Corrigendum: Virtual Avatar for Emotion Recognition in Patients with Schizophrenia: A Pilot Study

Marcos-Pablos, Samuel; González-Pablos, Emilio; Martín-Lorenzo, Carlos; Flores, Luis Alarcón; Gómez-García-Bermejo, Jaime; Zalama, Eduardo

doi:10.3389/fnhum.2016.00554

Cited by 3 publications

(20 citation statements)

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“…Compared with interventions, fewer studies were found for health-related assessments ( Tables 1 and 3 ); most of these were for people with autism spectrum disorder [ 94 , 96 , 99 ] and psychotic disorders [ 74 , 98 , 103 ]. Other studies used VH to assess PTSD symptoms [ 54 , 55 ], emotion recognition [ 95 ], and body image disorder [ 79 ]. Six studies used VH in a VR environment; 5 used HMDs [ 74 , 79 , 98 , 99 , 103 ], while 1 used a computer [ 96 ].…”

Section: Resultsmentioning

confidence: 99%

“…They were designed to be racially ambiguous [ 64 ] or accordant with target users [ 86 ]. But Marcos-Pablos et al [ 95 ] generated dynamic expressions for VH (eg, anger, happiness, sadness) by first using a laser scanner to obtain a 3D face model and then using animation software. The speech of VH was either synthetically produced [ 76 ] or lip-synced to audio-recorded voice narrations [ 58 , 92 ].…”

Section: Resultsmentioning

confidence: 99%

“…Of the 53 articles, 23 explicitly described their VH design process [ 55 , 58 , 59 , 68 , 71 , 73 , 75 , 78 , 80 , 82 - 87 , 89 - 93 , 95 , 97 , 104 ]. The most common design approaches were collaboration with domain experts, qualitative observation of similar human-human interactions, and adoption of public health and governmental guidelines.…”

Section: Resultsmentioning

confidence: 99%

“…A total of 26 studies (16 articles) published between 2000 and December 31, 2019 were eligible for the meta-analysis, targeting 11 types of populations and including 10 studies with healthy adults [ 68 , 69 , 81 , 83 , 87 ], 3 studies with older adults [ 63 , 67 ], 3 studies with women [ 58 , 88 ], 2 studies with individuals with schizophrenia [ 78 , 95 ], and 3 studies with individuals with depression [ 72 , 76 ].…”

Section: Resultsmentioning

confidence: 99%

“…Of the 26 studies, 9 focused on attitudes toward health and wellbeing [ 81 , 83 , 87 , 88 , 91 ]. There were more cross-sectional ( k =15) [ 54 , 68 , 81 , 83 , 87 , 88 , 91 , 92 , 95 ] than longitudinal studies ( k =11) [ 58 , 63 , 67 , 69 , 72 , 76 , 78 , 88 ]. Longitudinal studies ranged from 1 month to 6 months (see Multimedia Appendix 2 ).…”

Section: Resultsmentioning

confidence: 99%

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Computer-Controlled Virtual Humans in Patient-Facing Systems: Systematic Review and Meta-Analysis

Chattopadhyay¹,

Ma²,

Sharifi³

et al. 2020

J Med Internet Res

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Background Virtual humans (VH) are computer-generated characters that appear humanlike and simulate face-to-face conversations using verbal and nonverbal cues. Unlike formless conversational agents, like smart speakers or chatbots, VH bring together the capabilities of both a conversational agent and an interactive avatar (computer-represented digital characters). Although their use in patient-facing systems has garnered substantial interest, it is unknown to what extent VH are effective in health applications. Objective The purpose of this review was to examine the effectiveness of VH in patient-facing systems. The design and implementation characteristics of these systems were also examined. Methods Electronic bibliographic databases were searched for peer-reviewed articles with relevant key terms. Studies were included in the systematic review if they designed or evaluated VH in patient-facing systems. Of the included studies, studies that used a randomized controlled trial to evaluate VH were included in the meta-analysis; they were then summarized using the PICOTS framework (population, intervention, comparison group, outcomes, time frame, setting). Summary effect sizes, using random-effects models, were calculated, and the risk of bias was assessed. Results Among the 8,125 unique records identified, 53 articles describing 33 unique systems, were qualitatively, systematically reviewed. Two distinct design categories emerged — simple VH and VH augmented with health sensors and trackers. Of the 53 articles, 16 (26 studies) with 44 primary and 22 secondary outcomes were included in the meta-analysis. Meta-analysis of the 44 primary outcome measures revealed a significant difference between intervention and control conditions, favoring the VH intervention (SMD = .166, 95% CI .039-.292, P=.012), but with evidence of some heterogeneity, I2=49.3%. There were more cross-sectional (k=15) than longitudinal studies (k=11). The intervention was delivered using a personal computer in most studies (k=18), followed by a tablet (k=4), mobile kiosk (k=2), head-mounted display (k=1), and a desktop computer in a community center (k=1). Conclusions We offer evidence for the efficacy of VH in patient-facing systems. Considering that studies included different population and outcome types, more focused analysis is needed in the future. Future studies also need to identify what features of virtual human interventions contribute toward their effectiveness.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 3 more Smart Citations

