1
|
Zaharieva MS, Salvadori EA, Messinger DS, Visser I, Colonnesi C. Automated facial expression measurement in a longitudinal sample of 4- and 8-month-olds: Baby FaceReader 9 and manual coding of affective expressions. Behav Res Methods 2024:10.3758/s13428-023-02301-3. [PMID: 38273072 DOI: 10.3758/s13428-023-02301-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/20/2023] [Indexed: 01/27/2024]
Abstract
Facial expressions are among the earliest behaviors infants use to express emotional states, and are crucial to preverbal social interaction. Manual coding of infant facial expressions, however, is laborious and poses limitations to replicability. Recent developments in computer vision have advanced automated facial expression analyses in adults, providing reproducible results at lower time investment. Baby FaceReader 9 is commercially available software for automated measurement of infant facial expressions, but has received little validation. We compared Baby FaceReader 9 output to manual micro-coding of positive, negative, or neutral facial expressions in a longitudinal dataset of 58 infants at 4 and 8 months of age during naturalistic face-to-face interactions with the mother, father, and an unfamiliar adult. Baby FaceReader 9's global emotional valence formula yielded reasonable classification accuracy (AUC = .81) for discriminating manually coded positive from negative/neutral facial expressions; however, the discrimination of negative from neutral facial expressions was not reliable (AUC = .58). Automatically detected a priori action unit (AU) configurations for distinguishing positive from negative facial expressions based on existing literature were also not reliable. A parsimonious approach using only automatically detected smiling (AU12) yielded good performance for discriminating positive from negative/neutral facial expressions (AUC = .86). Likewise, automatically detected brow lowering (AU3+AU4) reliably distinguished neutral from negative facial expressions (AUC = .79). These results provide initial support for the use of selected automatically detected individual facial actions to index positive and negative affect in young infants, but shed doubt on the accuracy of complex a priori formulas.
Collapse
Affiliation(s)
- Martina S Zaharieva
- Department of Developmental Psychology, Faculty of Social and Behavioural Sciences, University of Amsterdam, Nieuwe Achtergracht 129b, 1001 NK, Amsterdam, The Netherlands.
- Developmental Psychopathology Unit, Development and Education, Faculty of Social and Behavioural Sciences, Research Institute of Child, University of Amsterdam, Nieuwe Achtergracht 129b, 1001 NK, Amsterdam, The Netherlands.
- Yield, Research Priority Area, University of Amsterdam, Amsterdam, The Netherlands.
| | - Eliala A Salvadori
- Developmental Psychopathology Unit, Development and Education, Faculty of Social and Behavioural Sciences, Research Institute of Child, University of Amsterdam, Nieuwe Achtergracht 129b, 1001 NK, Amsterdam, The Netherlands
- Yield, Research Priority Area, University of Amsterdam, Amsterdam, The Netherlands
| | - Daniel S Messinger
- Department of Psychology, University of Miami, Coral Gables, FL, USA
- Department of Pediatrics, University of Miami, Coral Gables, FL, USA
- Department of Music Engineering, University of Miami, Coral Gables, FL, USA
- Department of Electrical and Computer Engineering, University of Miami, Coral Gables, FL, USA
| | - Ingmar Visser
- Department of Developmental Psychology, Faculty of Social and Behavioural Sciences, University of Amsterdam, Nieuwe Achtergracht 129b, 1001 NK, Amsterdam, The Netherlands
- Yield, Research Priority Area, University of Amsterdam, Amsterdam, The Netherlands
| | - Cristina Colonnesi
- Developmental Psychopathology Unit, Development and Education, Faculty of Social and Behavioural Sciences, Research Institute of Child, University of Amsterdam, Nieuwe Achtergracht 129b, 1001 NK, Amsterdam, The Netherlands
- Yield, Research Priority Area, University of Amsterdam, Amsterdam, The Netherlands
| |
Collapse
|
2
|
