_name

IEEE J Biomed Health Inform

journal_title

IEEE journal of biomedical and health informatics

authors

Lu N,Wu Y,Feng L,Song J

doi

10.1109/JBHI.2018.2808281

subject

Has Abstract

pub_date

2019-01-01 00:00:00

pages

314-323

issue

1

eissn

2168-2194

issn

2168-2208

journal_volume

23

pub_type

杂志文章

相关文献

IEEE Journal of Biomedical and Health Informatics文献大全
  • Blood Vessel Segmentation of Fundus Images by Major Vessel Extraction and Subimage Classification.

    abstract：:This paper presents a novel three-stage blood vessel segmentation algorithm using fundus photographs. In the first stage, the green plane of a fundus image is preprocessed to extract a binary image after high-pass filtering, and another binary image from the morphologically reconstructed enhanced image for the vessel ...

    journal_title：IEEE journal of biomedical and health informatics

    pub_type： 杂志文章

    doi：10.1109/JBHI.2014.2335617

    authors： Roychowdhury S,Koozekanani DD,Parhi KK

    更新日期：2015-05-01 00:00:00

  • Blind end-member and abundance extraction for multispectral fluorescence lifetime imaging microscopy data.

    abstract：:This paper proposes a new blind end-member and abundance extraction (BEAE) method for multispectral fluorescence lifetime imaging microscopy (m-FLIM) data. The chemometrical analysis relies on an iterative estimation of the fluorescence decay end-members and their abundances. The proposed method is based on a linear m...

    journal_title：IEEE journal of biomedical and health informatics

    pub_type： 杂志文章

    doi：10.1109/JBHI.2013.2279335

    authors： Gutierrez-Navarro O,Campos-Delgado DU,Arce-Santana ER,Mendez MO,Jo JA

    更新日期：2014-03-01 00:00:00

  • Three-Dimensional Needle Shape Estimation in TRUS-Guided Prostate Brachytherapy Using 2-D Ultrasound Images.

    abstract：:In this paper, we propose an automated method to reconstruct the three-dimensional (3-D) needle shape during needle insertion procedures using only 2-D transverse ultrasound (US) images. Using a set of transverse US images, image processing and random sample consensus are used to locate the needle within each image an...

    journal_title：IEEE journal of biomedical and health informatics

    pub_type： 杂志文章

    doi：10.1109/JBHI.2015.2477829

    authors： Waine M,Rossa C,Sloboda R,Usmani N,Tavakoli M

    更新日期：2016-11-01 00:00:00

  • A machine learning approach to improve contactless heart rate monitoring using a webcam.

    abstract：:Unobtrusive, contactless recordings of physiological signals are very important for many health and human-computer interaction applications. Most current systems require sensors which intrusively touch the user's skin. Recent advances in contact-free physiological signals open the door to many new types of application...

    journal_title：IEEE journal of biomedical and health informatics

    pub_type： 杂志文章

    doi：10.1109/JBHI.2013.2291900

    authors： Monkaresi H,Calvo RA,Yan H

    更新日期：2014-07-01 00:00:00

  • Fast QRS Detection and ECG Compression Based on Signal Structural Analysis.

    abstract：OBJECTIVE:This paper presents a fast approach to detect QRS complexes based on a simple analysis of the temporal ECG structure. METHODS:The ECG is processed through several steps involving noise removal, feature detection, and feature analysis. The obtained feature set, which holds most of the ECG information while re...

    journal_title：IEEE journal of biomedical and health informatics

    pub_type： 杂志文章

    doi：10.1109/JBHI.2018.2792404

    authors： Burguera A

    更新日期：2019-01-01 00:00:00

  • A Realistic Framework for Investigating Decision Making in the Brain With High Spatiotemporal Resolution Using Simultaneous EEG/fMRI and Joint ICA.

    abstract：:Human decision making is a multidimensional construct, driven by a complex interplay between external factors, internal biases, and computational capacity constraints. Here, we propose a layered approach to experimental design in which multiple tasks-from simple to complex-with additional layers of complexity introduc...

