当前位置： SCI文献检索 > IEEE Journal of Biomedical and Health Informatics期刊下所有文献 > Unobtrusive in-home detection of time spent out-of-home with applications to loneliness and physical activity.

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 often makes it difficult to detect by traditional methods. One critical component in assessing loneliness unobtrusively is to measure time spent out-of-home, as loneliness often presents with decreased physical activity, decreased motor functioning, and a decline in activities of daily living, all of which may cause decrease in the amount of time spent outside the home. Using passive and unobtrusive in-home sensing technologies, we have developed a methodology for detecting time spent out-of-home based on logistic regression. Our approach was both sensitive (0.939) and specific (0.975) in detecting time out-of-home across over 41,000 epochs of data collected from four subjects monitored for at least 30 days each in their own homes. In addition to linking time spent out-of-home to loneliness, (r = -0.44, p = 0.011) as measured by the UCLA Loneliness Index, we demonstrate its usefulness in other applications such as uncovering general behavioral patterns of elderly and exploring the link between time spent out-of-home and physical activity ( r = 0.415, p = 0.031), as measured by the Berkman Social Disengagement Index.

journal_name

IEEE J Biomed Health Inform

journal_title

IEEE journal of biomedical and health informatics

authors

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

doi

10.1109/JBHI.2013.2294276

subject

Has Abstract

pub_date

2014-09-01 00:00:00

pages

1590-6

issue

5

eissn

2168-2194

issn

2168-2208

journal_volume

18

pub_type

杂志文章

相关文献

IEEE Journal of Biomedical and Health Informatics文献大全
  • 2D/3D-Display Auto-Adjustment Switch System.

    abstract：:Recently, 2-D/3-D switchable displays have become the mainstream in 3-D display technologies, and people can now watch 3-D movies with a naked 2-D/3-D switchable display at home. However, some studies have indicated that people might encounter visual fatigue after enjoying a 3-D film in the theater. Although 2-D/3-D s...

    journal_title：IEEE journal of biomedical and health informatics

    pub_type： 杂志文章

    doi：10.1109/JBHI.2017.2700794

    authors： Lin BS,Wu PJ,Chen CY

    更新日期：2018-05-01 00:00:00

  • 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

  • Simultaneous Clustering and Feature Weighting Using Multiobjective Optimization for Identifying Functionally Similar miRNAs.

    abstract：:MicroRNAs (miRNAs) are a type of RNAs, which are responsible for monitoring the gene expression values. Recent research asserts that miRNAs form some clustering on chromosomes. The miRNAs belonging to a particular cluster are highly similar in terms of their activity and they are termed as "coregulated" miRNAs. The cu...

    journal_title：IEEE journal of biomedical and health informatics

    pub_type： 杂志文章

    doi：10.1109/JBHI.2017.2784898

    authors： Saha S,Acharya S,K K,Miriyala S

    更新日期：2018-09-01 00:00:00

  • Optimized Multistable Stochastic Resonance for the Enhancement of Pituitary Microadenoma in MRI.

    abstract：:Magnetic resonance imaging (MRI) is the modality of choice as far as imaging diagnosis of pathologies in the pituitary gland is concerned. Furthermore, the advent of dynamic contrast enhanced (DCE) has enhanced the capability of this modality in detecting minute benign but endocrinologically significant tumors called ...

    journal_title：IEEE journal of biomedical and health informatics

    pub_type： 杂志文章

    doi：10.1109/JBHI.2017.2715078

    authors： Singh M,Verma A,Sharma N

    更新日期：2018-05-01 00:00:00

  • Neonatal Heart and Lung Sound Quality Assessment for Robust Heart and Breathing Rate Estimation for telehealth Applications.

    abstract：:With advances in digital stethoscopes, internet of things, signal processing and machine learning, chest sounds can be easily collected and transmitted to the cloud for remote monitoring and diagnosis. However, low quality of recordings complicates remote monitoring and diagnosis, particularly for neonatal care. This ...

    journal_title：IEEE journal of biomedical and health informatics

    pub_type： 杂志文章

    doi：10.1109/JBHI.2020.3047602

    authors： Grooby E,He J,Kiewsky J,Fattahi D,Zhou L,King A,Ramanathan A,Malhotra A,Dumont GA,Marzbanrad F

    更新日期：2020-12-28 00:00:00

  • Attention-Guided 3D-CNN Framework for Glaucoma Detection and Structural-Functional Association Using Volumetric Images.