Khoo LS, Lim MK, Chong CY, McNaney R. Machine Learning for Multimodal Mental Health Detection: A Systematic Review of Passive Sensing Approaches. SENSORS (BASEL, SWITZERLAND) 2024; 24:348. [PMID: 38257440 PMCID: PMC10820860 DOI: 10.3390/s24020348] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Revised: 12/14/2023] [Accepted: 12/18/2023] [Indexed: 01/24/2024]
Abstract
As mental health (MH) disorders become increasingly prevalent, their multifaceted symptoms and comorbidities with other conditions introduce complexity to diagnosis, posing a risk of underdiagnosis. While machine learning (ML) has been explored to mitigate these challenges, we hypothesized that multiple data modalities support more comprehensive detection and that non-intrusive collection approaches better capture natural behaviors. To understand the current trends, we systematically reviewed 184 studies to assess feature extraction, feature fusion, and ML methodologies applied to detect MH disorders from passively sensed multimodal data, including audio and video recordings, social media, smartphones, and wearable devices. Our findings revealed varying correlations of modality-specific features in individualized contexts, potentially influenced by demographics and personalities. We also observed the growing adoption of neural network architectures for model-level fusion and as ML algorithms, which have demonstrated promising efficacy in handling high-dimensional features while modeling within and cross-modality relationships. This work provides future researchers with a clear taxonomy of methodological approaches to multimodal detection of MH disorders to inspire future methodological advancements. The comprehensive analysis also guides and supports future researchers in making informed decisions to select an optimal data source that aligns with specific use cases based on the MH disorder of interest.
Collapse
Affiliation(s)
- Lin Sze Khoo
- Department of Human-Centered Computing, Faculty of Information Technology, Monash University, Clayton, VIC 3800, Australia;
| | - Mei Kuan Lim
- School of Information Technology, Monash University Malaysia, Subang Jaya 46150, Malaysia; (M.K.L.); (C.Y.C.)
| | - Chun Yong Chong
- School of Information Technology, Monash University Malaysia, Subang Jaya 46150, Malaysia; (M.K.L.); (C.Y.C.)
| | - Roisin McNaney
- Department of Human-Centered Computing, Faculty of Information Technology, Monash University, Clayton, VIC 3800, Australia;
| |
Collapse
|
3
|
Sellers KK, Cohen JL, Khambhati AN, Fan JM, Lee AM, Chang EF, Krystal AD. Closed-loop neurostimulation for the treatment of psychiatric disorders. Neuropsychopharmacology 2024; 49:163-178. [PMID: 37369777 PMCID: PMC10700557 DOI: 10.1038/s41386-023-01631-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 05/31/2023] [Accepted: 06/02/2023] [Indexed: 06/29/2023]
Abstract
Despite increasing prevalence and huge personal and societal burden, psychiatric diseases still lack treatments which can control symptoms for a large fraction of patients. Increasing insight into the neurobiology underlying these diseases has demonstrated wide-ranging aberrant activity and functioning in multiple brain circuits and networks. Together with varied presentation and symptoms, this makes one-size-fits-all treatment a challenge. There has been a resurgence of interest in the use of neurostimulation as a treatment for psychiatric diseases. Initial studies using continuous open-loop stimulation, in which clinicians adjusted stimulation parameters during patient visits, showed promise but also mixed results. Given the periodic nature and fluctuations of symptoms often observed in psychiatric illnesses, the use of device-driven closed-loop stimulation may provide more effective therapy. The use of a biomarker, which is correlated with specific symptoms, to deliver stimulation only during symptomatic periods allows for the personalized therapy needed for such heterogeneous disorders. Here, we provide the reader with background motivating the use of closed-loop neurostimulation for the treatment of psychiatric disorders. We review foundational studies of open- and closed-loop neurostimulation for neuropsychiatric indications, focusing on deep brain stimulation, and discuss key considerations when designing and implementing closed-loop neurostimulation.
