TY - GEN
T1 - Assessment of Pain Using Facial Pictures Taken with a Smartphone
AU - Adibuzzaman, Mohammad
AU - Ostberg, Colin
AU - Ahamed, Sheikh
AU - Povinelli, Richard
AU - Sindhu, Bhagwant
AU - Love, Richard
AU - Kawsar, Ferdaus
AU - Ahsan, Golam Mushih Tanimul
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/9/21
Y1 - 2015/9/21
N2 - Timely and accurate information about patients' symptoms is important for clinical decision making such as adjustment of medication. Due to the limitations of self-reported symptom such as pain, we investigated whether facial images can be used for detecting pain level accurately using existing algorithms and infrastructure for cancer patients. For low cost and better pain management solution, we present a smart phone based system for pain expression recognition from facial images. To the best of our knowledge, this is the first study for mobile based chronic pain intensity detection. The proposed algorithms classify faces, represented as a weighted combination of Eigenfaces, using an angular distance, and support vector machines (SVMs). A pain score was assigned to each image by the subject. The study was done in two phases. In the first phase, data were collected as a part of a six month long longitudinal study in Bangladesh. In the second phase, pain images were collected for a cross-sectional study in three different countries: Bangladesh, Nepal and the United States. The study shows that a personalized model for pain assessment performs better for automatic pain assessment and the training set should contain varying levels of pain representing the application scenario.
AB - Timely and accurate information about patients' symptoms is important for clinical decision making such as adjustment of medication. Due to the limitations of self-reported symptom such as pain, we investigated whether facial images can be used for detecting pain level accurately using existing algorithms and infrastructure for cancer patients. For low cost and better pain management solution, we present a smart phone based system for pain expression recognition from facial images. To the best of our knowledge, this is the first study for mobile based chronic pain intensity detection. The proposed algorithms classify faces, represented as a weighted combination of Eigenfaces, using an angular distance, and support vector machines (SVMs). A pain score was assigned to each image by the subject. The study was done in two phases. In the first phase, data were collected as a part of a six month long longitudinal study in Bangladesh. In the second phase, pain images were collected for a cross-sectional study in three different countries: Bangladesh, Nepal and the United States. The study shows that a personalized model for pain assessment performs better for automatic pain assessment and the training set should contain varying levels of pain representing the application scenario.
KW - Automatic pain assessment
KW - Quality of life
KW - Remote monitoring
UR - http://www.scopus.com/inward/record.url?scp=84962163777&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84962163777&partnerID=8YFLogxK
U2 - 10.1109/COMPSAC.2015.150
DO - 10.1109/COMPSAC.2015.150
M3 - Conference contribution
AN - SCOPUS:84962163777
T3 - Proceedings - International Computer Software and Applications Conference
SP - 726
EP - 731
BT - Proceedings - 2015 IEEE 39th Annual Computer Software and Applications Conference, COMPSAC 2015
A2 - Huang, Gang
A2 - Yang, Jingwei
A2 - Ahamed, Sheikh Iqbal
A2 - Hsiung, Pao-Ann
A2 - Chang, Carl K.
A2 - Chu, William
A2 - Crnkovic, Ivica
PB - IEEE Computer Society
T2 - 39th IEEE Annual Computer Software and Applications Conference, COMPSAC 2015
Y2 - 1 July 2015 through 5 July 2015
ER -