Beyond pose data, leveraging additional types of human motion data can enhance the understanding of long-term multi-agent interactions:
Gait Analysis: Incorporating gait analysis data, including stride length, cadence, and walking patterns, can provide valuable insights into human locomotion and interaction dynamics over time.
Gesture Recognition: Utilizing data on gestures, hand movements, and body language can help capture non-verbal communication cues and social interactions among agents in a multi-agent setting.
Emotion Recognition: Integrating emotion recognition data can offer a deeper understanding of human behavior and intentions, enabling models to predict how emotions influence interactions and movements over extended periods.
Environmental Context: Considering environmental factors such as obstacles, terrain variations, and spatial constraints can enrich the dataset with contextual information that influences human motion and interactions in complex scenarios.