    abstract：:The direct analysis of 3D Optical Coherence Tomography (OCT) volumes enables deep learning models (DL) to learn spatial structural information and discover new bio-markers that are relevant to glaucoma. Downsampling 3D input volumes is the state-of-art solution to accommodate for the limited number of training volumes...

    journal_title：IEEE journal of biomedical and health informatics

    pub_type： 杂志文章

    doi：10.1109/JBHI.2020.3001019

    authors： George Y,Antony BJ,Ishikawa H,Wollstein G,Schuman JS,Garnavi R

    更新日期：2020-12-01 00:00:00

  • Deep Learning for Health Informatics.

    abstract：:With a massive influx of multimodality data, the role of data analytics in health informatics has grown rapidly in the last decade. This has also prompted increasing interests in the generation of analytical, data driven models based on machine learning in health informatics. Deep learning, a technique with its founda...

    journal_title：IEEE journal of biomedical and health informatics

    pub_type： 杂志文章,评审

    doi：10.1109/JBHI.2016.2636665

    authors： Ravi D,Wong C,Deligianni F,Berthelot M,Andreu-Perez J,Lo B,Yang GZ

    更新日期：2017-01-01 00:00:00

  • The Removal of EOG Artifacts From EEG Signals Using Independent Component Analysis and Multivariate Empirical Mode Decomposition.

    abstract：:The recorded electroencephalography (EEG) signals are usually contaminated by electrooculography (EOG) artifacts. In this paper, by using independent component analysis (ICA) and multivariate empirical mode decomposition (MEMD), the ICA-based MEMD method was proposed to remove EOG artifacts (EOAs) from multichannel EE...

    journal_title：IEEE journal of biomedical and health informatics

    pub_type： 杂志文章

    doi：10.1109/JBHI.2015.2450196

    authors： Wang G,Teng C,Li K,Zhang Z,Yan X

    更新日期：2016-09-01 00:00:00

  • Detecting Suspected Pump Thrombosis in Left Ventricular Assist Devices via Acoustic Analysis.

    abstract：OBJECTIVE:Left ventricular assist devices (LVADs) fail in up to 10% of patients due to the development of pump thrombosis. Remote monitoring of patients with LVADs can enable early detection and, subsequently, treatment and prevention of pump thrombosis. We assessed whether acoustical signals measured on the chest of p...

    journal_title：IEEE journal of biomedical and health informatics

    pub_type： 杂志文章

    doi：10.1109/JBHI.2020.2966178

    authors： Semiz B,Hersek S,Pouyan MB,Partida C,Blazquez-Arroyo L,Selby V,Wieselthaler G,Rehg JM,Klein L,Inan OT

    更新日期：2020-07-01 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

  • Automatic Screening of Sleep Apnea Patients Based on the SpO2 Signal.

    abstract：OBJECTIVE:This paper presents a methodology to automatically screen for sleep apnea based on the detection of apnea and hypopnea events in the blood oxygen saturation (SpO2) signal. METHODS:It starts by detecting all desaturations in the SpO2 signal. From these desaturations, a total of 143 time-domain features are ex...

    journal_title：IEEE journal of biomedical and health informatics

    pub_type： 杂志文章

    doi：10.1109/JBHI.2018.2817368

    authors： Deviaene M,Testelmans D,Buyse B,Borzee P,Van Huffel S,Varon C

    更新日期：2019-03-01 00:00:00

  • A multiprocessing scheme for PET image pre-screening, noise reduction, segmentation and lesion partitioning.

    abstract：:Accurate segmentation and segmentation of lesions in PET images provide computer-aided procedures and doctors with parameters for tumour diagnosis, staging and prognosis. Currently, PET segmentation and lesion partitioning are manually measured by radiologists, which is time consuming and laborious, and tedious manual...

    journal_title：IEEE journal of biomedical and health informatics

    pub_type： 杂志文章

    doi：10.1109/JBHI.2020.3024563

    authors： Cui R,Chen Z,Wu J,Tan Y,Yu G

    更新日期：2020-09-18 00:00:00

  • Low-Dimensional Subject Representation-based Transfer Learning in EEG Decoding.

    abstract：:Recently, the advances in passive brain-computer interfaces (BCIs) based on electroencephalogram (EEG) have shed light on real-world neuromonitoring technologies. However, human variability in the EEG activities hinders the development of practical applications of EEG-based BCI. To tackle this problem, many transfer-l...

