The biovid heat pain database data for the advancement and systematic validation of an automated pain recognition system

Walter, Steffen; Gruss, Sascha; Ehleiter, Hagen; Tan, Jun-Wen; Traue, Harald C.; Crawcour, Stephen; Werner, Philipp; Al-Hamadi, Ayoub; Andrade, Adriano O.

doi:10.1109/cybconf.2013.6617456

Cited by 193 publications

(228 citation statements)

References 17 publications

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“…Our experiments are conducted with the BioVid Heat Pain Database [7], [8] which was collected in a study with 90 participants. Heat pain was induced experimentally at the right arm (see Fig.…”

Section: Biovid Heat Pain Databasementioning

confidence: 99%

“…For adequate pain management, the assessment must be repeated regularly, especially if the patient cannot call for help by himself. For providing a continuous assessment, we work towards an automatic system for measurement and monitoring of pain [7], [8], which can alert hospital staff timely and provide additional information for the patient's medical record. In this paper, we present first results in multi-modal pain recognition from video (i. e. facial expression, head movement) and biomedical signals (i. e. ECG, GSR, EMG).…”

Section: Introductionmentioning

confidence: 99%

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Automatic Pain Recognition from Video and Biomedical Signals

Werner

Al-Hamadi

Niese

et al. 2014

2014 22nd International Conference on Pattern Recognition

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120

127

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How much does it hurt? Accurate assessment of pain is very important for selecting the right treatment, however current methods are not sufficiently valid and reliable in many cases. Automatic pain monitoring may help by providing an objective and continuous assessment. In this paper we propose an automatic pain recognition system combining information from video and biomedical signals, namely facial expression, head movement, galvanic skin response, electromyography and electrocardiogram. Using the BioVid Heat Pain Database, the system is evaluated in the task of pain detection showing significant improvement over the current state of the art. Further, we discuss the relevance of the modalities and compare person-specific and generic classification models.

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Section: Biovid Heat Pain Databasementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatic Pain Recognition from Video and Biomedical Signals

Werner

Al-Hamadi

Niese

et al. 2014

2014 22nd International Conference on Pattern Recognition

Self Cite

120

127

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show abstract

“…In Section II, we give a short presentation of the BioVid Heat pain database [20] upon which the present work is built. Section III provides a thorough description of the features extracted in the scope of the development of the pain recognition system in an offline scenario.…”

Section: Introductionmentioning

confidence: 99%

Methods for Person-Centered Continuous Pain Intensity Assessment From Bio-Physiological Channels

Kächele

Thiam

Amirian

et al. 2016

IEEE J. Sel. Top. Signal Process.

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In this work, we present methods for the personalization of a system for the continuous estimation of pain intensity from bio-physiological channels. We investigate various ways to estimate the similarity of persons and to retrieve the most informative ones using meta information, personality traits and machine learning techniques. Given this information, specialized classifiers can be created that are both, more efficient in terms of complexity and training times and also more accurate than classifiers trained on the complete data. To capture the most information in the different bio-physiological channels, we cover a broad spectrum of different feature extraction algorithms. Furthermore, we show that the system is capable of running in real-time and discuss issues that arise when dealing with incremental data processing. In extensive experiments we verify the validity of our approach.

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“…Facial videos were also recorded for all datsets. Another interesting multi-modal database is the BioVid Heat Pain database [60]. The tEMG, zEMG, cEMG, ECG, GSR, and EEG signals were collected along with facial videos from 86 subjects during exposure to painful heat stimuli.…”

Section: Emotion Recognitionmentioning

confidence: 99%

EMG Pattern Recognition in the Era of Big Data and Deep Learning

Phinyomark

Scheme

2018

BDCC

191

105

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Abstract:The increasing amount of data in electromyographic (EMG) signal research has greatly increased the importance of developing advanced data analysis and machine learning techniques which are better able to handle "big data". Consequently, more advanced applications of EMG pattern recognition have been developed. This paper begins with a brief introduction to the main factors that expand EMG data resources into the era of big data, followed by the recent progress of existing shared EMG data sets. Next, we provide a review of recent research and development in EMG pattern recognition methods that can be applied to big data analytics. These modern EMG signal analysis methods can be divided into two main categories: (1) methods based on feature engineering involving a promising big data exploration tool called topological data analysis; and (2) methods based on feature learning with a special emphasis on "deep learning". Finally, directions for future research in EMG pattern recognition are outlined and discussed.

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The biovid heat pain database data for the advancement and systematic validation of an automated pain recognition system

Cited by 193 publications

References 17 publications

Automatic Pain Recognition from Video and Biomedical Signals

Automatic Pain Recognition from Video and Biomedical Signals

Methods for Person-Centered Continuous Pain Intensity Assessment From Bio-Physiological Channels

EMG Pattern Recognition in the Era of Big Data and Deep Learning

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