Collapse
Affiliation(s)
- Kristin K Sellers
- Department of Neurological Surgery, University of California, San Francisco, CA, USA
- Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
| | - Joshua L Cohen
- Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
- Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, CA, USA
| | - Ankit N Khambhati
- Department of Neurological Surgery, University of California, San Francisco, CA, USA
- Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
| | - Joline M Fan
- Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
- Department of Neurology, University of California, San Francisco, CA, USA
| | - A Moses Lee
- Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
- Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, CA, USA
| | - Edward F Chang
- Department of Neurological Surgery, University of California, San Francisco, CA, USA
- Weill Institute for Neurosciences, University of California, San Francisco, CA, USA
| | - Andrew D Krystal
- Weill Institute for Neurosciences, University of California, San Francisco, CA, USA.
- Department of Psychiatry and Behavioral Sciences, University of California, San Francisco, CA, USA.
| |
Collapse
|
4
|
Sabater-Gárriz Á, Gaya-Morey FX, Buades-Rubio JM, Manresa-Yee C, Montoya P, Riquelme I. Automated facial recognition system using deep learning for pain assessment in adults with cerebral palsy. Digit Health 2024; 10:20552076241259664. [PMID: 38846372 PMCID: PMC11155325 DOI: 10.1177/20552076241259664] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Accepted: 05/07/2024] [Indexed: 06/09/2024] Open
Abstract
Objective Assessing pain in individuals with neurological conditions like cerebral palsy is challenging due to limited self-reporting and expression abilities. Current methods lack sensitivity and specificity, underlining the need for a reliable evaluation protocol. An automated facial recognition system could revolutionize pain assessment for such patients.The research focuses on two primary goals: developing a dataset of facial pain expressions for individuals with cerebral palsy and creating a deep learning-based automated system for pain assessment tailored to this group. Methods The study trained ten neural networks using three pain image databases and a newly curated CP-PAIN Dataset of 109 images from cerebral palsy patients, classified by experts using the Facial Action Coding System. Results The InceptionV3 model demonstrated promising results, achieving 62.67% accuracy and a 61.12% F1 score on the CP-PAIN dataset. Explainable AI techniques confirmed the consistency of crucial features for pain identification across models. Conclusion The study underscores the potential of deep learning in developing reliable pain detection systems using facial recognition for individuals with communication impairments due to neurological conditions. A more extensive and diverse dataset could further enhance the models' sensitivity to subtle pain expressions in cerebral palsy patients and possibly extend to other complex neurological disorders. This research marks a significant step toward more empathetic and accurate pain management for vulnerable populations.
Collapse
Affiliation(s)
- Álvaro Sabater-Gárriz
- Department of Research and Training, Balearic ASPACE Foundation, Marratxí, Spain
- Department of Nursing and Physiotherapy, University of the Balearic Islands, Palma de Mallorca, Spain
- Research Institute on Health Sciences (IUNICS), University of the Balearic Islands, Palma de Mallorca, Spain
- Health Research Institute of the Balearic Islands (IdISBa), Palma de Mallorca, Spain
| | - F Xavier Gaya-Morey
- Department of Mathematics and Computer Science, University of the Balearic Islands, Palma de Mallorca, Spain
| | - José María Buades-Rubio
- Research Institute on Health Sciences (IUNICS), University of the Balearic Islands, Palma de Mallorca, Spain
- Department of Mathematics and Computer Science, University of the Balearic Islands, Palma de Mallorca, Spain
| | - Cristina Manresa-Yee
- Research Institute on Health Sciences (IUNICS), University of the Balearic Islands, Palma de Mallorca, Spain
- Department of Mathematics and Computer Science, University of the Balearic Islands, Palma de Mallorca, Spain
| | - Pedro Montoya
- Research Institute on Health Sciences (IUNICS), University of the Balearic Islands, Palma de Mallorca, Spain
- Health Research Institute of the Balearic Islands (IdISBa), Palma de Mallorca, Spain
- Center for Mathematics, Computation and Cognition, Federal University of ABC, São Bernardo do Campo, Brazil
| | - Inmaculada Riquelme
- Department of Nursing and Physiotherapy, University of the Balearic Islands, Palma de Mallorca, Spain
- Research Institute on Health Sciences (IUNICS), University of the Balearic Islands, Palma de Mallorca, Spain
