Posture Identification and Detection for Understanding Cognitive Behavior

Human posture recognition has been of interest for the better part of the past decade due to its advantages in multiple domains. It has been noticed that human posture is correlated to their cognition or the environment they are subjected to. However, detecting and distinguishing body postures is impacted by several barriers such as environment, background, sounds, and so on. Hence, we try to correlate these external factors with human postures influenced due to their cognition. In this study, we attempt to develop a model that will be able to distinguish the postures caused due to diverse emotional and external factors. This is being done to further challenge computer vision to integrate with multimodal neurological features to identify whether the subject in question is under stress or not. The following study encompasses 3 different approaches using with the features to find the best working usable model.