Computer-Controlled Virtual Humans in Patient-Facing Systems: Systematic Review and Meta-Analysis

Chattopadhyay¹,

Ma²,

Sharifi³

et al. 2020

J Med Internet Res

View full text Add to dashboard Cite

show abstract

Computer-Controlled Virtual Humans in Patient-Facing Systems: Systematic Review and Meta-Analysis (Preprint)

Chattopadhyay¹,

Ma²,

Sharifi³

et al. 2020

Preprint

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BACKGROUND Virtual humans (VH) are computer-generated characters that appear humanlike and simulate face-to-face conversations using verbal and nonverbal cues. Although their use in patient-facing systems has garnered substantial interest, it is unknown to what extent VH are effective in health applications. OBJECTIVE The purpose of this review was to examine the effectiveness of VH in patient-facing systems. The design and implementation characteristics of these systems were also examined. METHODS Electronic bibliographic databases were searched for peer-reviewed articles with relevant key terms. Studies were included in the systematic review if they designed or evaluated VH in patient-facing systems. Out of them, studies that used a randomized controlled trial (RCT) to evaluate VH were included in a meta-analysis; they were then summarized using the PICOTS framework (population, intervention, comparison group, outcomes, time frame, setting). Summary effect sizes, using random-effects models, were calculated, and the risk of bias was assessed. RESULTS Among the 8,125 unique records identified, 53 articles, describing 33 unique systems, were qualitatively, systematically reviewed. Two distinct design categories emerged—simple VH and VH augmented with health sensors and trackers. Out of the 53, 16 articles (26 studies) with 44 primary and 22 secondary outcomes were included in the meta-analysis. Meta-analysis of the 44 primary outcome measures revealed a significant difference between intervention and control conditions, favoring the virtual human intervention (SMD = .166, 95% CI =.039, .292, P = .012), but with evidence of some heterogeneity, I2 = 49.3%. The intervention time was equally split between cross-sectional (k = 13) and longitudinal (k = 13). The intervention was delivered using a personal computer in most studies (k = 18), followed by a tablet (k = 3), mobile kiosk (k = 4), and desktop computer in a community center (k = 1). CONCLUSIONS We offer evidence for the efficacy of VH in patient-facing systems. Considering that studies included different population and outcome types, more focused analysis is needed in the future. Future studies also need to identify what features of virtual human interventions contribute toward their effectiveness. CLINICALTRIAL

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Using Facial Micro-Expressions in Combination With EEG and Physiological Signals for Emotion Recognition

et al. 2022

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Emotions are multimodal processes that play a crucial role in our everyday lives. Recognizing emotions is becoming more critical in a wide range of application domains such as healthcare, education, human-computer interaction, Virtual Reality, intelligent agents, entertainment, and more. Facial macro-expressions or intense facial expressions are the most common modalities in recognizing emotional states. However, since facial expressions can be voluntarily controlled, they may not accurately represent emotional states. Earlier studies have shown that facial micro-expressions are more reliable than facial macro-expressions for revealing emotions. They are subtle, involuntary movements responding to external stimuli that cannot be controlled. This paper proposes using facial micro-expressions combined with brain and physiological signals to more reliably detect underlying emotions. We describe our models for measuring arousal and valence levels from a combination of facial micro-expressions, Electroencephalography (EEG) signals, galvanic skin responses (GSR), and Photoplethysmography (PPG) signals. We then evaluate our model using the DEAP dataset and our own dataset based on a subject-independent approach. Lastly, we discuss our results, the limitations of our work, and how these limitations could be overcome. We also discuss future directions for using facial micro-expressions and physiological signals in emotion recognition.

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Corrigendum: Virtual Avatar for Emotion Recognition in Patients with Schizophrenia: A Pilot Study

Cited by 3 publications

References 0 publications

Computer-Controlled Virtual Humans in Patient-Facing Systems: Systematic Review and Meta-Analysis

Computer-Controlled Virtual Humans in Patient-Facing Systems: Systematic Review and Meta-Analysis

Computer-Controlled Virtual Humans in Patient-Facing Systems: Systematic Review and Meta-Analysis (Preprint)

Using Facial Micro-Expressions in Combination With EEG and Physiological Signals for Emotion Recognition

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