Automatic Pain Assessment with Facial Activity Descriptors

Werner, Philipp; Al-Hamadi, Ayoub; Limbrecht-Ecklundt, Kerstin; Walter, Steffen; Gruss, Sascha; Traue, Harald C.

doi:10.1109/taffc.2016.2537327

Cited by 124 publications

(100 citation statements)

References 53 publications

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“…Despite the face still receiving more attention than other affective channels in building affect aware systems (e.g., [32][33] [34]), the emergence of low-cost movement sensing technology is increasingly leading researchers to target movement as an affective modality, including in clinical contexts. Beyond work aimed at assessing affective states during sedentary clinical settings [35], there is also a growing interest in their assessment during physical activity and in situ [36].…”

Section: Background: Automatic Detection Of Pain Related Affect From mentioning

confidence: 99%

Human Observer and Automatic Assessment of Movement Related Self-Efficacy in Chronic Pain: From Exercise to Functional Activity

Olugbade

Bianchi-Berthouze

Marquardt

et al. 2020

IEEE Trans. Affective Comput.

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Abstract-Clinicians tailor intervention in chronic pain rehabilitation to movement related self-efficacy (MRSE). This motivates us to investigate automatic MRSE estimation in this context towards the development of technology that is able to provide appropriate support in the absence of a clinician. We first explored clinical observer estimation, which showed that body movement behaviours, rather than facial expressions or engagement behaviours, were more pertinent to MRSE estimation during physical activity instances. Based on our findings, we built a system that estimates MRSE from bodily expressions and bodily muscle activity captured using wearable sensors. Our results (F1 scores of 0.95 and 0.78 in two physical exercise types) provide evidence of the feasibility of automatic MRSE estimation to support chronic pain physical rehabilitation. We further explored automatic estimation of MRSE with a reduced set of low-cost sensors to investigate the possibility of embedding such capabilities in ubiquitous wearable devices to support functional activity. Our evaluation for both exercise and functional activity resulted in F1 score of 0.79. This result suggests the possibility of (and calls for more studies on) MRSE estimation during everyday functioning in ubiquitous settings. We provide a discussion of the implication of our findings for relevant areas.

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Section: Background: Automatic Detection Of Pain Related Affect From mentioning

confidence: 99%

Human Observer and Automatic Assessment of Movement Related Self-Efficacy in Chronic Pain: From Exercise to Functional Activity

Olugbade

Bianchi-Berthouze

Marquardt

et al. 2020

IEEE Trans. Affective Comput.

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“…In cases where hybrid, mixed, or multiple features of the same type were used, the fusion of features was performed either before the learning step (cf. [75], [123]), or by fusing the decisions of classifiers trained separately for each feature (cf. [107], [116]).…”

Section: Feature Extractionmentioning

confidence: 99%

Automatic Detection of Pain from Facial Expressions: A Survey

Hassan

Seus

Wollenberg

et al. 2021

IEEE Trans. Pattern Anal. Mach. Intell.

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Pain sensation is essential for survival, since it draws attention to physical threat to the body. Pain assessment is usually done through self-reports. However, self-assessment of pain is not available in the case of noncommunicative patients, and therefore, observer reports should be relied upon. Observer reports of pain could be prone to errors due to subjective biases of observers. Moreover, continuous monitoring by humans is impractical. Therefore, automatic pain detection technology could be deployed to assist human caregivers and complement their service, thereby improving the quality of pain management, especially for noncommunicative patients. Facial expressions are a reliable indicator of pain, and are used in all observer-based pain assessment tools. Following the advancements in automatic facial expression analysis, computer vision researchers have tried to use this technology for developing approaches for automatically detecting pain from facial expressions. This paper surveys the literature published in this field over the past decade, categorizes it, and identifies future research directions. The survey covers the pain datasets used in the reviewed literature, the learning tasks targeted by the approaches, the features extracted from images and image sequences to represent pain-related information, and finally, the machine learning methods used.

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“…Next, the corresponding temporal series were employed for each facial descriptor obtained from all video frames, which is inspired by [28].…”

Section: Temporal Descriptors For Video Sequencementioning

confidence: 99%

“…These signals could be treated as the state signal, speed signal and acceleration signal of the descriptor signal, respectively. Subsequently, several parameters could be extracted from the temporal signals to better depict the characteristics of the signal variance over time [28]. A total of 16 temporal geometric facial features were extracted for each video sequence, and we categorized these parameters into 6 groups as follows:…”

Section: Temporal Descriptors For Video Sequencementioning

confidence: 99%

Infants' Pain Recognition Based on Facial Expression: Dynamic Hybrid Descriptions

Zhi

Goldgof

Ashmeade

et al. 2018

IEICE Trans. Inf. & Syst.

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SUMMARYThe accurate assessment of infants' pain is important for understanding their medical conditions and developing suitable treatment. Pediatric studies reported that the inadequate treatment of infants' pain might cause various neuroanatomical and psychological problems. The fact that infants can not communicate verbally motivates increasing interests to develop automatic pain assessment system that provides continuous and accurate pain assessment. In this paper, we propose a new set of pain facial activity features to describe the infants' facial expression of pain. Both dynamic facial texture feature and dynamic geometric feature are extracted from video sequences and utilized to classify facial expression of infants as pain or no pain. For the dynamic analysis of facial expression, we construct spatiotemporal domain representation for texture features and time series representation (i.e. time series of frame-level features) for geometric features. Multiple facial features are combined through both feature fusion and decision fusion schemes to evaluate their effectiveness in infants' pain assessment. Experiments are conducted on the video acquired from NICU infants, and the best accuracy of the proposed pain assessment approaches is 95.6%. Moreover, we find that although decision fusion does not perform better than that of feature fusion, the False Negative Rate of decision fusion (6.2%) is much lower than that of feature fusion (25%). key words: infants pain assessment, temporal geometric descriptor, LBP-TOP, decision fusion, video analysis

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Automatic Pain Assessment with Facial Activity Descriptors

Cited by 124 publications

References 53 publications

Human Observer and Automatic Assessment of Movement Related Self-Efficacy in Chronic Pain: From Exercise to Functional Activity

Human Observer and Automatic Assessment of Movement Related Self-Efficacy in Chronic Pain: From Exercise to Functional Activity

Automatic Detection of Pain from Facial Expressions: A Survey

Infants' Pain Recognition Based on Facial Expression: Dynamic Hybrid Descriptions

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