    journal_title：IEEE journal of biomedical and health informatics

    pub_type： 杂志文章

    doi：10.1109/JBHI.2016.2590434

    authors： Kyathanahally SP,Franco-Watkins A,Zhang X,Calhoun VD,Deshpande G

    更新日期：2017-05-01 00:00:00

  • Design comorbidity portfolios to improve treatment cost prediction of asthma using machine learning.

    abstract：:Comorbidity is an important factor to consider when trying to predict the cost of treating asthma patients. When an asthmatic patient suffered from comorbidity, the cost of treating such a patient becomes dependent on the nature of the comorbidity. Therefore, lack of recognition of comorbidity on asthmatic patient pos...

    journal_title：IEEE journal of biomedical and health informatics

    pub_type： 杂志文章

    doi：10.1109/JBHI.2020.3034092

    authors： Luo L,Yu X,Yong Z,Li C,Gu Y

    更新日期：2020-10-27 00:00:00

  • Acoustic signal classification of breathing movements to virtually aid breath regulation.

    abstract：:Monitoring breath and identifying breathing movements have settled importance in many biomedical research areas, especially in the treatment of those with breathing disorders, e.g., lung cancer patients. Moreover, virtual reality (VR) revolution and their implementations on ubiquitous hand-held devices have a lot of i...

    journal_title：IEEE journal of biomedical and health informatics

    pub_type： 杂志文章

    doi：10.1109/JBHI.2013.2244901

    authors： Abushakra A,Faezipour M

    更新日期：2013-03-01 00:00:00

  • Statistical Metamodeling and Sequential Design of Computer Experiments to Model Glyco-Altered Gating of Sodium Channels in Cardiac Myocytes.

    abstract：:Glycan structures account for up to 35% of the mass of cardiac sodium ( Nav ) channels. To question whether and how reduced sialylation affects Nav activity and cardiac electrical signaling, we conducted a series of in vitro experiments on ventricular apex myocytes under two different glycosylation conditions, reduced...

    journal_title：IEEE journal of biomedical and health informatics

    pub_type： 杂志文章

    doi：10.1109/JBHI.2015.2458791

    authors： Du D,Yang H,Ednie AR,Bennett ES

    更新日期：2016-09-01 00:00:00

  • A learning scheme for reach to grasp movements: on EMG-based interfaces using task specific motion decoding models.

    abstract：:A learning scheme based on random forests is used to discriminate between different reach to grasp movements in 3-D space, based on the myoelectric activity of human muscles of the upper-arm and the forearm. Task specificity for motion decoding is introduced in two different levels: Subspace to move toward and object ...

    journal_title：IEEE journal of biomedical and health informatics

    pub_type： 杂志文章

    doi：10.1109/JBHI.2013.2259594

    authors： Liarokapis MV,Artemiadis PK,Kyriakopoulos KJ,Manolakos ES

    更新日期：2013-09-01 00:00:00

  • A Deep Convolutional Neural Network-Based Framework for Automatic Fetal Facial Standard Plane Recognition.

    abstract：:Ultrasound imaging has become a prevalent examination method in prenatal diagnosis. Accurate acquisition of fetal facial standard plane (FFSP) is the most important precondition for subsequent diagnosis and measurement. In the past few years, considerable effort has been devoted to FFSP recognition using various hand-...

    journal_title：IEEE journal of biomedical and health informatics

    pub_type： 杂志文章

    doi：10.1109/JBHI.2017.2705031

    authors： Yu Z,Tan EL,Ni D,Qin J,Chen S,Li S,Lei B,Wang T

    更新日期：2018-05-01 00:00:00

  • Near-realistic mobile exergames with wireless wearable sensors.

    abstract：:Exergaming is expanding as an option for sedentary behavior in childhood/adult obesity and for extra exercise for gamers. This paper presents the development process for a mobile active sports exergame with near-realistic motions through the usage of body-wearable sensors. The process begins by collecting a dataset sp...

