This research addresses the challenge of deploying real-time drinking gesture detection in messy, "in-the-wild" environments. We propose and evaluate two computationally inexpensive systems, one using a Random Forest classifier, another using a 1-Dimensional Convolutional Neural Net (1D-CNN) classifier. Both are trained on 2D skeleton data, or features derived from that solely that data. Tested on the Conflab social interaction dataset, our method is designed to handle sparse labels and significant data occlusion. This study reports on the performance of this light-weight, video-based approach, providing a benchmark for applicability in real-world health and human-computer-interaction applications where privacy and computational efficiency are important factors. Although we were unable to create a robust and reliable classifier (f1 of 0.07 and 0.03 respectively), this work shows that there is potential for future work to succeed (roc-auc’s of 0.63 and 0.55 respectively) and provides critical insights into pitfalls to avoid when designing similar systems.

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.

This study investigates the feasibility of detecting drinking behavior in social environments using chest-mounted accelerometer data. A dataset collected during a conference is used, consisting of accelerometer data and annotated video recordings of 48 participants. After preprocessing, a random forest classifier is trained using statistical features: mean, correlation, maximum, minimum, and covariance derived from the yand z-axes of acceleration data. Evaluation through sixfold cross-validation for one participant yields an accuracy of 79%, while a leave-one-out cross-validation across participants achieves 70% accuracy. Qualitative analysis of false predictions reveals that actions like nodding, walking while drinking, or movement of the drinking hand towards the face can mimic drinking behavior. These findings demonstrate that accelerometer data contains detectable signals of drinking behavior even in noisy real-world conditions. However, further improvements require more diverse training data, consistent annotation, and possibly the inclusion of additional movement categories. The results support the potential of wearable accelerometers for drinking monitoring in social settings.

Laughter is a complex multimodal behavior and one of the most essential aspects of social interactions. Although previous research has used both auditory and facial cues for laughter detection, these approaches are commonly afflicted with difficulties in noisy, occluded, and privacy-sensitive settings. This paper explores the potential of using body posture alone—captured through 2D keypoint estimation as a robust signal for automatic laughter detection in naturalistic settings. We create a machine learning pipeline using the ConfLab dataset, which segments pose data, extracts motion-based features, and trains Random Forest classifiers on various annotation modalities (audio-only, video-only, and audiovisual) and segmentation methods (fixed and variable length). We show that, while variable-length segmentation yields optimal performance, it leads to overfitting. On the other hand, fixed-duration segmentation with three-second windows and audiovisual annotations achieves a pragmatic compromise and reaches F1-scores (65\%) comparable to earlier efforts in ideal environments. Upper-body movement, especially head and arm motion, is seen to be salient cues to laughter via feature importance analysis. Annotation modality is also found to significantly affect both classification performance and relative pose feature importance. These findings demonstrate the viability of pose-based laughter detection and reveal how annotation choices shape model behavior, offering insights for affective computing in the wild.