Background: Balance and proprioception are essential elements in postural control and injury prevention. Proprioception, the body’s sense of position and movement, is closely tied to balance, which depends on input from the visual, vestibular, and somatosensory systems. This article explores the link between trauma experiences and proprioceptive dysfunction, emphasizing how heightened muscle tone, dissociation, and altered sensory processing contribute to balance issues and the risk of injury. Method: The study included 48 participants, aged 18–25. Participants completed the Emotional Regulation Scale, Dissociative Experiences Scale II, and Childhood Trauma Questionnaire, after which they had to stand on a BTrackS Balance Plate while being exposed to images that are designed to evoke emotions from the OASIS image set. The balance plate software calculated outcomes of the participants’ postural sway (total sway, sway area, root mean square (RMS) to the mediolateral (ML) and anteroposterior (AP) way, and excursion to ML and AP ways). Results: Dissociative experience shows significant correlation with RMS ML when viewing positive pictures (rτ = 0.207, p = 0.045) and when viewing negative pictures again; scores with RMS ML (rτ = 0.204, p = 0.049) but also with RMS AP (rτ = 0.209, p = 0.042) and with Excursion ML (rτ = 0.200, p = 0.049) were significant. Experiences of physical abuse affected certain indicators of postural sway when viewing positive images compared to participants with no such experience (sway area: U = 374.50, p = 0.027; RMS AP: U = 383.50, p = 0.016; Excursion ML: U = 397.00, p = 0.007). Similarly, physical neglect affected postural sway during viewing of negative images (sway area: U = 366.50, p = 0.003; RMS AP: U = 371.00, p = 0.004; Excursion ML: U = 347.00, p = 0.034; and Excursion AP: U = 353.00, p = 0.010). Conclusions: The study highlights that dissociation disrupts balance in trauma survivors, especially under emotional stress which highlights the potential for motor-based treatments.

Introduction: The relationship between human emotions and their bodily expressions has been receiving increasing attention in psychology and cognitive sciences. Emotions are not only expressed through verbal and facial cues but also through posture, gestures, and movement patterns. Motion analysis is one of the possible methods for identifying emotions; however, there is currently no clear consensus on which method is the most suitable for this purpose. Objective: The aim of this study is to systematically review motion analysis methods used for emotion recognition and to compare human-based and artificial intelligence-based approaches. Method: This study follows a systematic literature review using the PRISMA protocol. The initial search identified 7699 scientific articles from various databases. After applying inclusion and exclusion criteria, 16 relevant studies were selected for detailed analysis. The research examines different motion analysis techniques, their applicability, and their accuracy. Results: A total of 9 different motion analysis methods were identified in the literature, of which 4 utilized artificial intelligence for emotion recognition. These methods differ in terms of data processing techniques, applied technologies, and accuracy in detecting emotions. Discussion: Motion analysis is a promising tool for studying emotions; however, no universally applicable technique currently exists. Artificial intelligence plays an increasing role in emotion recognition, yet challenges remain in algorithm development and handling individual differences in emotional expression. Conclusion: Currently, there is no single, universally accepted motion analysis technique for emotion identification. Artificial intelligence can be a useful tool, but it has limitations when used alone. The most reliable approach appears to be the application of multidisciplinary methods, which combine human observation, classical motion analysis, and machine learning techniques. Orv Hetil. 2025; 166(46): 1803–1809.