Recognizing Patients’ Emotions: Teaching Health Care Providers to Interpret Facial Expressions

Ragsdale, John W.; Deusen, Reed Van; Rubio, Doris McGartland; Spagnoletti, Carla

doi:10.1097/acm.0000000000001163

Cited by 13 publications

(7 citation statements)

References 12 publications

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“…In another small sample study, Pollux (2016) found large improvements in the ability to recognize subtle facial expressions due to three training sessions with morphed faces showing happy, sad or surprised faces, as well as event-related potential (ERP) modulations in the brain due to training. In a group intervention, Ragsdale et al (2016) presented health care providers with information about facial expression features (i.e., typical muscle contractions) and facilitated three interactive exercises about facial emotional expressions in general and in the medical context. They found a substantial improvement from pre to posttest.…”

Section: Review Of Emotion Recognition Trainings For Healthy Adultsmentioning

confidence: 99%

Training Emotion Recognition Accuracy: Results for Multimodal Expressions and Facial Micro Expressions

et al. 2021

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Nonverbal emotion recognition accuracy (ERA) is a central feature of successful communication and interaction, and is of importance for many professions. We developed and evaluated two ERA training programs—one focusing on dynamic multimodal expressions (audio, video, audio-video) and one focusing on facial micro expressions. Sixty-seven subjects were randomized to one of two experimental groups (multimodal, micro expression) or an active control group (emotional working memory task). Participants trained once weekly with a brief computerized training program for three consecutive weeks. Pre-post outcome measures consisted of a multimodal ERA task, a micro expression recognition task, and a task about patients' emotional cues. Post measurement took place approximately a week after the last training session. Non-parametric mixed analyses of variance using the Aligned Rank Transform were used to evaluate the effectiveness of the training programs. Results showed that multimodal training was significantly more effective in improving multimodal ERA compared to micro expression training or the control training; and the micro expression training was significantly more effective in improving micro expression ERA compared to the other two training conditions. Both pre-post effects can be interpreted as large. No group differences were found for the outcome measure about recognizing patients' emotion cues. There were no transfer effects of the training programs, meaning that participants only improved significantly for the specific facet of ERA that they had trained on. Further, low baseline ERA was associated with larger ERA improvements. Results are discussed with regard to methodological and conceptual aspects, and practical implications and future directions are explored.

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Section: Review Of Emotion Recognition Trainings For Healthy Adultsmentioning

confidence: 99%

Training Emotion Recognition Accuracy: Results for Multimodal Expressions and Facial Micro Expressions

et al. 2021

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“…According to Darwin and Prodger [4], human facial expressions indicate humans' emotional states and intentions. Recently, automatic emotion detection through computer vision techniques has shown a growth in interest and application across many domains, including hospital patient care [5], neuroscience research [6], smart home technologies [7], and even in cancer treatment [8,9]. This diversity has established emotion recognition as a distinct and growing field in research fields, primarily due to its wide range of applications and intense impact on various phases of human life.…”

Section: Introductionmentioning

confidence: 99%

Simple Histogram Equalization Technique Improves Performance of VGG Models on Facial Emotion Recognition Datasets

Chowdhury,

Liu,

Ramanna

2024

Algorithms

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Facial emotion recognition (FER) is crucial across psychology, neuroscience, computer vision, and machine learning due to the diversified and subjective nature of emotions, varying considerably across individuals, cultures, and contexts. This study explored FER through convolutional neural networks (CNNs) and Histogram Equalization techniques. It investigated the impact of histogram equalization, data augmentation, and various model optimization strategies on FER accuracy across different datasets like KDEF, CK+, and FER2013. Using pre-trained VGG architectures, such as VGG19 and VGG16, this study also examined the effectiveness of fine-tuning hyperparameters and implementing different learning rate schedulers. The evaluation encompassed diverse metrics including accuracy, Area Under the Receiver Operating Characteristic Curve (AUC-ROC), Area Under the Precision–Recall Curve (AUC-PRC), and Weighted F1 score. Notably, the fine-tuned VGG architecture demonstrated a state-of-the-art performance compared to conventional transfer learning models and achieved 100%, 95.92%, and 69.65% on the CK+, KDEF, and FER2013 datasets, respectively.

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“…These studies are in favor of the assumption that psychotherapists have greater ERA than other populations. When investigating other mental health professionals (like psychiatrists or nurses), two studies ( Arango de Montis et al, 2014 ; Minardi, 2013 ) found some indication to believe that clinical training or experience leads to increased ERA, whereas another study ( Ragsdale et al, 2016 ) did not find a difference in ERA between medical faculty and medical students. To sum up, the research about psychotherapists’ ERA in comparison to other populations, and about whether ERA improves due to clinical training and experience, is inconclusive and sample sizes are often small.…”

Section: Introductionmentioning

confidence: 99%

Trainee psychotherapists’ emotion recognition accuracy during 1.5 years of psychotherapy education compared to a control group: no improvement after psychotherapy training

Döllinger,

Letellier,

Högman

et al. 2023

PeerJ

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The ability to recognize and work with patients’ emotions is considered an important part of most psychotherapy approaches. Surprisingly, there is little systematic research on psychotherapists’ ability to recognize other people’s emotional expressions. In this study, we compared trainee psychotherapists’ nonverbal emotion recognition accuracy to a control group of undergraduate students at two time points: at the beginning and at the end of one and a half years of theoretical and practical psychotherapy training. Emotion recognition accuracy (ERA) was assessed using two standardized computer tasks, one for recognition of dynamic multimodal (facial, bodily, vocal) expressions and one for recognition of facial micro expressions. Initially, 154 participants enrolled in the study, 72 also took part in the follow-up. The trainee psychotherapists were moderately better at recognizing multimodal expressions, and slightly better at recognizing facial micro expressions, than the control group at the first test occasion. However, mixed multilevel modeling indicated that the ERA change trajectories for the two groups differed significantly. While the control group improved in their ability to recognize multimodal emotional expressions from pretest to follow-up, the trainee psychotherapists did not. Both groups improved their micro expression recognition accuracy, but the slope for the control group was significantly steeper than the trainee psychotherapists’. These results suggest that psychotherapy education and clinical training do not always contribute to improved emotion recognition accuracy beyond what could be expected due to time or other factors. Possible reasons for that finding as well as implications for the psychotherapy education are discussed.

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Recognizing Patients’ Emotions: Teaching Health Care Providers to Interpret Facial Expressions

Cited by 13 publications

References 12 publications

Training Emotion Recognition Accuracy: Results for Multimodal Expressions and Facial Micro Expressions

Training Emotion Recognition Accuracy: Results for Multimodal Expressions and Facial Micro Expressions

Simple Histogram Equalization Technique Improves Performance of VGG Models on Facial Emotion Recognition Datasets

Trainee psychotherapists’ emotion recognition accuracy during 1.5 years of psychotherapy education compared to a control group: no improvement after psychotherapy training

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