- Health Research Institute of the Balearic Islands (IdISBa), Palma de Mallorca, Spain
| |
Collapse
|
5
|
Bian Y, Küster D, Liu H, Krumhuber EG. Understanding Naturalistic Facial Expressions with Deep Learning and Multimodal Large Language Models. SENSORS (BASEL, SWITZERLAND) 2023; 24:126. [PMID: 38202988 PMCID: PMC10781259 DOI: 10.3390/s24010126] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 11/30/2023] [Accepted: 12/21/2023] [Indexed: 01/12/2024]
Abstract
This paper provides a comprehensive overview of affective computing systems for facial expression recognition (FER) research in naturalistic contexts. The first section presents an updated account of user-friendly FER toolboxes incorporating state-of-the-art deep learning models and elaborates on their neural architectures, datasets, and performances across domains. These sophisticated FER toolboxes can robustly address a variety of challenges encountered in the wild such as variations in illumination and head pose, which may otherwise impact recognition accuracy. The second section of this paper discusses multimodal large language models (MLLMs) and their potential applications in affective science. MLLMs exhibit human-level capabilities for FER and enable the quantification of various contextual variables to provide context-aware emotion inferences. These advancements have the potential to revolutionize current methodological approaches for studying the contextual influences on emotions, leading to the development of contextualized emotion models.
Collapse
Affiliation(s)
- Yifan Bian
- Department of Experimental Psychology, University College London, London WC1H 0AP, UK;
| | - Dennis Küster
- Department of Mathematics and Computer Science, University of Bremen, 28359 Bremen, Germany; (D.K.); (H.L.)
| | - Hui Liu
- Department of Mathematics and Computer Science, University of Bremen, 28359 Bremen, Germany; (D.K.); (H.L.)
| | - Eva G. Krumhuber
- Department of Experimental Psychology, University College London, London WC1H 0AP, UK;
| |
Collapse
|
6
|
Onal Ertugrul I, Ahn YA, Bilalpur M, Messinger DS, Speltz ML, Cohn JF. Infant AFAR: Automated facial action recognition in infants. Behav Res Methods 2023; 55:1024-1035. [PMID: 35538295 PMCID: PMC9646921 DOI: 10.3758/s13428-022-01863-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/13/2022] [Indexed: 11/08/2022]
Abstract
Automated detection of facial action units in infants is challenging. Infant faces have different proportions, less texture, fewer wrinkles and furrows, and unique facial actions relative to adults. For these and related reasons, action unit (AU) detectors that are trained on adult faces may generalize poorly to infant faces. To train and test AU detectors for infant faces, we trained convolutional neural networks (CNN) in adult video databases and fine-tuned these networks in two large, manually annotated, infant video databases that differ in context, head pose, illumination, video resolution, and infant age. AUs were those central to expression of positive and negative emotion. AU detectors trained in infants greatly outperformed ones trained previously in adults. Training AU detectors across infant databases afforded greater robustness to between-database differences than did training database specific AU detectors and outperformed previous state-of-the-art in infant AU detection. The resulting AU detection system, which we refer to as Infant AFAR (Automated Facial Action Recognition), is available to the research community for further testing and applications in infant emotion, social interaction, and related topics.
Collapse
|
7
|
Scangos KW, State MW, Miller AH, Baker JT, Williams LM. New and emerging approaches to treat psychiatric disorders. Nat Med 2023; 29:317-333. [PMID: 36797480 PMCID: PMC11219030 DOI: 10.1038/s41591-022-02197-0] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Accepted: 12/21/2022] [Indexed: 02/18/2023]
Abstract
Psychiatric disorders are highly prevalent, often devastating diseases that negatively impact the lives of millions of people worldwide. Although their etiological and diagnostic heterogeneity has long challenged drug discovery, an emerging circuit-based understanding of psychiatric illness is offering an important alternative to the current reliance on trial and error, both in the development and in the clinical application of treatments. Here we review new and emerging treatment approaches, with a particular emphasis on the revolutionary potential of brain-circuit-based interventions for precision psychiatry. Limitations of circuit models, challenges of bringing precision therapeutics to market and the crucial advances needed to overcome these obstacles are presented.
