The SenseEmotion Database: A Multimodal Database for the Development and Systematic Validation of an Automatic Pain- and Emotion-Recognition System

In our modern industrial society the group of the older (generation 65+) is constantly growing. Many subjects of this group are severely affected by their health and are suffering from disability and pain. The problem with chronic illness and pain is that it lowers the patient’s quality of life, and therefore accurate pain assessment is needed to facilitate effective pain management and treatment. In the future, automatic pain monitoring may enable health care professionals to assess and manage pain in a more and more objective way. To this end, the goal of our SenseEmotion project is to develop automatic pain- and emotion-recognition systems for successful assessment and effective personalized management of pain, particularly for the generation 65+. In this paper the recently created SenseEmotion Database for pain- vs. emotion-recognition is presented. Data of 45 healthy subjects is collected to this database. For each subject approximately 30 min of multimodal sensory data has been recorded. For a comprehensive understanding of pain and affect three rather different modalities of data are included in this study: biopotentials, camera images of the facial region, and, for the first time, audio signals. Heat stimulation is applied to elicit pain, and affective image stimuli accompanied by sound stimuli are used for the elicitation of emotional states.

[1]  Stefanie Rukavina,et al.  Recognition of Intensive Valence and Arousal Affective States via Facial Electromyographic Activity in Young and Senior Adults , 2016, PloS one.

[2]  Jennifer Healey.  Physiological Sensing of Emotion , 2015 .

[3]  P. Lang.  The emotion probe. Studies of motivation and attention. , 1995, The American psychologist.

[4]  Sascha Gruss.  Schmerzerkennung anhand psychophysiologischer Signale mithilfe maschineller Lerner , 2015 .

[5]  Naoki Ikegami,et al.  Prevalence of inappropriate medication using Beers criteria in Japanese long-term care facilities , 2006, BMC geriatrics.

[6]  Tobias Baur,et al.  The social signal interpretation (SSI) framework: multimodal signal processing and recognition in real-time , 2013, ACM Multimedia.

[7]  M. Berger,et al.  Pain in elderly people with severe dementia: A systematic review of behavioural pain assessment tools , 2006, BMC geriatrics.

[8]  Michael Serpell.  Handbook of Pain Management , 2008 .

[9]  P. Lang.  International affective picture system (IAPS) : affective ratings of pictures and instruction manual , 2005 .

[10]  Gustavo Moreira da Silva,et al.  Automatic pain quantification using autonomic parameters , 2014 .

[11]  Andrew Moore,et al.  Fortnightly review: Treating acute pain in hospital , 1997, BMJ.

[12]  N. Lowe,et al.  A critical review of visual analogue scales in the measurement of clinical phenomena. , 1990, Research in nursing & health.

[13]  Patrick Thiam,et al.  Adaptive confidence learning for the personalization of pain intensity estimation systems , 2017, Evol. Syst..

[14]  Ayoub Al-Hamadi,et al.  Automatic Pain Assessment with Facial Activity Descriptors , 2017, IEEE Transactions on Affective Computing.

[15]  H. Traue,et al.  Pain Intensity Recognition Rates via Biopotential Feature Patterns with Support Vector Machines , 2015, PloS one.

[16]  Chris Robertson,et al.  Human Papilloma Virus (HPV) Oral Prevalence in Scotland (HOPSCOTCH): A Feasibility Study in Dental Settings , 2016, PloS one.

[17]  M. Bradley,et al.  Emotion and motivation I: defensive and appetitive reactions in picture processing. , 2001, Emotion.

[18]  H C Traue,et al.  [Erratum to: Mimic activity of differentiated pain intensities. Correlation of characteristics of Facial Action Coding System and electromyography]. , 2016, Schmerz.

[19]  M. Johnson,et al.  Circulating microRNAs in Sera Correlate with Soluble Biomarkers of Immune Activation but Do Not Predict Mortality in ART Treated Individuals with HIV-1 Infection: A Case Control Study , 2015, PloS one.

[20]  Ayoub Al-Hamadi,et al.  The biovid heat pain database data for the advancement and systematic validation of an automated pain recognition system , 2013, 2013 IEEE International Conference on Cybernetics (CYBCO).

[21]  C. Jensen,et al.  The influence of electrode position on bipolar surface electromyogram recordings of the upper trapezius muscle , 2004, European Journal of Applied Physiology and Occupational Physiology.

[22]  Jason Williams,et al.  Emotion Recognition Using Bio-sensors: First Steps towards an Automatic System , 2004, ADS.

[23]  Randolph C. Arnau,et al.  Pain and Emotion: Effects of Affective Picture Modulation , 2001, Psychosomatic medicine.

[24]  Jeffrey F. Cohn,et al.  Automatic detection of pain intensity , 2012, ICMI '12.

[25]  M. Bradley,et al.  Looking at pictures: affective, facial, visceral, and behavioral reactions. , 1993, Psychophysiology.

[26]  J. Rhudy,et al.  Affective modulation of nociception at spinal and supraspinal levels. , 2005, Psychophysiology.

[27]  H. Kehlet,et al.  Acute pain control and accelerated postoperative surgical recovery. , 1999, The Surgical clinics of North America.

[28]  M. Bradley,et al.  Measuring emotion: the Self-Assessment Manikin and the Semantic Differential. , 1994, Journal of behavior therapy and experimental psychiatry.

[29]  Elisabeth André,et al.  Emotion recognition based on physiological changes in music listening , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[30]  P. Lang,et al.  Affective judgment and psychophysiological response: Dimensional covariation in the evaluation of pictorial stimuli. , 1989 .

[31]  N. Frijda,et al.  Emotions and respiratory patterns: review and critical analysis. , 1994, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.