Bayesian Multicategorical Soft Data Fusion for Human–Robot Collaboration

Ahmed, Nisar; Sample, Eric; Campbell, Mark

doi:10.1109/tro.2012.2214556

Cited by 53 publications

(68 citation statements)

References 26 publications

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“…According to (1), O 2 appears in only two forms in the blood -it is either bound to hemoglobin or dissolved in the blood. Equation (1) [17].…”

Section: Problem Statementmentioning

confidence: 99%

“…Other related problems are sensor scheduling [10], [21] and filtering with intermittent measurements [20] but these works only handle continuous measurements. In robotic applications, techniques have been developed to use both kinds of measurements through information fusion [1], [4], [24] and bounded distributions [18]; however, the former works do not incorporate system dynamics whereas the latter approach results in unwieldy distributions that require approximations. Finally, a similar problem to the one addressed in this paper can be solved using particle filters [3]; yet, particle filters may suffer from particledeprivation problems in high-dimensional spaces whereas the context-aware filter provides exact estimates regardless of dimensionality.…”

Section: Introductionmentioning

confidence: 99%

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Estimation of Blood Oxygen Content Using Context-Aware Filtering

Ivanov

Atanasov

Weimer

et al. 2016

2016 ACM/IEEE 7th International Conference on Cyber-Physical Systems (ICCPS)

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In this paper we address the problem of estimating the blood oxygen concentration in children during surgery.Currently, the oxygen content can only be measured through invasive means such as drawing blood from the patient. In this work, we attempt to perform estimation by only using other non-invasive measurements (e.g., fraction of oxygen in inspired air, volume of inspired air) collected during surgery. Although models mapping these measurements to blood oxygen content contain multiple parameters that vary widely across patients, the non-invasive measurements can be used to provide binary information about whether the oxygen concentration is rising or dropping. This information can then be incorporated in a context-aware filter that is used to combine regular continuous measurements with discrete detection events in order to improve estimation. We evaluate the filter using real-patient data collected over the last decade at the Children's Hospital of Philadelphia and show that it is a promising approach for the estimation of unobservable physiological variables. Abstract-In this paper we address the problem of estimating the blood oxygen concentration in children during surgery. Currently, the oxygen content can only be measured through invasive means such as drawing blood from the patient. In this work, we attempt to perform estimation by only using other non-invasive measurements (e.g., fraction of oxygen in inspired air, volume of inspired air) collected during surgery. Although models mapping these measurements to blood oxygen content contain multiple parameters that vary widely across patients, the non-invasive measurements can be used to provide binary information about whether the oxygen concentration is rising or dropping. This information can then be incorporated in a context-aware filter that is used to combine regular continuous measurements with discrete detection events in order to improve estimation. We evaluate the filter using real-patient data collected over the last decade at the Children's Hospital of Philadelphia and show that it is a promising approach for the estimation of unobservable physiological variables. Disciplines Computer Engineering | Computer Sciences

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“…According to (1), O 2 appears in only two forms in the blood -it is either bound to hemoglobin or dissolved in the blood. Equation (1) [17].…”

Section: Problem Statementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimation of Blood Oxygen Content Using Context-Aware Filtering

Ivanov

Atanasov

Weimer

et al. 2016

2016 ACM/IEEE 7th International Conference on Cyber-Physical Systems (ICCPS)

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“…see for instance [5][6][7][8], although Bayesian fusion of hard UGS data with statistically uncharacterized soft HDO observations has not been previously considered for road network target localization. In this work, we assume HDOs have a mobile computing interface which allows them to report time-stamped information to UAVs through wireless link; at a minimum, a human can be thought of as 'mobile UGS', but in general could be capable of providing varying degrees of positive/negative information for physical/low-level phenomena (e.g.…”

Section: Related Workmentioning

confidence: 99%

Bayesian hidden Markov models for UAV-enabled target localization on road networks with soft-hard data

Ahmed¹,

Casbeer

Cao

et al. 2015

SPIE Proceedings

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This work addresses the problem of localizing a mobile intruder on a road network with a small UAV through fusion of event-based 'hard data' collected from a network of unattended ground sensors (UGS) and 'soft data' provided by human dismount operators (HDOs) whose statistical characteristics may be unknown. Current approaches to road network intruder detection/tracking have two key limitations: predictions become computationally expensive with highly uncertain target motions and sparse data, and they cannot easily accommodate fusion with uncertain sensor models. This work shows that these issues can be addressed in a practical and theoretically sound way using hidden Markov models (HMMs) within a comprehensive Bayesian framework. A formal procedure is derived for automatically generating sparse Markov chain approximations for target state dynamics based on standard motion assumptions. This leads to efficient online implementation via fast sparse matrix operations for non-Gaussian localization aboard small UAV platforms, and also leads to useful statistical insights about stochastic target dynamics that could be exploited by autonomous UAV guidance and control laws. The computational efficiency of the HMM can be leveraged in Rao-Blackwellized sampling schemes to address the problem of simultaneously fusing and characterizing uncertain HDO soft sensor data via hierarchical Bayesian estimation. Simulation results are provided to demonstrate the proposed approach.

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“…In [12][13][14][15][16][17], the algorithms are designed to overcome the limitation for the cases of object alignment. In [12][13][14], a specific model is proposed for estimating objects in the occluded area.…”

Section: Introductionmentioning

confidence: 99%

“…In [12][13][14], a specific model is proposed for estimating objects in the occluded area. Koch [15] and Ahmed et al [16] used data fusion to solve the problem of tracking the occluded object using a single laser scanner. An et al [17] eliminated measurements from moving objects on the grid map to estimate the exact road model.…”

Section: Introductionmentioning

confidence: 99%

Hough Transform-based Road Boundary Localization

Kim

Park

Kim

2017

IJFIS

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The advanced driver-assistance system (ADAS) is designed to help drivers while they are driving. To help the drivers, ADAS first comprehends the situation by analyzing the data obtained from the road surroundings. In this process, the road boundary is one of the most important targets to detect for safe driving, but is frequently misdetected on crowded roads. Therefore, a new method for robustly detecting road boundaries on crowded roads is presented in this paper. First, road-boundary detection using a standard Hough transform is described, and its limitations are shown. Second, the cause of the limitations is explained by the measurement model of a laser scanner. Then, the standard Hough transform is modified to reflect the measurement model of the laser scanner; this change reduces the effect of closed obstacles. Finally, the proposed method is tested in the real-world environment, and it shows better performance than previous works in crowded environments.

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Bayesian Multicategorical Soft Data Fusion for Human–Robot Collaboration

Cited by 53 publications

References 26 publications

Estimation of Blood Oxygen Content Using Context-Aware Filtering

Estimation of Blood Oxygen Content Using Context-Aware Filtering

Bayesian hidden Markov models for UAV-enabled target localization on road networks with soft-hard data

Hough Transform-based Road Boundary Localization

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