Emotion Recognition to Improve e-Healthcare Systems in Smart Cities
The ability to detect and control patients’ pain is a fundamental objective within any medical service. Nowadays, the evaluation of pain in patients depends mainly on the continuous monitoring of the medical staff and, where applicable, on people from the immediate environment of the patient. However, the detection of pain becomes a priority situation when the patient is unable to express verbally his/her experience of pain, as is the case of patients under sedation or babies, among others. Therefore, it is necessary to provide alternative methods for its evaluation and detection. As a result, the implementation of a system capable of determining whether a person suffers pain at any level would mean an increase in the quality of life of patients, enabling a more personalized adaptation of palliative treatments. Among other elements, it is possible to consider facial expressions as a valid indicator of a person’s degree of pain. Consequently, this paper presents the design of a remote patient monitoring system that uses an automatic emotion detection system by means of image analysis. For this purpose, a system based on texture descriptors is used together with Support Vector Machines (SVM) for their classification. The results obtained with different databases provide accuracies around 90%, which proves the validity of our proposal. In this way, the e-health systems of a Smart City will be improved by introducing a system as the one proposed here.
This work has been partially supported by the Spanish Research Agency (AEI) and the European Regional Development Fund (FEDER) under project CloudDriver4Industry TIN2017-89266-R.
- 1.IASP: IASP Terminology. https://www.iasp-pain.org/Education/Content.aspx?ItemNumber=1698, last accessed 2019/01/14
- 2.Ranstad: e-health, cómo la tecnología mejora nuestra salud. https://www.randstad.es/tendencias360/e-health-como-la-tecnologia-mejora-nuestra-salud/, last accessed 2019/01/22
- 4.The Statistics Portal: Global retail sales of products specified for babies and children from 2007 to 2016, by category (in billion U.S. dollars), https://www.statista.com/statistics/618389/global-retail-sales-baby-childrens-products/, last accessed 2019/01/18
- 5.The Statistics Portal, Baby care products market size worldwide from 2018 to 2026 (in billion U.S. dollars), https://www.statista.com/statistics/258435/revenue-of-the-baby-care-products-market-worldwide/, last accessed 2019/01/18
- 6.Google Trends: Baby Monitor. https://trends.google.es/trends/explore?date=all&q=baby%20monitor, last accessed 2019/01/18
- 9.Preciado, A.: Trabajo Fin de Grado: Valoración del dolor en neonatos: Propuesta de un modelo de registro en la unidad de neonatología del complejo hospitalario de Navarra. Universidad Pública de Navarra (2013)Google Scholar
- 10.Romero, S.: s.f. Muy interesante, Los bebés sienten dolor como los adultos, https://www.muyinteresante.es/salud/articulo/los-bebes-sienten-el-dolor-como-los-adultos-341429608053, last accessed 2019/01/20
- 11.Pietikäinen, M.: Local binary pattern. Scholarpedia 5(3), 9775 (2010). http://www.scholarpedia.org/article/Local_Binary_Patterns, last accessed 2019/01/10CrossRefGoogle Scholar
- 12.Lindahl, T.: Master thesis: Study of local binary pattern. Linköping University (2007)Google Scholar
- 13.Suárez, E.J.C.: Tutorial sobre máquinas de vectores soporte (sVM). ETS de Ingeniería Informática, Universidad Nacional de Educación a Distancia (UNED), Dpto. de Inteligencia Artificial (2014)Google Scholar