# Motion as Emotion: Detecting Affect and Cognitive Load from Free-Hand Gestures in VR

arXiv: https://arxiv.org/abs/2409.12921 · HTML: https://mlsanity.com/paper/2409.12921/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Sep 2024.

## Built on by (most cited first, 1 of 1)

- [Cognitive State Inference from VR Motion via Motion Foundation Model](https://mlsanity.com/paper/2509.24255/) (2025)

## Abstract (arXiv)

Affect and cognitive load influence many user behaviors. In this paper, we propose Motion as Emotion, a novel method that utilizes fine differences in hand motion to recognise affect and cognitive load in virtual reality (VR). We conducted a study with 22 participants who used common free-hand gesture interactions to carry out tasks of varying difficulty in VR environments. We find that the affect and cognitive load induced by tasks are associated with significant differences in gesture features such as speed, distance and hand tension. Standard support vector classification (SVC) models could accurately predict two levels (low, high) of valence, arousal and cognitive load from these features. Our results demonstrate the potential of Motion as Emotion as an accurate and reliable method of inferring user affect and cognitive load from free-hand gestures, without needing any additional wearable sensors or modifications to a standard VR headset.