111 results on '"Tanvi Banerjee"'
Search Results
102. Knowledge-Driven Personalized Contextual mHealth Service for Asthma Management in Children
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Krishnaprasad Thirunarayan, Surendra Marupudi, Tanvi Banerjee, Shalini G. Forbis, Vaikunth Sridharan, Amit P. Sheth, and Pramod Anantharam
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Service (systems architecture) ,medicine.medical_specialty ,Multimedia ,Computer science ,Public health ,Context (language use) ,Asthma management ,computer.software_genre ,Data science ,Digital health ,Personalization ,Quality of life (healthcare) ,medicine ,mHealth ,computer - Abstract
Wide adoption of smartphones and availability of low-cost sensors has resulted in seamless and continuous monitoring of physiology, environment, and public health notifications. However, personalized digital health and patient empowerment can become a reality only if the complex multisensory and multimodal data is processed within the patient context. Contextual processing of patient data along with personalized medical knowledge can lead to actionable information for better and timely decisions. We present a system called kHealth capable of aggregating multisensory and multimodal data from sensors (passive sensing) and answers to questionnaire (active sensing) from patients with asthma. We present our preliminary data analysis comprising data collected from real patients highlighting the challenges in deploying such an application. The results show strong promise to derive actionable information using a combination of physiological indicators from active and passive sensors that can help doctors determine more precisely the cause, severity, and control level of asthma. Information synthesized from kHealth can be used to alert patients and caregivers for seeking timely clinical assistance to better manage asthma and improve their quality of life.
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- 2015
103. Gender-Based Violence in 140 Characters or Fewer: A #BigData Case Study of Twitter
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Valerie L. Shalin, Hemant Purohit, Tanvi Banerjee, Amit P. Sheth, Andrew J. Hampton, Nayanesh Bhandutia, U.S. National Science Foundation, and Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis)
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FOS: Computer and information sciences ,J.4 ,Computer Networks and Communications ,050109 social psychology ,02 engineering and technology ,Social issues ,Public opinion ,H.1.2 ,Computer Science - Computers and Society ,Computers and Society (cs.CY) ,0202 electrical engineering, electronic engineering, information engineering ,0501 psychology and cognitive sciences ,Social media ,Sociology ,Public engagement ,Sociocultural evolution ,Social and Information Networks (cs.SI) ,Social computing ,business.industry ,05 social sciences ,Public institution ,Computer Science - Social and Information Networks ,computational social science ,gender-based violence ,social media ,quantitative analysis ,qualitative analysis ,citizen sensing ,public awareness ,public attitude ,policy ,intervention campaign ,Public relations ,Human-Computer Interaction ,Computer Science ,Social Science ,020201 artificial intelligence & image processing ,Computational sociology ,business - Abstract
Humanitarian and public institutions are increasingly relying on data from social media sites to measure public attitude, and provide timely public engagement. Such engagement supports the exploration of public views on important social issues such as gender-based violence (GBV). In this study, we examine Big (Social) Data consisting of nearly fourteen million tweets collected from the Twitter platform over a period of ten months to analyze public opinion regarding GBV, highlighting the nature of tweeting practices by geographical location and gender. The exploitation of Big Data requires the techniques of Computational Social Science to mine insight from the corpus while accounting for the influence of both transient events and sociocultural factors. We reveal public awareness regarding GBV tolerance and suggest opportunities for intervention and the measurement of intervention effectiveness assisting both governmental and non-governmental organizations in policy development
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- 2015
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104. Parameter estimation of three phase induction motor using gravitational search algorithm for IFOC
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Jitendranath Bera, Tanvi Banerjee, and G. Sarkar
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Engineering ,Vector control ,business.industry ,Estimation theory ,Rotor (electric) ,Gravitational search algorithm ,law.invention ,Control theory ,law ,business ,MATLAB ,computer ,Induction motor ,computer.programming_language - Abstract
This Paper presents dynamic parameter estimation of a three phase induction motor based on gravitational search algorithm (GSA) and Indirect Field Oriented Control (IFOC) methodology. The IFOC and Induction motor are modelled in MATLAB (Simulink) environment and the GSA algorithm has adopted for this parameter estimation purposes. The parameters like rotor resistance, time varying inductances etc. are estimated to have a better control over the running of the induction motor. It has been seen that the proposed method is efficient enough with enhanced accuracy in the estimation of parameters.
