We propose a method for detecting laughter in spontaneous social interactions using chest-worn accelerometers. Our approach compares three segmentation strategies—padded, centered, different sliding win- dowssizesandevaluatesannotationmodalities: No Audio, Only Audio, and With Audio. Using time-domain features and Random Forests, we reach up to 0.962 macro F1-score. Longer windows and multimodal annotations improve performance and generalizability. Key features include axis-wise means, deriva- tives, and inter-axis correlations. These results support the potential of motion-based laughter detection in privacy-sensitive environments, while highlighting the importance of segment and label design.