Real-time body pose non-verbal communication with a consistency-based reliability measure
Researchers have developed a method for real-time recognition of communicative intent using only 2D body pose data. This approach is particularly useful for low-cost, on-device person-to-robot communication in long-distance scenarios like rescue missions. The study introduces a new dataset with ten communicative intents and benchmarks various models, evaluating both accuracy and frame rate on embedded hardware. Additionally, the research proposes using a model's autoregressive self-consistency as an unsupervised reliability measure for its predictions. AI
IMPACT Enables more intuitive and robust human-robot interaction in environments where visual or auditory cues are limited.