Towards context-aware hard/soft information fusion: Incorporating situationally qualified human observations into a fusion process for intelligence analysis

Jenkins, Michael; Gross, Geoff A.; Bisantz, Ann M.; Nagi, Rakesh

doi:10.1109/cogsima.2011.5753757

Cited by 13 publications

(22 citation statements)

References 27 publications

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“…Even when the human "sensors" are trained and trusted personnel of known identity, it is difficult to categorize the accuracy of their observations due to factors such as stress, time pressure, and even the weight of objects that they are carrying [14].…”

Section: Challenges Of the Evolving Paradigmmentioning

confidence: 99%

Human cognitive and perceptual factors in JDL level 4 hard/soft data fusion

2012

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Utilization of human participants as "soft sensors" is becoming increasingly important for gathering information related to a wide range of phenomena including natural and man-made disasters, environmental changes over time, crime prevention, and other roles of the "citizen scientist." The ubiquity of advanced mobile devices is facilitating the role of humans as "hybrid sensor platforms", allowing them to gather data (e.g. video, still images, GPS coordinates), annotate it based on their intuitive human understanding, and upload it using existing infrastructure and social networks. However, this new paradigm presents many challenges related to source characterization, effective tasking, and utilization of massive quantities of physical sensor, human-based, and hybrid hard/soft data in a manner that facilitates decision making instead of simply amplifying information overload.In the Joint Directors of Laboratories (JDL) data fusion process model, "level 4" fusion is a meta-process that attempts to improve performance of the entire fusion system through effective source utilization. While there are well-defined approaches for tasking and categorizing physical sensors, these methods fall short when attempting to effectively utilize a hybrid group of physical sensors and human observers. While physical sensor characterization can rely on statistical models of performance (e.g. accuracy, reliability, specificity, etc.) under given conditions, "soft" sensors add the additional challenges of characterizing human performance, tasking without inducing bias, and effectively balancing strengths and weaknesses of both human and physical sensors. This paper addresses the challenges of the evolving human-centric fusion paradigm and presents cognitive, perceptual, and other human factors that help to understand, categorize, and augment the roles and capabilities of humans as observers in hybrid systems.

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Section: Challenges Of the Evolving Paradigmmentioning

confidence: 99%

Human cognitive and perceptual factors in JDL level 4 hard/soft data fusion

2012

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“…The process of combining data derived from different sensors is defined as sensor fusion. Developments towards a framework for sensor fusion have been made (Llinas and Hall 1998) and it has proven to be useful in different research areas such as intelligence analysis (Jenkins et al 2011) or indoor localisation (Martin et al 2010).…”

Section: Sensor Fusionmentioning

confidence: 99%

Mobility Collector

Prelipcean

Gidófalvi

Susilo

2014

Journal of Location Based Services

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Despite the availability of mobile positioning technologies and scientists' interests in tracking, modelling and predicting the movements of individuals and populations, these technologies are seldom efficiently used. The continuous changes in mobile positioning and other sensor technologies overburden scientists who are interested in data collection with the task of developing, implementing and testing tracking algorithms and their efficiency in terms of battery consumption. To this extent, this article proposes an adaptive, battery conscious tracking algorithm that collects trajectory data fused with accelerometer data and presents Mobility Collector, which is a prototype platform that, using the tracking algorithm, can produce highly configurable, off-the-shelf, multi-user tracking systems suitable for research purposes. The applicability of the tracking system is tested within the transport science domain by collecting labelled movement traces and related motion data, i.e. accelerometer data and derived information (number of steps and other useful movement features based on temporal aggregates of the raw readings) to develop and evaluate a method that automatically classifies the transportation mode of users with a 90.8% prediction accuracy.

QC 20150312

SPO

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“…Literature has begun to emerge in some domains 2 . Since 2002, there have been scholarly works published about Level 5 of the JDL data fusion process model 3,4,5,6 as well as about the domains of sense-making 7 , intelligence 8,9,10 , distributed cognition 11 , risk analysis 12,13,14,15 , collaborative sensing 16 , and situation awareness 17 .…”

Section: Introductionmentioning

confidence: 99%

Application of the JDL data fusion process model to hard/soft information fusion in the condition monitoring of aircraft

Bernardo

2014

SPIE Proceedings

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Hard/soft information fusion has been proposed as a way to enhance diagnostic capability for the condition monitoring of machinery. However, there is a limited understanding of where hard/soft information fusion could and should be applied in the condition monitoring of aircraft. Condition-based maintenance refers to the philosophy of performing maintenance when the need arises, based upon indicators of deterioration in the condition of the machinery. The addition of the multisensory capability of human cognition to electronic sensors may create a fuller picture of machinery condition.Since 1988, the Joint Directors of Laboratories (JDL) data fusion process model has served as a framework for information fusion research. Advances are described in the application of hard/soft information fusion in condition monitoring using terms that condition-based maintenance professionals in aviation will recognize. Emerging literature on hard/soft information fusion in condition monitoring is organized into the levels of the JDL data fusion process model. Gaps in the literature are identified, and the author's ongoing research is discussed. Future efforts will focus on building domain-specific frameworks and experimental design, which may provide a foundation for improving flight safety, increasing mission readiness, and reducing the cost of maintenance operations.

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Towards context-aware hard/soft information fusion: Incorporating situationally qualified human observations into a fusion process for intelligence analysis

Cited by 13 publications

References 27 publications

Human cognitive and perceptual factors in JDL level 4 hard/soft data fusion

Human cognitive and perceptual factors in JDL level 4 hard/soft data fusion

Mobility Collector

Application of the JDL data fusion process model to hard/soft information fusion in the condition monitoring of aircraft

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