    journal_title：IEEE journal of biomedical and health informatics

    pub_type： 杂志文章

    doi：10.1109/JBHI.2013.2293674

    authors： Mortazavi B,Nyamathi S,Lee SI,Wilkerson T,Ghasemzadeh H,Sarrafzadeh M

    更新日期：2014-03-01 00:00:00

  • A Fuzzy Kernel Motion Classifier for Autonomous Stroke Rehabilitation.

    abstract：:Autonomous poststroke rehabilitation systems which can be deployed outside hospital with no or reduced supervision have attracted increasing amount of research attentions due to the high expenditure associated with the current inpatient stroke rehabilitation systems. To realize an autonomous systems, a reliable patien...

    journal_title：IEEE journal of biomedical and health informatics

    pub_type： 杂志文章

    doi：10.1109/JBHI.2015.2430524

    authors： Zhang Z,Liparulo L,Panella M,Gu X,Fang Q

    更新日期：2016-05-01 00:00:00

  • Classification and Quantification of Emphysema Using a Multi-Scale Residual Network.

    abstract：:Automated tissue classification is an essential step for quantitative analysis and treatment of emphysema. Although many studies have been conducted in this area, there still remain two major challenges. First, different emphysematous tissue appears in different scales, which we call "inter-class variations." Second, ...

    journal_title：IEEE journal of biomedical and health informatics

    pub_type： 杂志文章

    doi：10.1109/JBHI.2018.2890045

    authors： Peng L,Lin L,Hu H,Li H,Chen Q,Ling X,Wang D,Han X,Iwamoto Y,Chen YW

    更新日期：2019-11-01 00:00:00

  • EMG-Torque Relation in Chronic Stroke: A Novel EMG Complexity Representation With a Linear Electrode Array.

    abstract：:This study examines the electromyogram (EMG)-torque relation for chronic stroke survivors using a novel EMG complexity representation. Ten stroke subjects performed a series of submaximal isometric elbow flexion tasks using their affected and contralateral arms, respectively, while a 20-channel linear electrode array ...

    journal_title：IEEE journal of biomedical and health informatics

    pub_type： 杂志文章

    doi：10.1109/JBHI.2016.2626399

    authors： Zhang X,Wang D,Yu Z,Chen X,Li S,Zhou P

    更新日期：2017-11-01 00:00:00

  • Objective study of sensor relevance for automatic cough detection.

    abstract：:The development of a system for the automatic, objective, and reliable detection of cough events is a need underlined by the medical literature for years. The benefit of such a tool is clear as it would allow the assessment of pathology severity in chronic cough diseases. Even though some approaches have recently repo...

    journal_title：IEEE journal of biomedical and health informatics

    pub_type： 杂志文章

    doi：10.1109/jbhi.2013.2239303

    authors： Drugman T,Urbain J,Bauwens N,Chessini R,Valderrama C,Lebecque P,Dutoit T

    更新日期：2013-05-01 00:00:00

  • Enhancing Predictive Accuracy of Cardiac Autonomic Neuropathy Using Blood Biochemistry Features and Iterative Multitier Ensembles.

    abstract：:Blood biochemistry attributes form an important class of tests, routinely collected several times per year for many patients with diabetes. The objective of this study is to investigate the role of blood biochemistry for improving the predictive accuracy of the diagnosis of cardiac autonomic neuropathy (CAN) progressi...

    journal_title：IEEE journal of biomedical and health informatics

    pub_type： 杂志文章

    doi：10.1109/JBHI.2014.2363177

    authors： Abawajy J,Kelarev A,Chowdhury MU,Jelinek HF

    更新日期：2016-01-01 00:00:00

  • Performance Analysis of Gyroscope and Accelerometer Sensors for Seismocardiography-Based Wearable Pre-Ejection Period Estimation.

    abstract：OBJECTIVE:Systolic time intervals, such as the pre-ejection period (PEP), are important parameters for assessing cardiac contractility that can be measured non-invasively using seismocardiography (SCG). Recent studies have shown that specific points on accelerometer- and gyroscope-based SCG signals can be used for PEP ...