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- 2015
105. Monitoring patients in hospital beds using unobtrusive depth sensors
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Marilyn Rantz, James M. Keller, Tanvi Banerjee, Mihail Popescu, Marjorie Skubic, and Moein Enayati
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business.industry ,Movement ,Real-time computing ,Beds ,Activity recognition ,Image Interpretation, Computer-Assisted ,Humans ,Medicine ,Computer vision ,Artificial intelligence ,Fall detection ,business ,Algorithms ,Monitoring, Physiologic - Abstract
We present an approach for patient activity recognition in hospital rooms using depth data collected using a Kinect sensor. Depth sensors such as the Kinect ensure that activity segmentation is possible during day time as well as night while addressing the privacy concerns of patients. It also provides a technique to remotely monitor patients in a non-intrusive manner. An existing fall detection algorithm is currently generating fall alerts in several rooms in the University of Missouri Hospital (MUH). In this paper we describe a technique to reduce false alerts such as pillows falling off the bed or equipment movement. We do so by detecting the presence of the patient in the bed for the times when the fall alert is generated. We test our algorithm on 96 hours obtained in two hospital rooms from MUH.
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- 2014
106. Evolving a fuzzy goal-driven strategy for the game of Geister: An exercise in teaching computational intelligence
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Tanvi Banerjee, James M. Keller, and Andrew R. Buck
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Adaptive neuro fuzzy inference system ,Neuro-fuzzy ,Artificial neural network ,Computer science ,business.industry ,Computational intelligence ,Machine learning ,computer.software_genre ,Unobservable ,Fuzzy logic ,Competition (economics) ,Artificial intelligence ,business ,computer - Abstract
This paper presents an approach to designing a strategy for the game of Geister using the three main research areas of computational intelligence. We use a goal-based fuzzy inference system to evaluate the utility of possible actions and a neural network to estimate unobservable features (the true natures of the opponent ghosts). Finally, we develop a coevolutionary algorithm to learn the parameters of the strategy. The resulting autonomous gameplay agent was entered in a global competition sponsored by the IEEE Computational Intelligence Society and finished second among eight participating teams.
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- 2014
107. Building a framework for recognition of activities of daily living from depth images using fuzzy logic
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Marjorie Skubic, James M. Keller, and Tanvi Banerjee
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Activity recognition ,Measure (data warehouse) ,Activities of daily living ,Computer science ,business.industry ,Artificial intelligence ,Machine learning ,computer.software_genre ,business ,human activities ,computer ,Fuzzy logic - Abstract
Complex activities such as instrumental activities of daily living (IADLs) can be identified by creating a hierarchical model of fuzzy rules. In this work, we present a framework to model a specific IADL - "making the bed". For this activity recognition, the need for a three level Fuzzy Inference System (FIS) model is shown. Simple features such as bounding box parameters were extracted from the foreground images and combined with 3D features extracted from the Kinect depth data. This was then fed as input to the three layered FIS for further analysis. Data collected from several participants were tested and evaluated. Such a framework can be used to model several other IADLS as well as basic activities of daily living (ADLs). Analysis of ADLs can be used to compare daily patterns in older adults to measure changes in behavior. This can then be used to predict health changes to assist older adults in leading independent lifestyles for longer time periods.
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- 2014
108. Automated fall detection with quality improvement 'rewind' to reduce falls in hospital rooms
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Marjorie Skubic, Marilyn Rantz, Susan D. Scott, Mihail Popescu, Tanvi Banerjee, and Erin Cattoor
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Data collection ,Quality management ,business.industry ,Hospitalized patients ,Computer science ,Patient privacy ,Real-time computing ,Poison control ,Automation ,Quality Improvement ,Article ,Hospitalization ,Patients' Rooms ,Humans ,Accidental Falls ,Fall detection ,Shoe laces ,business ,Gerontology ,General Nursing - Abstract
The purpose of this study was to test the implementation of a fall detection and “rewind” privacy-protecting technique using the Microsoft ® Kinect ™ to not only detect but prevent falls from occurring in hospitalized patients. Kinect sensors were placed in six hospital rooms in a step-down unit and data were continuously logged. Prior to implementation with patients, three researchers performed a total of 18 falls (walking and then falling down or falling from the bed) and 17 non-fall events (crouching down, stooping down to tie shoe laces, and lying on the floor). All falls and non-falls were correctly identified using automated algorithms to process Kinect sensor data. During the first 8 months of data collection, processing methods were perfected to manage data and provide a “rewind” method to view events that led to falls for post-fall quality improvement process analyses. Preliminary data from this feasibility study show that using the Microsoft Kinect sensors provides detection of falls, fall risks, and facilitates quality improvement after falls in real hospital environments unobtrusively, while taking into account patient privacy. [ Journal of Gerontological Nursing, 40 (1), 13–17.]
