Abstract
In this brief report, we present the results of our investigation into the impact of age on reactions in the form of facial expressions to positive and negative feedback during human-computer interaction. In total, 30 subjects were analyzed after a video-recorded mental task in the style of a Wizard of Oz scenario. All subjects and their facial reactions were coded using the Facial Expression Coding System (FACES). To summarize briefly, we can conclude from our facial expression analysis that compared with their younger counterparts, elderly people show significantly lower levels of negative expression in response to positive feedback from the technical system (“Your performance is improving!”). This result indicates that elderly people seem to benefit more from praise during interaction than younger people, which is significant for the design of future companion technologies.
Highlights
Since the publication of Rosalind Picard’s theory on affective computing (Picard, 1995), many research labs are still trying to build computer programs or digital devices to improve human-computer interaction (HCI), not on-How to cite this paper: Rukavina, S., Gruss, S., Hoffmann, H., & Traue, H
Over the past decade of affective computing, research focused mainly on automatic emotion recognition with machine learning techniques based on different modalities, e.g., audio, video- or physiological features (Kächele et al, 2015; Rukavina et al, 2016b; Walter et al, 2011), which can be understood as the first important step
We analyzed the effect of differently valenced feedback in an HCI based on reactions in the form of facial expressions extracted from an accessible corpus OPEN_EmoRec II (Rukavina et al, 2015)
Summary
Since the publication of Rosalind Picard’s theory on affective computing (Picard, 1995), many research labs are still trying to build computer programs or digital devices to improve human-computer interaction (HCI), not on-How to cite this paper: Rukavina, S., Gruss, S., Hoffmann, H., & Traue, H. To be an empathetic and adaptive interactive partner, it is essential for the technical system to be able to recognize and respond to the emotional and motivational states of its user (Calvo et al, 2010), which in turn means that it can be defined as a companion technology (Traue et al, 2013; Wendemuth et al, 2012) Such companion technologies would be very useful and supportive for future HCI, especially for elderly people or people with limited cognitive abilities (Walter, et al, 2013). Considerable effort went into automated emotion recognition using machine learning algorithms, one additional important factor seemed to be neglected or even ignored: Effective feedback provided by the technical system This would, for example, address the kind of strategies the technical device can use to alter the user’s emotions if something goes awry during the interaction. We analyzed the influence of age as an additional independent variable on the effectiveness of feedback during a HCI, measured using the FACES facial expression measurement system according to (Kring et al, 2007; see 2.1)
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