    journal_title：IEEE journal of biomedical and health informatics

    pub_type： 杂志文章

    doi：10.1109/JBHI.2019.2895775

    authors： Shandhi MMH,Semiz B,Hersek S,Goller N,Ayazi F,Inan OT

    更新日期：2019-11-01 00:00:00

  • Near real-time retroflexion detection in colonoscopy.

    abstract：:Colonoscopy is the most popular screening tool for colorectal cancer. Recent studies reported that retroflexion during colonoscopy helped to detect more polyps. Retroflexion is an endoscope maneuver that enables visualization of internal mucosa along the shaft of the endoscope, enabling visualization of the mucosa are...

    journal_title：IEEE journal of biomedical and health informatics

    pub_type： 杂志文章

    doi：10.1109/TITB.2012.2226595

    authors： Wang Y,Tavanapong W,Wong J,Oh J,de Groen PC

    更新日期：2013-01-01 00:00:00

  • 2D and 3D CT Radiomic Features Performance Comparison in Characterization of Gastric Cancer: A Multi-center Study.

    abstract：OBJECTIVE:Radiomics, an emerging tool for medical image analysis, is potential towards precisely characterizing gastric cancer (GC). Whether using one-slice 2D annotation or whole-volume 3D annotation remains a long-time debate, especially for heterogeneous GC. We comprehensively compared 2D and 3D radiomic features' r...

    journal_title：IEEE journal of biomedical and health informatics

    pub_type： 杂志文章

    doi：10.1109/JBHI.2020.3002805

    authors： Meng L,Dong D,Chen X,Fang M,Wang R,Li J,Liu Z,Tian J

    更新日期：2020-06-16 00:00:00

  • A Continuously Updated, Computationally Efficient Stress Recognition Framework Using Electroencephalogram (EEG) by Applying Online Multitask Learning Algorithms (OMTL).

    abstract：:Recognizing the factors that cause stress is a crucial step toward early detection of stressors. In this regard, several studies make an effort to recognize individuals' stress using an Electroencephalogram (EEG). However, current EEG-based stress recognition frameworks have several drawbacks. First, they are mostly d...

    journal_title：IEEE journal of biomedical and health informatics

    pub_type： 杂志文章

    doi：10.1109/JBHI.2018.2870963

    authors： Jebelli H,Mahdi Khalili M,Lee S

    更新日期：2019-09-01 00:00:00

  • Unobtrusive in-home detection of time spent out-of-home with applications to loneliness and physical activity.

    abstract：:Loneliness is a common condition in elderly associated with severe health consequences including increased mortality, decreased cognitive function, and poor quality of life. Identifying and assisting lonely individuals is therefore increasingly important-especially in the home setting-as the very nature of loneliness ...

    journal_title：IEEE journal of biomedical and health informatics

    pub_type： 杂志文章

    doi：10.1109/JBHI.2013.2294276

    authors： Petersen J,Austin D,Kaye JA,Pavel M,Hayes TL

    更新日期：2014-09-01 00:00:00

  • How to Extract More Information With Less Burden: Fundus Image Classification and Retinal Disease Localization With Ophthalmologist Intervention.

    abstract：:Image classification using convolutional neural networks (CNNs) outperforms other state-of-the-art methods. Moreover, attention can be visualized as a heatmap to improve the explainability of results of a CNN. We designed a framework that can generate heatmaps reflecting lesion regions precisely. We generated initial ...

    journal_title：IEEE journal of biomedical and health informatics

    pub_type： 杂志文章

    doi：10.1109/JBHI.2020.3011805

    authors： Meng Q,Hashimoto Y,Satoh S

    更新日期：2020-12-01 00:00:00

  • Enhancing Heart-Beat-Based Security for mHealth Applications.

    abstract：:In heart-beat-based security, a security key is derived from the time difference between consecutive heart beats (the inter-pulse interval, IPI), which may, subsequently, be used to enable secure communication. While heart-beat-based security holds promise in mobile health (mHealth) applications, there currently exist...

