An evidence-based toolset to Measure &amp; Assess Emotional Health

Hill, Edward

doi:10.1097/02024458-201012000-00010

Cited by 4 publications

(5 citation statements)

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“…There is no way of evaluating an emotion recognition system by comparing it with a grounded truth, since such thing does not exist in emotions (Hill et al, 2011). As a consequence, emotion recognition systems are typically evaluated by comparing their output with some assumed "real emotions" provided by a self-report method (Ramakrishnan, 2012).…”

Section: Evaluation Of Emotion Recognition Systemsmentioning

confidence: 99%

Bodily sensation maps: Exploring a new direction for detecting emotions from user self-reported data

Garca-Magario

Chittaro

Plaza

2018

International Journal of Human-Computer Studies

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This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Highlights We explore bodily sensation maps (BSMs) as a novel way to detect emotions We propose EmoPaint, a mobile app to collect BSMs and detect emotions from them A user study reveals that the app is easy to use and able to detect emotions The app improves accuracy over a traditional method: Affect Grid with Circumplex model

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Section: Evaluation Of Emotion Recognition Systemsmentioning

confidence: 99%

Bodily sensation maps: Exploring a new direction for detecting emotions from user self-reported data

Garca-Magario

Chittaro

Plaza

2018

International Journal of Human-Computer Studies

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“…Though several biomarkers for apathy are discussed in Hampel et al [6], automated apathy diagnosis is a novel research area of high impact and hence interest. The computer vision based analysis of face and gesture has shown to provide abundant information about different neurodegenerative disorders [10], [11], [12], [13], which we here aim at exploiting for apathy diagnosis.…”

Section: Related Workmentioning

confidence: 99%

“…Facial expression recognition and its applications in disease diagnosis: The emotional health, i.e. the ability to express emotions and identify others emotions, plays a major role in cognitive behavioral therapy [10], [15], [16]. Facial expression recognition, as an indicator of internal emotional state, has been widely explored in the last two decades [17], [18], [19].…”

Section: Related Workmentioning

confidence: 99%

Characterizing the State of Apathy with Facial Expression and Motion Analysis

Happy

Dantcheva

Das

et al. 2019

2019 14th IEEE International Conference on Automatic Face &Amp; Gesture Recognition (FG 2019)

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Reduced emotional response, lack of motivation, and limited social interaction comprise the major symptoms of apathy. Current methods for apathy diagnosis require the patient's presence in a clinic, and time consuming clinical interviews and questionnaires involving medical personnel, which are costly and logistically inconvenient for patients and clinical staff, hindering among other large scale diagnostics. In this paper we introduce a novel machine learning framework to classify apathetic and non-apathetic patients based on analysis of facial dynamics, entailing both emotion and facial movement. Our approach caters to the challenging setting of current apathy assessment interviews, which include short video clips with wide face pose variations, very low-intensity expressions, and insignificant inter-class variations. We test our algorithm on a dataset consisting of 90 video sequences acquired from 45 subjects and obtained an accuracy of 84% in apathy classification. Based on extensive experiments, we show that the fusion of emotion and facial local motion produces the best feature set for apathy classification. In addition, we train regression models to predict the clinical scores related to the mental state examination (MMSE) and the neuropsychiatric apathy inventory (NPI) using the motion and emotion features. Our results suggest that the performance can be further improved by appending the predicted clinical scores to the video-based feature representation.

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“…and the audio response (e.g., ''I am angry!'') was recorded on the web server [24]. The entire 15second dialogue is depicted in Figure 2.…”

Section: Emotional State Capture and Measurementioning

confidence: 99%

“…We monitored SUBX patients by using an evidence-based toolkit constructed from emotion detection in speech that can capture and accurately measure momentary emotional states of patients in their natural environment [22][23][24]. The benefits of this assessment toolkit, which includes the Experience Sample Method, are (1) collecting data on momentary states to avoid of recall deficits and bias, (2) ecological validity by data collection in the real-world, and (3) enabling analysis that is a dynamic process over time and can achieve temporal resolution.…”

Section: Introductionmentioning

confidence: 99%

Correction: Long Term Suboxone™ Emotional Reactivity As Measured by Automatic Detection in Speech

Hill¹,

Han²,

Dumouchel³

et al. 2013

PLoS ONE

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Addictions to illicit drugs are among the nation's most critical public health and societal problems. The current opioid prescription epidemic and the need for buprenorphine/naloxone (SuboxoneH; SUBX) as an opioid maintenance substance, and its growing street diversion provided impetus to determine affective states (''true ground emotionality'') in long-term SUBX patients. Toward the goal of effective monitoring, we utilized emotion-detection in speech as a measure of ''true'' emotionality in 36 SUBX patients compared to 44 individuals from the general population (GP) and 33 members of Alcoholics Anonymous (AA). Other less objective studies have investigated emotional reactivity of heroin, methadone and opioid abstinent patients. These studies indicate that current opioid users have abnormal emotional experience, characterized by heightened response to unpleasant stimuli and blunted response to pleasant stimuli. However, this is the first study to our knowledge to evaluate ''true ground'' emotionality in long-term buprenorphine/naloxone combination (Suboxone TM ). We found in long-term SUBX patients a significantly flat affect (p,0.01), and they had less self-awareness of being happy, sad, and anxious compared to both the GP and AA groups. We caution definitive interpretation of these seemingly important results until we compare the emotional reactivity of an opioid abstinent control using automatic detection in speech. These findings encourage continued research strategies in SUBX patients to target the specific brain regions responsible for relapse prevention of opioid addiction.

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An evidence-based toolset to Measure & Assess Emotional Health

Cited by 4 publications

References 0 publications

Bodily sensation maps: Exploring a new direction for detecting emotions from user self-reported data

Bodily sensation maps: Exploring a new direction for detecting emotions from user self-reported data

Characterizing the State of Apathy with Facial Expression and Motion Analysis

Correction: Long Term Suboxone™ Emotional Reactivity As Measured by Automatic Detection in Speech

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