Video2Report: A Video Database for Automatic Reporting of Medical Consultancy Sessions

Schiphorst, Laura; Doyran, Metehan; Molenaar, Sabine; Salah, Albert Ali; Brinkkemper, Sjaak

doi:10.1109/fg47880.2020.00020

Cited by 2 publications

(9 citation statements)

References 20 publications

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“…A classifier that is able to recognize medical actions performed in a GP's office can support the administrative work of GPs, not by diagnosing disease or by detecting anomalies, but by serving the medical reporting of the consultation. The classifier and its results are also discussed in [22].…”

Section: Case Study: Medical Consultationsmentioning

confidence: 99%

“…For the latter, actions that require subjects to (partially) undress were excluded to preserve the privacy of subjects participating in the recordings. The following medical actions were included in the dataset, in decreasing occurrence order in the guidelines [22]: Blood pressure measurement (BPM), Palpation abdomen (PaA), Percussion abdomen (PeA), Auscultation lungs (AL), Auscultation heart (AH), and Auscultation abdomen (AA).…”

Section: Case Study: Medical Consultationsmentioning

confidence: 99%

“…Dataset creation. The videos [22] were recorded with four subjects (three female, one male), who all played the roles of both GP and patient. Since GPs examine one patient at a time, exactly two subjects appear in all videos.…”

Section: Case Study: Medical Consultationsmentioning

confidence: 99%

“…Stethoscopes were used during recordings, both a red and black one to introduce variation. Finally, three cameras from three different angles were used, which is visualized in [22].…”

Section: Case Study: Medical Consultationsmentioning

confidence: 99%

“…By placing them in different positions and thus acquiring three different angles, the chances of occlusion are reduced. A total of 451 videos were recorded, 73.6% of which included a single action (those videos contained frames without any action as well), the rest contained sequences [22].…”

Section: Case Study: Medical Consultationsmentioning

confidence: 99%

See 4 more Smart Citations

Reference Method for the Development of Domain Action Recognition Classifiers: The Case of Medical Consultations

Molenaar

Schiphorst

Doyran

et al. 2020

Enterprise, Business-Process and Information Systems Modeling

Self Cite

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Advances in human action recognition and interaction recognition enable the reliable execution of action classification tasks through machine learning algorithms. However, no systematic approach for developing such classifiers exists and since actions vary between domains, appropriate and usable datasets are uncommon. In this paper, we propose a reference method that assists non-experts in building classifiers for domain action recognition. To demonstrate feasibility, we instantiate it in a case study in the medical domain that concerns the recognition of basic actions of general practitioners. The developed classifier is effective, as it shows a prediction accuracy of 75.6% for the medical action classification task and of more than 90% for three related classification tasks. The study shows that the method can be applied to a specific activity context and that the resulting classifier has an acceptable prediction accuracy. In the future, fine-tuning of the method parameters will endorse the applicability to other domains.

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Section: Case Study: Medical Consultationsmentioning

confidence: 99%

Section: Case Study: Medical Consultationsmentioning

confidence: 99%

Section: Case Study: Medical Consultationsmentioning

confidence: 99%

“…Stethoscopes were used during recordings, both a red and black one to introduce variation. Finally, three cameras from three different angles were used, which is visualized in [22].…”

Section: Case Study: Medical Consultationsmentioning

confidence: 99%

Section: Case Study: Medical Consultationsmentioning

confidence: 99%

See 3 more Smart Citations

Reference Method for the Development of Domain Action Recognition Classifiers: The Case of Medical Consultations

Molenaar

Schiphorst

Doyran

et al. 2020

Enterprise, Business-Process and Information Systems Modeling

Self Cite

View full text Add to dashboard Cite

show abstract

Didn't see that coming: a survey on non-verbal social human behavior forecasting

Barquero¹,

Núñez²,

Escalera³

et al. 2022

Preprint

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Non-verbal social human behavior forecasting has increasingly attracted the interest of the research community in recent years. Its direct applications to human-robot interaction and socially-aware human motion generation make it a very attractive field. In this survey, we define the behavior forecasting problem for multiple interactive agents in a generic way that aims at unifying the fields of social signals prediction and human motion forecasting, traditionally separated. We hold that both problem formulations refer to the same conceptual problem, and identify many shared fundamental challenges: future stochasticity, context awareness, history exploitation, etc. We also propose a taxonomy that comprises methods published in the last 5 years in a very informative way and describes the current main concerns of the community with regard to this problem. In order to promote further research on this field, we also provide a summarized and friendly overview of audiovisual datasets featuring non-acted social interactions. Finally, we describe the most common metrics used in this task and their particular issues.

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Video2Report: A Video Database for Automatic Reporting of Medical Consultancy Sessions

Cited by 2 publications

References 20 publications

Reference Method for the Development of Domain Action Recognition Classifiers: The Case of Medical Consultations

Reference Method for the Development of Domain Action Recognition Classifiers: The Case of Medical Consultations

Didn't see that coming: a survey on non-verbal social human behavior forecasting

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