    journal_title：IEEE journal of biomedical and health informatics

    pub_type： 杂志文章

    doi：10.1109/JBHI.2020.3025865

    authors： Jeng PY,Wei CS,Jung TP,Wang LC

    更新日期：2020-09-22 00:00:00

  • Posture and activity recognition and energy expenditure estimation in a wearable platform.

    abstract：:The use of wearable sensors coupled with the processing power of mobile phones may be an attractive way to provide real-time feedback about physical activity and energy expenditure (EE). Here, we describe the use of a shoe-based wearable sensor system (SmartShoe) with a mobile phone for real-time recognition of variou...

    journal_title：IEEE journal of biomedical and health informatics

    pub_type： 杂志文章

    doi：10.1109/JBHI.2015.2432454

    authors： Sazonov E,Hegde N,Browning RC,Melanson EL,Sazonova NA

    更新日期：2015-07-01 00:00:00

  • Screening For Depression With Retrospectively Harvested Private Versus Public Text.

    abstract：:Depression is the leading cause of disability, often undiagnosed, and one of the most treatable mood disorders. As such, unobtrusively diagnosing depression is important. Many studies are starting to utilize machine learning for depression sensing from social media and Smartphone data to replace the survey instruments...

    journal_title：IEEE journal of biomedical and health informatics

    pub_type： 杂志文章

    doi：10.1109/JBHI.2020.2983035

    authors： Tlachac ML,Rundensteiner E

    更新日期：2020-11-01 00:00:00

  • AW-SDRLSE: Adaptive Weighting and Scalable Distance Regularized Level Set Evolution for Lymphoma Segmentation on PET Images.

    abstract：:Accurate lymphoma segmentation on Positron Emission Tomography (PET) images is of great importance for medical diagnoses, such as for distinguishing benign and malignant. To this end, this paper proposes an adaptive weighting and scalable distance regularized level set evolution (AW-SDRLSE) method for delineating lymp...

    journal_title：IEEE journal of biomedical and health informatics

    pub_type： 杂志文章

    doi：10.1109/JBHI.2020.3017546

    authors： Li S,Jiang H,Li H,Yao YD

    更新日期：2020-08-25 00:00:00

  • α-Satellite: An AI-Driven System and Benchmark Datasets for Dynamic COVID-19 Risk Assessment in the United States.

    abstract：:The fast evolving and deadly outbreak of coronavirus disease (COVID-19) has posed grand challenges to human society. To slow the spread of virus infections and better respond for community mitigation, by advancing capabilities of artificial intelligence (AI) and leveraging the large-scale and up-to-date data generated...

    journal_title：IEEE journal of biomedical and health informatics

    pub_type： 杂志文章

    doi：10.1109/JBHI.2020.3009314

    authors： Ye Y,Hou S,Fan Y,Zhang Y,Qian Y,Sun S,Peng Q,Ju M,Song W,Loparo K

    更新日期：2020-10-01 00:00:00

  • A Hybrid Global-Local Representation CNN Model for Automatic Cataract Grading.

    abstract：:Cataract is one of the most serious eye diseases leading to blindness. Early detection and treatment can reduce the rate of blindness in cataract patients. However, the professional knowledge of ophthalmologists is necessary for the clinical cataract detection. Therefore, the potential costs may make it difficult for ...

    journal_title：IEEE journal of biomedical and health informatics

    pub_type： 杂志文章

    doi：10.1109/JBHI.2019.2914690

    authors： Xu X,Zhang L,Li J,Guan Y,Zhang L

    更新日期：2020-02-01 00:00:00

  • A Global Covariance Descriptor for Nuclear Atypia Scoring in Breast Histopathology Images.

    abstract：:Nuclear atypia scoring is a diagnostic measure commonly used to assess tumor grade of various cancers, including breast cancer. It provides a quantitative measure of deviation in visual appearance of cell nuclei from those in normal epithelial cells. In this paper, we present a novel image-level descriptor for nuclear...

    journal_title：IEEE journal of biomedical and health informatics

    pub_type： 杂志文章

    doi：10.1109/JBHI.2015.2447008

    authors： Khan AM,Sirinukunwattana K,Rajpoot N

    更新日期：2015-09-01 00:00:00

  • Efficient and Effective Training of COVID-19 Classification Networks With Self-Supervised Dual-Track Learning to Rank.