    journal_title：IEEE journal of biomedical and health informatics

    pub_type： 杂志文章

    doi：10.1109/JBHI.2015.2496151

    authors： Seepers RM,Strydis C,Sourdis I,De Zeeuw CI

    更新日期：2017-01-01 00:00:00

  • cuProCell: GPU-Accelerated Analysis of Cell Proliferation With Flow Cytometry Data.

    abstract：:The investigation of cell proliferation can provide useful insights for the comprehension of cancer progression, resistance to chemotherapy and relapse. To this aim, computational methods and experimental measurements based on in vivo label-retaining assays can be coupled to explore the dynamic behavior of tumoral cel...

    journal_title：IEEE journal of biomedical and health informatics

    pub_type： 杂志文章

    doi：10.1109/JBHI.2020.3005423

    authors： Nobile MS,Nisoli E,Vlachou T,Spolaor S,Cazzaniga P,Mauri G,Pelicci PG,Besozzi D

    更新日期：2020-11-01 00:00:00

  • Detection of wrinkle frames in endoluminal videos using betweenness centrality measures for images.

    abstract：:Intestinal contractions are one of the most important events to diagnose motility pathologies of the small intestine. When visualized by wireless capsule endoscopy (WCE), the sequence of frames that represents a contraction is characterized by a clear wrinkle structure in the central frames that corresponds to the fol...

    journal_title：IEEE journal of biomedical and health informatics

    pub_type： 杂志文章

    doi：10.1109/JBHI.2014.2304179

    authors： Seguí S,Drozdzal M,Zaytseva E,Malagelada C,Azpiroz F,Radeva P,Vitrià J

    更新日期：2014-11-01 00:00:00

  • Multi-Hypergraph Learning for Incomplete Multimodality Data.

    abstract：:Multi-modality data convey complementary information that can be used to improve the accuracy of prediction models in disease diagnosis. However, effectively integrating multi-modality data remains a challenging problem, especially when the data are incomplete. For instance, more than half of the subjects in the Alzhe...

    journal_title：IEEE journal of biomedical and health informatics

    pub_type： 杂志文章

    doi：10.1109/JBHI.2017.2732287

    authors： Liu M,Gao Y,Yap PT,Shen D

    更新日期：2018-07-01 00:00:00

  • A Visually Interpretable Deep Learning Framework for Histopathological Image-based Skin Cancer Diagnosis.

    abstract：:Owing to the high incidence rate and the severe impact of skin cancer, the precise diagnosis of malignant skin tumors is a significant goal, especially considering treatment is normally effective if the tumor is detected early. Limited published histopathological image sets and the lack of an intuitive correspondence ...

    journal_title：IEEE journal of biomedical and health informatics

    pub_type： 杂志文章

    doi：10.1109/JBHI.2021.3052044

    authors： Jiang S,Li H,Jin Z

    更新日期：2021-01-15 00:00:00

  • Intuitionistic Center-Free FCM Clustering for MR Brain Image Segmentation.

    abstract：:In this paper, an intuitionistic center-free fuzzy c-means clustering method (ICFFCM) is proposed for magnetic resonance (MR) brain image segmentation. First, in order to suppress the effect of noise in MR brain images, a pixel-to-pixel similarity with spatial information is defined. Then, for the purpose of handling ...

    journal_title：IEEE journal of biomedical and health informatics

    pub_type： 杂志文章

    doi：10.1109/JBHI.2018.2884208

    authors： Bai X,Zhang Y,Liu H,Wang Y

    更新日期：2019-09-01 00:00:00

  • A decision-support framework for promoting independent living and ageing well.

    abstract：:Artificial intelligence and decision support systems offer a plethora of health monitoring capabilities in ambient assisted living environment. Continuous assessment of health indicators for elderly people living on their own is of utmost importance, so as to prolong their independence and quality of life. Slow varyin...

    journal_title：IEEE journal of biomedical and health informatics

    pub_type： 杂志文章

    doi：10.1109/JBHI.2014.2336757

    authors： Billis AS,Papageorgiou EI,Frantzidis CA,Tsatali MS,Tsolaki AC,Bamidis PD

    更新日期：2015-01-01 00:00:00