Collapse
Affiliation(s)
- Katherine W Scangos
- Department of Psychiatry and Behavioral Sciences, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA.
| | - Matthew W State
- Department of Psychiatry and Behavioral Sciences, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
| | - Andrew H Miller
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA, USA
| | - Justin T Baker
- McLean Hospital Institute for Technology in Psychiatry, Belmont, MA, USA
| | - Leanne M Williams
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
- Mental Illness Research Education and Clinical Center (MIRECC), VA Palo Alto Health Care System, Palo Alto, CA, USA
| |
Collapse
|
8
|
Provenza NR, Sheth SA, Dastin-van Rijn EM, Mathura RK, Ding Y, Vogt GS, Avendano-Ortega M, Ramakrishnan N, Peled N, Gelin LFF, Xing D, Jeni LA, Ertugrul IO, Barrios-Anderson A, Matteson E, Wiese AD, Xu J, Viswanathan A, Harrison MT, Bijanki KR, Storch EA, Cohn JF, Goodman WK, Borton DA. Long-term ecological assessment of intracranial electrophysiology synchronized to behavioral markers in obsessive-compulsive disorder. Nat Med 2021; 27:2154-2164. [PMID: 34887577 PMCID: PMC8800455 DOI: 10.1038/s41591-021-01550-z] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Accepted: 09/22/2021] [Indexed: 01/02/2023]
Abstract
Detection of neural signatures related to pathological behavioral states could enable adaptive deep brain stimulation (DBS), a potential strategy for improving efficacy of DBS for neurological and psychiatric disorders. This approach requires identifying neural biomarkers of relevant behavioral states, a task best performed in ecologically valid environments. Here, in human participants with obsessive-compulsive disorder (OCD) implanted with recording-capable DBS devices, we synchronized chronic ventral striatum local field potentials with relevant, disease-specific behaviors. We captured over 1,000 h of local field potentials in the clinic and at home during unstructured activity, as well as during DBS and exposure therapy. The wide range of symptom severity over which the data were captured allowed us to identify candidate neural biomarkers of OCD symptom intensity. This work demonstrates the feasibility and utility of capturing chronic intracranial electrophysiology during daily symptom fluctuations to enable neural biomarker identification, a prerequisite for future development of adaptive DBS for OCD and other psychiatric disorders.
Collapse
Affiliation(s)
- Nicole R Provenza
- Brown University School of Engineering, Providence, RI, USA
- Charles Stark Draper Laboratory, Cambridge, MA, USA
| | - Sameer A Sheth
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, USA
| | | | - Raissa K Mathura
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, USA
| | - Yaohan Ding
- Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, USA
| | - Gregory S Vogt
- Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, USA
| | - Michelle Avendano-Ortega
- Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, USA
| | - Nithya Ramakrishnan
- Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, USA
| | - Noam Peled
- MGH/HST Martinos Center for Biomedical Imaging, Charlestown, MA, USA
- Harvard Medical School, Cambridge, MA, USA
| | | | - David Xing
- Brown University School of Engineering, Providence, RI, USA
| | - Laszlo A Jeni
- Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Itir Onal Ertugrul
- Department of Cognitive Science and Artificial Intelligence, Tilburg University, Tilburg, the Netherlands
| | | | - Evan Matteson
- Brown University School of Engineering, Providence, RI, USA
| | - Andrew D Wiese
- Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, USA
- Department of Psychology, University of Missouri-Kansas City, Kansas City, MO, USA
| | - Junqian Xu
- Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, USA
- Department of Radiology, Baylor College of Medicine, Houston, TX, USA
| | - Ashwin Viswanathan
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, USA
| | | | - Kelly R Bijanki
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, USA
- Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, USA
| | - Eric A Storch
- Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, USA
| | - Jeffrey F Cohn
- Department of Psychology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Wayne K Goodman
- Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, USA
| | - David A Borton
- Brown University School of Engineering, Providence, RI, USA.
- Carney Institute for Brain Science, Brown University, Providence, RI, USA.
