Affective states recognition through touch dynamics

Balducci, Fabrizio; Impedovo, Donato; Macchiarulo, Nicola; Pirlo, Giuseppe

doi:10.1007/s11042-020-09146-4

Cited by 12 publications

(22 citation statements)

References 55 publications

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“…Depression, Anxiety and Stress Scales (DASS) [98] is a tool consisting of 42 questions giving a score for each of the three states. It was used, for example, in [99] to detect depression, anxiety, and stress through touch dynamics.…”

Section: Data Labellingmentioning

confidence: 99%

“…These data are also processed to extract features, which may be useful for emotion recognition. Feature values are calculated on the basis of single strokes [99] or may be averaged over a series of them from a predefined time window [44] or session or a predefined number of consecutive strokes [41].…”

Section: Touch Dynamicsmentioning

confidence: 99%

“…More complex gestures, such as swipe or scroll, can be described by a set of simple parameters, e.g., stroke length [41,69,86,99], distance between the beginning and ending point of a stroke [69,86], speed [41,68,69,86,99], gesture duration [41,69,99], distance of the gesture from the centre of the screen or from the selected corner [69], pressure [41,69,86,99], and touch area [99]. In the case of pressure and touch area, the feature value has to be estimated on the basis of all points constituting a stroke, for example by averaging.…”

Section: Touch Dynamicsmentioning

confidence: 99%

“…It is also worth measuring the linearity of a stroke. The easiest way is to take into account the relation between the stroke length and the distance between its starting and ending points [99]. Another idea is to measure the average distance to a predefined line, when a user is supposed to follow it [68].…”

Section: Touch Dynamicsmentioning

confidence: 99%

See 3 more Smart Citations

A Review of Emotion Recognition Methods Based on Data Acquired via Smartphone Sensors

Kołakowska

Szwoch

2020

Sensors

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In recent years, emotion recognition algorithms have achieved high efficiency, allowing the development of various affective and affect-aware applications. This advancement has taken place mainly in the environment of personal computers offering the appropriate hardware and sufficient power to process complex data from video, audio, and other channels. However, the increase in computing and communication capabilities of smartphones, the variety of their built-in sensors, as well as the availability of cloud computing services have made them an environment in which the task of recognising emotions can be performed at least as effectively. This is possible and particularly important due to the fact that smartphones and other mobile devices have become the main computer devices used by most people. This article provides a systematic overview of publications from the last 10 years related to emotion recognition methods using smartphone sensors. The characteristics of the most important sensors in this respect are presented, and the methods applied to extract informative features on the basis of data read from these input channels. Then, various machine learning approaches implemented to recognise emotional states are described.

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Section: Data Labellingmentioning

confidence: 99%

Section: Touch Dynamicsmentioning

confidence: 99%

Section: Touch Dynamicsmentioning

confidence: 99%

Section: Touch Dynamicsmentioning

confidence: 99%

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A Review of Emotion Recognition Methods Based on Data Acquired via Smartphone Sensors

Kołakowska

Szwoch

2020

Sensors

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“…While affective computing has received more attention in humans due to its widespread implications for e.g. neuromarketing ( 5 ), entertainment ( 5 ), monitoring mental health ( 6 , 7 ) and human-robot interactions ( 8 ), the application of affective computing on farm animal welfare is however in its infancy and therefore, more multidisciplinary and exploratory studies are needed to further develop this highly promising field.…”

Section: Introductionmentioning

confidence: 99%

The Use of Artificial Intelligence in Assessing Affective States in Livestock

Neethirajan

2021

Front. Vet. Sci.

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In order to promote the welfare of farm animals, there is a need to be able to recognize, register and monitor their affective states. Numerous studies show that just like humans, non-human animals are able to feel pain, fear and joy amongst other emotions, too. While behaviorally testing individual animals to identify positive or negative states is a time and labor consuming task to complete, artificial intelligence and machine learning open up a whole new field of science to automatize emotion recognition in production animals. By using sensors and monitoring indirect measures of changes in affective states, self-learning computational mechanisms will allow an effective categorization of emotions and consequently can help farmers to respond accordingly. Not only will this possibility be an efficient method to improve animal welfare, but early detection of stress and fear can also improve productivity and reduce the need for veterinary assistance on the farm. Whereas affective computing in human research has received increasing attention, the knowledge gained on human emotions is yet to be applied to non-human animals. Therefore, a multidisciplinary approach should be taken to combine fields such as affective computing, bioengineering and applied ethology in order to address the current theoretical and practical obstacles that are yet to be overcome.

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