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- 2013
109. Sit-to-stand measurement for in-home monitoring using voxel analysis
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Tanvi Banerjee, James M. Keller, Carmen Abbott, and Marjorie Skubic
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Computer science ,Movement ,Posture ,Monitoring, Ambulatory ,computer.software_genre ,Sitting ,Ellipse ,Accelerometer ,Motion capture ,Models, Biological ,Activity recognition ,Health Information Management ,Voxel ,Accelerometry ,Activities of Daily Living ,Humans ,Computer vision ,Electrical and Electronic Engineering ,Aged ,Orientation (computer vision) ,business.industry ,Ranging ,Computer Science Applications ,Accidental Falls ,Artificial intelligence ,business ,computer ,Biotechnology - Abstract
We present algorithms to segment the activities of sitting and standing, and identify the regions of sit-to-stand (STS) transitions in a given image sequence. As a means of fall risk assessment, we propose methods to measure STS time using the 3-D modeling of a human body in voxel space as well as ellipse fitting algorithms and image features to capture orientation of the body. The proposed algorithms were tested on ten older adults with ages ranging from 83 to 97. Two techniques in combination yielded the best results, namely the voxel height in conjunction with the ellipse fit. Accurate STS time was computed on various STSs and verified using a marker-based motion capture system. This application can be used as part of a continuous video monitoring system in the homes of older adults and can provide valuable information to help detect fall risk and enable early interventions.
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- 2013
110. Detecting foreground disambiguation of depth images using fuzzy logic
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Marjorie Skubic, Tanvi Banerjee, and James M. Keller
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Ground truth ,Fuzzy rule ,business.industry ,Feature extraction ,Frame (networking) ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Pattern recognition ,Filter (signal processing) ,Fuzzy logic ,Skeletonization ,Minimum bounding box ,Computer vision ,Artificial intelligence ,business ,Mathematics - Abstract
We present a unique occlusion and foreground overlap detection technique from depth sensor data using a fuzzy rule-based system. Features such as bounding box parameters and skeletonization were extracted from the foreground images and then input to the Fuzzy Inference System. Overlap and occlusion confidence measures were taken for each frame in the image sequence and compared against the extracted ground truth. This technique can help filter out occluded regions in the image sequence which, in an Eldercare environment, can then be used to compute accurate estimates of fall risk parameters such as stride time, stride length, and walking speed on a daily basis in in order to monitor the well-being of older adults in an ambient assisted living facility.
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- 2013
111. Testing an in-home gait assessment tool for older adults
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Marjorie Skubic, Marilyn Rantz, Jarod T. Giger, Tanvi Banerjee, Fang Wang, Jean Krampe, Erik E. Stone, and Wenqing Dai
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Male ,medicine.medical_specialty ,Engineering ,Remote patient monitoring ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,Monitoring, Ambulatory ,Sensitivity and Specificity ,Image Interpretation, Computer-Assisted ,medicine ,Daily living ,Humans ,Gait ,Aged, 80 and over ,business.industry ,Everyday activities ,Retirement community ,Reproducibility of Results ,Actigraphy ,Test (assessment) ,Medical services ,Gait analysis ,Physical therapy ,Female ,business ,Algorithms ,Locomotion - Abstract
In this paper, we present results of an automatic vision-based gait assessment tool, using two cameras. Elderly residents from TigerPlace, a retirement community, were recruited to participate in the validation and test of the system in scripted scenarios representing everyday activities. The residents were first tested on a GAITRite mat, an electronic walkway that captures footfalls, and with inexpensive web cameras recording images. The extracted gait parameters from the camera system were compared with the GAITRite; excellent agreement was achieved. The residents then participated in the scenarios, with only the cameras recording. We found that the residents displayed different gait patterns during the realistic scenarios compared to the GAITRite runs. This finding provides support of the importance and advantage of continuous gait assessment in a daily living environment. Results on 4 elderly participants are included in the paper.
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- 2009
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