- Center for Neurorestoration and Neurotechnology, Rehabilitation R&D Service, Department of Veterans Affairs, Providence, RI, USA.
| |
Collapse
|
9
|
Wörtwein T, Sheeber LB, Allen N, Cohn JF, Morency LP. Human-Guided Modality Informativeness for Affective States. PROCEEDINGS OF THE ... ACM INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION. ICMI (CONFERENCE) 2021; 2021:728-734. [PMID: 35128550 PMCID: PMC8812829 DOI: 10.1145/3462244.3481004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
This paper studies the hypothesis that not all modalities are always needed to predict affective states. We explore this hypothesis in the context of recognizing three affective states that have shown a relation to a future onset of depression: positive, aggressive, and dysphoric. In particular, we investigate three important modalities for face-to-face conversations: vision, language, and acoustic modality. We first perform a human study to better understand which subset of modalities people find informative, when recognizing three affective states. As a second contribution, we explore how these human annotations can guide automatic affect recognition systems to be more interpretable while not degrading their predictive performance. Our studies show that humans can reliably annotate modality informativeness. Further, we observe that guided models significantly improve interpretability, i.e., they attend to modalities similarly to how humans rate the modality informativeness, while at the same time showing a slight increase in predictive performance.
Collapse
Affiliation(s)
- Torsten Wörtwein
- Language Technologies Institute, Carnegie Mellon University, Pittsburgh, PA, USA
| | | | - Nicholas Allen
- Department of Psychology, University of Oregon, Eugene, OR, USA
| | - Jeffrey F Cohn
- Department of Psychology, University of Pittsburgh, Pittsburgh, PA, USA
| | | |
Collapse
|
10
|
Namba S, Sato W, Osumi M, Shimokawa K. Assessing Automated Facial Action Unit Detection Systems for Analyzing Cross-Domain Facial Expression Databases. SENSORS (BASEL, SWITZERLAND) 2021; 21:4222. [PMID: 34203007 PMCID: PMC8235167 DOI: 10.3390/s21124222] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Revised: 06/15/2021] [Accepted: 06/17/2021] [Indexed: 11/16/2022]
Abstract
In the field of affective computing, achieving accurate automatic detection of facial movements is an important issue, and great progress has already been made. However, a systematic evaluation of systems that now have access to the dynamic facial database remains an unmet need. This study compared the performance of three systems (FaceReader, OpenFace, AFARtoolbox) that detect each facial movement corresponding to an action unit (AU) derived from the Facial Action Coding System. All machines could detect the presence of AUs from the dynamic facial database at a level above chance. Moreover, OpenFace and AFAR provided higher area under the receiver operating characteristic curve values compared to FaceReader. In addition, several confusion biases of facial components (e.g., AU12 and AU14) were observed to be related to each automated AU detection system and the static mode was superior to dynamic mode for analyzing the posed facial database. These findings demonstrate the features of prediction patterns for each system and provide guidance for research on facial expressions.
Collapse
Affiliation(s)
- Shushi Namba
- Psychological Process Team, BZP, Robotics Project, RIKEN, 2-2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto 6190288, Japan
| | - Wataru Sato
- Psychological Process Team, BZP, Robotics Project, RIKEN, 2-2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto 6190288, Japan
| | - Masaki Osumi
- KOHINATA Limited Liability Company, 2-7-3, Tateba, Naniwa-ku, Osaka 5560020, Japan; (M.O.); (K.S.)
| | - Koh Shimokawa
- KOHINATA Limited Liability Company, 2-7-3, Tateba, Naniwa-ku, Osaka 5560020, Japan; (M.O.); (K.S.)
