The Process of Emotion Recognition in Athletes

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It has only be recently that emotions have been analysed competitive games citation needed . This paper provides an overview over relevant emotions for competitive sports and their possible effects on performance. Further, we analyse what features can be extracted from voice, video and still images and how they can be mapped to emotions. Because detailed annotation of videodata that itself is of good quality is still sparse or hard to access, we present a way to easily obtain lots of data and analyse problems and suggest possible solutions to them.

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What are emotions?

In order to work with emotions we first need a definition for it, a common seems to be the one of Deci: “An Emotion is a reaction to a stimulus event (either actual or imagined). It involves change in the viscera and musculature of the person, is experienced subjectively in characteristic ways, is expressed through such means as facial changes and action tendencies and may mediate and energize subsequent behaviors.”

According to Deci’s definition, an emotion is short term reaction to a recent stimulus and can therefore be influenced by a wide variety of external events and differentiated from a mood.

The way and intensity in which different athletes experience emotions differs a lot. While some of them experience strong emotions in the same setting others don’t and some can control them effectively.

Incidental Emotions

Incidental Emotions carry over from different situations but are still involved in the decision-making and are experienced as the decision is made. These emotions put us in a certain kind of mental mindset, and when a decision is in front of us, we sometimes have a hard time separating these emotions from the way we feel about the decision itself.

Integral Emotions

Contrary to incidental emotions, integral emotions are emotions arising by judgement or decisions themselves.

If thinking through a decision causes you some anxiety, that is useful information: it might be a sign that you need to be very cautious, and that you should potentially be more risk-averse rather than risk-seeking with the decision.

Following this view, anger, for example, provides the motivation to respond to injustice 5 (Solomon 1993), and anticipation of regret provides a reason to avoid excessive risk-taking 6 (Loomes & Sugden 1982).

On the flipside Integral emotions can negatively impact one’s decision by having an irrational fear of spiders, even though they are mostly harmless.

Ways of influence on performance

Lazarus (2000) identifies three main components of the psychology that affect performance, and which can be influenced by emotion: Motivation, Attention and Concentration. Motivation is needed to channel the available resources towards the goal in the game itself as well as the training and the free time. To be able to properly react to a given situation the athlete must pay attention to what is going on in the game. Concentration is needed for choosing the right actions and strategies to succeed in the game.

These components are affected by interference from exterior factors such as goals or thoughts that aren’t in line with the current game.

Facial expressions

Facial expressions are the primary Language and one of the most natural ways for human beings to communicate their emotions and intentions with each other. Due to its variety in expressions and the amount of annotated, available data, the face it is a good candidate for automated feature extraction.

Facial expressions inform about moods and emotions such as joy, anger and irritableness. They express the current cognitive activity such as concentration and characteristic traits such as shyness. Despite the correlation between them, not every facial expression occurs when there is a certain emotion on display, it is often the case that it is rather subtle. The intensity and way certain expressions are displayed differs among humans.

To find a common denominator of describing facial expressions different coding systems were developed

Facial Action Coding System (FACS).

The Facial Action Coding System (FACS) is a comprehensive and widely used system to enumerate facial activities by their appearance. The system was invented by Carl Herman Hjortsjö in 1970 and further developed in 1978 by Paul Ekman and Wallace V. Friesen with significant updates in 2002. The rating-system has been proven to work well independent of the trained judge when it comes to the classification of occurrence, intensity, and timing of individual action units. The independence allows for usage in recognition of basic emotions. Despite having been originally developed for humans, FACS has been adapted for other species such as dogs and various monkeys.

FACS features are divided into permanent features s such as the eyebrows, lips, eye and cheeks and the transient features e.g. nose wrinkles and dimples. These features are transformed when muscles contract or relax beneath the surface of the skin and thus altering their appearance. Smiling for example stretches the lips by using about 42 muscles, but it may not be possible to reliably distinguish between the specific contracted muscles and to decide which muscle-portions are used. As a consequence, FACS is not defined as the measurement of muscle activity. Instead Action Units (AUs) are defined. AUs are the result of a combination of contracted musclegroups. There are about 66 AUs defined for facial expression, 9 for the upper and 18 for the lower part of the face, 5 that can’t be assigned to either of them, 11 for the head position, 9 for the eye position and 14 for miscellaneous action descriptors. In total there have been 5000 different combinations of these AU observed.

How exactly are they defined?

The coding system was used together with the 527 page manual to train participants to dissect images or videos into its specific action units for about 100 hours. Automatizing this process is the subject-matter of a lot of different theses.

One idea to keep track of these features is by using a Multistate Facial Component Model as Tian et. Al present. Their model uses manually modelled features that uses color, shape, and motion and keeps track of the position of manually modelled key points that are distributed across parts of the face such as the lips or the eyes.

Problems of Action Unit recognition and the Facial Action Coding System

Some of the problems that occur when using FACS are due to the inter-individual differences between faces. Some may have permanent features that look like an AU although the related muscles are not in use. This may include eyebrows in certain shapes or transient features that become permanent with age such as wrinkling of the skin or sagging of the facial musculature. Two limits of FACS are the lack of temporal and detailed spatial information (both local and global). It is argued that an expression is not made of static points but rather of the change of the tracked points in time (which also limits the use for animation and closely related tasks).

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