Pain intensity estimation based on a spatial transformation and attention CNN

Abstract

Models designed to detect abnormalities that reflect disease from facial structures are an emerging area of research for automated facial analysis, which has important potential value in smart healthcare applications. However, most of the proposed models directly analyze the whole face image containing the background information, and rarely consider the effects of the background and different face regions on the analysis results. Therefore, in view of these effects, we propose an end-to-end attention network with spatial transformation to estimate different pain intensities. In the proposed method, the face image is first provided as input to a spatial transformation network for solving the problem of background interference; then, the attention mechanism is used to adaptively adjust the weights of different face regions of the transformed face image; finally, a convolutional neural network (CNN) containing a Softmax function is utilized to classify the pain levels. The extensive experiments and analysis are conducted on the benchmarking and publicly available database, namely the UNBC-McMaster shoulder pain. More specifically, in order to verify the superiority of our proposed method, the comparisons with the basic CNNs and the-state-of-the-arts are performed, respectively. The experiments show that the introduced spatial transformation and attention mechanism in our method can significantly improve the estimation performances and outperform the-state-of-the-arts.

Data Science Trends Relevant to Nursing Practice: A Rapid Review of the 2020 Literature

“…Out of the total of 27 studies that were identified for the topic of pain, 14 used a prospective cohort design, 197 198 199 200 201 202 203 204 205 206 207 208 209 210 11 used an observational design, 200 204 205 207 210 211 212 213 214 215 216 6 used a retrospective cohort design, 211 214 215 217 218 219 4 used a randomized control trial, 201 212 220 221 1 used a cross-sectional design, 222 and 1 used mixed methods. 223 Most studies used questionnaire/survey data, but eight used administrative databases, 206 207 208 210 212 220 221 222 seven used mobile devices/sensors, 200 203 204 205 210 216 220 and four used a data warehouse or registry.…”

“…198 203 208 214 Study populations were mostly done with adults in the outpatient setting but four were inpatient 197 201 211 223 and one was done with a pediatric population. 205 Although many studies were conducted in the United States, others included China, 213 214 215 222 Australia, 207 Canada, 202 the Netherlands, 199 212 Germany, 208 210 211 Norway, 201 Finland, 204 South Korea, 203 Argentina, 219 Portugal, 197 Japan, 209 and Spain. 206 Sample sizes ranged from 10 to 6,316 observations.…”

“…Studies explored various outcomes including surgical applications such as determination of postsurgical measures based on residual pain, 197 predicting patellofemoral pain 1 year after intervention, 201 predicting neuropathic pain, 202 predicting chronic pain of 7 to 10 years into the future, 217 predicting complex regional pain syndrome, 207 predicting pain relief for knee osteoarthritis patients, 209 detection of pain, 210 214 216 222 and pain intensity estimation/classification. 205 213 215 220 Other outcomes focused on pain as a predictor of anxiety and depression, coronary heart disease, 199 health status, 218 noncancer pain as predictor of brain aging, 208 and length of stay. 211 For patients with low back pain, societal cost, 212 and clinical and sociodemographic predictors of increased disability 221 have been studied.…”

“…219 223 There were several novel data sources included, such as the use of physiologic signals from electroencephalograms (EEG), 213 electromyography, 204 220 spectrogram, 205 electrodermal activity, 216 sensor data, 200 MRIs, 198 208 214 222 kinematics/motion data, 203 210 220 and medical images. 197 198 208 210 214 215 222…”

Image-Based Pain Intensity Estimation Using Parallel CNNs with Regional Attention

“…Mohammad et al [ 22 ] proposed statistical spatiotemporal distillation (SSD) to encode the spatiotemporal variations underlying the facial video into a single RGB image and then used 2D models to process video data. Xin et al [ 23 ] performed a 2D affine transformation (i.e., translation, cropping, rotation, scaling, and skewing) against background interference, spatial transformation, and attentional CNN to improve the estimation performance. Huang et al [ 24 ] proposed a deep spatiotemporal attention model PAN (Pain-Attentive Network), which integrates the attention mechanism of both spatial and temporal dimensions, but this model consumed a lot of computing resources.…”