Recognition of Intensive Valence and Arousal Affective States via Facial Electromyographic Activity in Young and Senior Adults

Abstract

BackgroundResearch suggests that interaction between humans and digital environments characterizes a form of companionship in addition to technical convenience. To this effect, humans have attempted to design computer systems able to demonstrably empathize with the human affective experience. Facial electromyography (EMG) is one such technique enabling machines to access to human affective states. Numerous studies have investigated the effects of valence emotions on facial EMG activity captured over the corrugator supercilii (frowning muscle) and zygomaticus major (smiling muscle). The arousal emotion, specifically, has not received much research attention, however. In the present study, we sought to identify intensive valence and arousal affective states via facial EMG activity.MethodsTen blocks of affective pictures were separated into five categories: neutral valence/low arousal (0VLA), positive valence/high arousal (PVHA), negative valence/high arousal (NVHA), positive valence/low arousal (PVLA), and negative valence/low arousal (NVLA), and the ability of each to elicit corresponding valence and arousal affective states was investigated at length. One hundred and thirteen participants were subjected to these stimuli and provided facial EMG. A set of 16 features based on the amplitude, frequency, predictability, and variability of signals was defined and classified using a support vector machine (SVM).ResultsWe observed highly accurate classification rates based on the combined corrugator and zygomaticus EMG, ranging from 75.69% to 100.00% for the baseline and five affective states (0VLA, PVHA, PVLA, NVHA, and NVLA) in all individuals. There were significant differences in classification rate accuracy between senior and young adults, but there was no significant difference between female and male participants.ConclusionOur research provides robust evidences for recognition of intensive valence and arousal affective states in young and senior adults. These findings contribute to the successful future application of facial EMG for identifying user affective states in human machine interaction (HMI) or companion robotic systems (CRS).

Facial EMG Correlates of Subjective Hedonic Responses During Food Consumption

“…Artifact rejection using statistical methods, such as independent component analysis [63], may remove artifacts related to mouth movements and may increase the signal-to-noise ratio. Nonlinear analysis using machine learning [64] may more sensitively detect associations between subjective hedonic experiences and facial EMG activity while eating food.…”

Elderly People Benefit More from Positive Feedback Based on Their Reactions in the Form of Facial Expressions during Human-Computer Interaction

“…Due to demographic changes in our society, age plays an important role as a subject-specific variable, particularly for future companion technologies involving HCI. To the best of our knowledge, however, age has rarely been observed in past affective computing studies, although there are some exceptions, e.g., Tan et al (2016). This seems to be at odds with the fact that several articles do report age differences during emotion induction, recorded, e.g., by means of physiological measurements such as skin conductance or facial electromyography (Burriss et al, 2007).…”

“…This seems to be at odds with the fact that several articles do report age differences during emotion induction, recorded, e.g., by means of physiological measurements such as skin conductance or facial electromyography (Burriss et al, 2007). Furthermore, age was also reported as influencing automatic emotion classification accuracy, which is an integral element of affective computing (Rukavina et al, 2016b;Tan et al, 2016).…”

Subject-independent decoding of affective states using functional near-infrared spectroscopy

“…During the next 30 seconds, a new figure was displayed every 5 seconds, totaling six randomly selected figures per trial corresponding to the desired affective class (Figure 2). Presenting a group of images with the same valence ensures the maintenance of cognitive engagement (Tie et al, 2009) and the required duration to achieve the peak of oxygen concentration change relative to baseline (Schroeter et al, 2006). At the end of the trial, a new screen was presented asking the participant to assign a score from 1 to 9 for the subjective valence (1extremely negative valence; 9highly positive valence) and subjective arousal (1lower arousal; 9higher arousal) experiences.…”