This study investigates the relationship between acute psychosocial stress and body posture and movements. Using inertial measurement unit (IMU)-based motion capture suits, researchers collected motion data from N = 59 individuals across two studies (Pilot Study: N = 20, Main Study: N = 39). Participants underwent the Trier Social Stress Test (TSST) and a stress-free control condition (friendly-TSST; f-TSST) in randomized order. The results show that acute stress induction leads to a reproducible freezing behavior, characterized by less overall motion and more and longer periods of no movement. Based on these data, machine learning pipelines were trained to detect acute stress solely from movement information, achieving an accuracy of 75.0 ± 17.7% (Pilot Study) and 73.4 ± 7.7% (Main Study). This suggests that body posture and movements can be used to detect whether individuals are exposed to acute psychosocial stress. The study is the first to systematically explore the use of full-body posture and movement to gain novel insights into the human stress response and its effects on the body and mind. The findings highlight the potential of motion information as a valuable extension to existing biomarkers and as a tool for obtaining a more holistic picture of the human stress response. The study also addresses the limitations of traditional stress assessment methods, such as self-reports and neuroendocrine biomarkers, and proposes motion data as a promising alternative. The results indicate that acute psychosocial stress leads to significant changes in body posture and movement, which can be detected using machine learning algorithms. The study provides a foundation for further research into the use of body posture and movement information as behavioral markers during stress.This study investigates the relationship between acute psychosocial stress and body posture and movements. Using inertial measurement unit (IMU)-based motion capture suits, researchers collected motion data from N = 59 individuals across two studies (Pilot Study: N = 20, Main Study: N = 39). Participants underwent the Trier Social Stress Test (TSST) and a stress-free control condition (friendly-TSST; f-TSST) in randomized order. The results show that acute stress induction leads to a reproducible freezing behavior, characterized by less overall motion and more and longer periods of no movement. Based on these data, machine learning pipelines were trained to detect acute stress solely from movement information, achieving an accuracy of 75.0 ± 17.7% (Pilot Study) and 73.4 ± 7.7% (Main Study). This suggests that body posture and movements can be used to detect whether individuals are exposed to acute psychosocial stress. The study is the first to systematically explore the use of full-body posture and movement to gain novel insights into the human stress response and its effects on the body and mind. The findings highlight the potential of motion information as a valuable extension to existing biomarkers and as a tool for obtaining a more holistic picture of the human stress response. The study also addresses the limitations of traditional stress assessment methods, such as self-reports and neuroendocrine biomarkers, and proposes motion data as a promising alternative. The results indicate that acute psychosocial stress leads to significant changes in body posture and movement, which can be detected using machine learning algorithms. The study provides a foundation for further research into the use of body posture and movement information as behavioral markers during stress.