| |
Collapse
|
11
|
Rezaei S, Moturu A, Zhao S, Prkachin KM, Hadjistavropoulos T, Taati B. Unobtrusive Pain Monitoring in Older Adults With Dementia Using Pairwise and Contrastive Training. IEEE J Biomed Health Inform 2021; 25:1450-1462. [PMID: 33338024 DOI: 10.1109/jbhi.2020.3045743] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Although pain is frequent in old age, older adults are often undertreated for pain. This is especially the case for long-term care residents with moderate to severe dementia who cannot report their pain because of cognitive impairments that accompany dementia. Nursing staff acknowledge the challenges of effectively recognizing and managing pain in long-term care facilities due to lack of human resources and, sometimes, expertise to use validated pain assessment approaches on a regular basis. Vision-based ambient monitoring will allow for frequent automated assessments so care staff could be automatically notified when signs of pain are displayed. However, existing computer vision techniques for pain detection are not validated on faces of older adults or people with dementia, and this population is not represented in existing facial expression datasets of pain. We present the first fully automated vision-based technique validated on a dementia cohort. Our contributions are threefold. First, we develop a deep learning-based computer vision system for detecting painful facial expressions on a video dataset that is collected unobtrusively from older adult participants with and without dementia. Second, we introduce a pairwise comparative inference method that calibrates to each person and is sensitive to changes in facial expression while using training data more efficiently than sequence models. Third, we introduce a fast contrastive training method that improves cross-dataset performance. Our pain estimation model outperforms baselines by a wide margin, especially when evaluated on faces of people with dementia. Pre-trained model and demo code available at https://github.com/TaatiTeam/pain_detection_demo.
Collapse
|
12
|
Ding Y, Ertugrul IO, Darzi A, Provenza N, Jeni LA, Borton D, Goodman W, Cohn J. Automated Detection of Enhanced DBS Device Settings. COMPANION PUBLICATION OF THE 2020 INTERNATIONAL CONFERENCE ON MULTIMODAL INTERACTION 2020; 2020:354-356. [PMID: 33937916 DOI: 10.1145/3395035.3425354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
Continuous deep brain stimulation (DBS) of the ventral striatum (VS) is an effective treatment for severe, treatment-refractory obsessive-compulsive disorder (OCD). Optimal parameter settings are signaled by a mirth response of intense positive affect, which are subjectively identified by clinicians. Subjective judgments are idiosyncratic and difficult to standardize. To objectively measure mirth responses, we used Automatic Facial Affect Recognition (AFAR) in a series of longitudinal assessments of a patient treated with DBS. Pre- and post-adjustment DBS were compared using both statistical and machine learning approaches. Positive affect was significantly higher post-DBS adjustment. Using SVM and XGBoost, participant's pre- and post-adjustment appearances were differentiated with F1 of 0.76, which suggests feasibility of objective measurement of mirth response.
Collapse
Affiliation(s)
| | | | - Ali Darzi
- University of Pittsburgh, Pittsburgh, U.S.A
| | | | | | | | | | | |
Collapse
|
13
|
Ertugrul IO, Yang L, Jeni LA, Cohn JF. D-PAttNet: Dynamic Patch-Attentive Deep Network for Action Unit Detection. FRONTIERS IN COMPUTER SCIENCE 2019; 1:11. [PMID: 31930192 PMCID: PMC6953909 DOI: 10.3389/fcomp.2019.00011] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Facial action units (AUs) relate to specific local facial regions. Recent efforts in automated AU detection have focused on learning the facial patch representations to detect specific AUs. These efforts have encountered three hurdles. First, they implicitly assume that facial patches are robust to head rotation; yet non-frontal rotation is common. Second, mappings between AUs and patches are defined a priori, which ignores co-occurrences among AUs. And third, the dynamics of AUs are either ignored or modeled sequentially rather than simultaneously as in human perception. Inspired by recent advances in human perception, we propose a dynamic patch-attentive deep network, called D-PAttNet, for AU detection that (i) controls for 3D head and face rotation, (ii) learns mappings of patches to AUs, and (iii) models spatiotemporal dynamics. D-PAttNet approach significantly improves upon existing state of the art.
Collapse
Affiliation(s)
- Itir Onal Ertugrul
- Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, United States
| | - Le Yang
- School of Computer Science, Northwestern Polytechnical University, Xian, China
| | - László A. Jeni
- Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, United States
| | - Jeffrey F. Cohn
- Department of Psychology, University of Pittsburgh, Pittsburgh, PA, United States
| |
Collapse
|