    abstract：:Coronavirus Disease 2019 (COVID-19) has rapidly spread worldwide since first reported. Timely diagnosis of COVID-19 is crucial both for disease control and patient care. Non-contrast thoracic computed tomography (CT) has been identified as an effective tool for the diagnosis, yet the disease outbreak has placed tremen...

    journal_title：IEEE journal of biomedical and health informatics

    pub_type： 杂志文章

    doi：10.1109/JBHI.2020.3018181

    authors： Li Y,Wei D,Chen J,Cao S,Zhou H,Zhu Y,Wu J,Lan L,Sun W,Qian T,Ma K,Xu H,Zheng Y

    更新日期：2020-10-01 00:00:00

  • Tear Film Classification in Interferometry Eye Images Using Phylogenetic Diversity Indexes and Ripley's K Function.

    abstract：:Dry eye syndrome is one of the most frequently reported eye diseases in ophthalmological practice. The diagnosis of this disease is a challenging task due to its multifactorial etiology. One of the most applied tests is the manual classification of tear film images captured with the Doane interferometer. The interfere...

    journal_title：IEEE journal of biomedical and health informatics

    pub_type： 杂志文章

    doi：10.1109/JBHI.2020.3026940

    authors： da Cruz LB,Souza JC,de Paiva AC,de Almeida JDS,Junior GB,Aires KRT,Silva AC,Gattass M

    更新日期：2020-12-01 00:00:00

  • Laryngeal Tumor Detection and Classification in Endoscopic Video.

    abstract：:The development of the narrow-band imaging (NBI) has been increasing the interest of medical specialists in the study of laryngeal microvascular network to establish diagnosis without biopsy and pathological examination. A possible solution to this challenging problem is presented in this paper, which proposes an auto...

    journal_title：IEEE journal of biomedical and health informatics

    pub_type： 杂志文章

    doi：10.1109/JBHI.2014.2374975

    authors： Barbalata C,Mattos LS

    更新日期：2016-01-01 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

  • Modeling of Heart Rate Variability and Respiratory Muscle Activity in Organophosphate Poisoned Patients.

    abstract：:We propose an extended model of cardiovascular regulation to assess heart rate variability in patients poisoned with organophosphate during their treatment with mechanical ventilation. The model was modified to fit a population of 21 patients poisoned with organophosphorus compounds and undergoing mechanical ventilati...

    journal_title：IEEE journal of biomedical and health informatics

    pub_type： 杂志文章

    doi：10.1109/JBHI.2019.2894758

    authors： Salazar MB,Mauricio Hernandez A,Mananas MA,Cortes Daza C

    更新日期：2019-11-01 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

  • 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

  • Using Lexical Chains to Identify Text Difficulty: A Corpus Statistics and Classification Study.

    abstract：:Our goal is data-driven discovery of features for text simplification. In this paper, we investigate three types of lexical chains: exact, synonymous, and semantic. A lexical chain links semantically related words in a document. We examine their potential with a document-level corpus statistics study (914 texts) to es...

    journal_title：IEEE journal of biomedical and health informatics

    pub_type： 杂志文章

    doi：10.1109/JBHI.2018.2885465

    authors： Mukherjee P,Leroy G,Kauchak D

    更新日期：2019-09-01 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

  • An Adaptive, Data-Driven Personalized Advisor for Increasing Physical Activity.

    abstract：:In recent years, there has been growing interest in the use of fitness trackers and smartphone applications for promoting physical activity. Many of these applications use accelerometers to estimate the level of activity that users engage in and provide visual reports of a user's step counts. When provided, most recom...

    journal_title：IEEE journal of biomedical and health informatics

    pub_type： 杂志文章

    doi：10.1109/JBHI.2018.2879805

    authors： Li Z,Das S,Codella J,Hao T,Lin K,Maduri C,Chen CH

    更新日期：2019-05-01 00:00:00

  • Delta Features From Ambient Sensor Data are Good Predictors of Change in Functional Health.

    abstract：:Sensor systems can be deployed in the homes of older adults living alone for functional health assessments. Their information is very useful for health care specialists. The problem lies in developing person independent models while facing a large variability in behavior. We address this problem by, first, proposing a...

    journal_title：IEEE journal of biomedical and health informatics

    pub_type： 杂志文章

    doi：10.1109/JBHI.2016.2593980

    authors： Robben S,Englebienne G,Krose B

    更新日期：2017-07-01 00:00:00