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Prediction of pain intensity using multimedia data

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Abstract

Increased medical expenditure, improper treatment, decreased productivity and above all inadequate pain, assessment is the global challenging problem, causing a substantial burden on the individual, their family, and society which often leads to loss of interest in life. The facial expression variation generally reflects clue for the occurrence of pain. This offers a vital aspect for non-verbal patients who are not in a position to rate their pain intensity level. The hypothesis proposes the developing of a multimodal machine based tool which will assess pain intensity. A framework has been designed to meet up with these specific issues which involve extracting features from the face and genes involved in pain. Patients suffering from pain require a comprehensive assessment to be conducted for proper diagnosis. The contributions of this paper are fourfold: Firstly, this paper provides an efficient approach to computational pain quantification. Secondly, it investigates the medical practitioner perception to pain along with the readings of the various tools available. Thirdly, the psychological aspect is taken into consideration to predict how pain is perceived by observers and experts (physicians). Fourthly, genes involved in pain and no pain conditions are taken and classified. Three large databases of spontaneous pain expressions are used i.e., McMaster UNBC Pain Archive database, self-prepared database and the other BioVid heat pain database of pain to verify the accuracy and robustness of the system. The methodology achieves 87% accuracy rate for classification of frames amid four levels of pain intensity. Designing intelligent computing systems with the given methodology will certainly improve the quality of life of patients.

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Authors and Affiliations

  1. Rajarshi School of Management and Technology, Varanasi, India

    Sanjay Kumar Singh

  2. Faculty of Engineering & Technology, Junwani, Bhilai, Durg, Chattisgarh, India

    Shrikant Tiwari

  3. Designer at the National Institute of Design, Ahmedabad, India

    Ali Imam Abidi

  4. Kamala Nehru Institute of Technology, Sultanpur, India

    Aruni Singh

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  1. Sanjay Kumar Singh
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  2. Shrikant Tiwari
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Correspondence to Sanjay Kumar Singh.

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Singh, S.K., Tiwari, S., Abidi, A.I. et al. Prediction of pain intensity using multimedia data. Multimed Tools Appl 76, 19317–19342 (2017). https://doi.org/10.1007/s11042-017-4718-6

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  • DOI: https://doi.org/10.1007/s11042-017-4718-6

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