Sawada, Suda and Ishii (2003) investigated the relationships between the characteristics of arm movements (such as speed, strength, directness) and emotional expression. They found joy is portrayed with slow and less powerful movements. The difference with the expression of grief is that with joy the movements are in more different directions (more indirectness) and that the distance of the different movements is longer (more expansiveness).

Desmet, 2007; adapted from Russell, 1980.

Note from the DSD editorial: The eight core affects that are brought together according to two dimensions of emotion: Arousal and Valence, can be divided over the eight primary colour codes according to the method of the Semantic Colour Space. If we assume that the semantic depth dimension corresponds to Valence, and the breadth to Arousal, the following connections may be laid:
(V-,A-) boredom, sadness, isolation: code 010 or 000, green or blue.
(V-,A0) disappointment, contempt, jealousy: code 000 or 010, blue or green.
(V-,A+) alarm, disgust, irritation: code 001 or 011, black or purple.
(V0,A+) astonishment, eagerness, curiosity: code 011 or 111, purple or yellow.
(V+,A-) satisfaction, softened, relaxed: code 100 or 110, brown or white.
(V0,A-) awaiting, deferent, calm: code 110 or 010, white or green.
(V+,A0) admiration, fascination, joyfulness: code 111 or 101, yellow or red.
(V+,A+) inspiration, desire, love: code 101 or 111, red or yellow.

The height dimension, with dominance as the emotional 3rd dimension, was not applied in Desmet and Hekkert’s research.

Abstract
Many of us “see red,” “feel blue,” or “turn green with envy.” Are such color-emotion associations fundamental to our shared cognitive architecture, or are they cultural creations learned through our languages and traditions? To answer these questions, we tested emotional associations of colors in 4,598 participants from 30 nations speaking 22 native languages. Participants associated 20 emotion concepts with 12 color terms. Pattern-similarity analyses revealed universal color-emotion associations (average similarity coefficient r = .88). However, local differences were also apparent. A machine-learning algorithm revealed that nation predicted color-emotion associations above and beyond those observed universally. Similarity was greater when nations were linguistically or geographically close. This study highlights robust universal color-emotion associations, further modulated by linguistic and geographic factors. These results pose further theoretical and empirical questions about the affective properties of color and may inform practice in applied domains, such as well-being and design.

Jonauskaite et al. (2020).