A Survey on Action Recognition: Multimodal Approaches, Ethical Considerations, and Feedback Mechanisms

Action recognition has emerged as a critical area of research within the realm of computer vision, driven by the increasing demand for intelligent human–machine systems capable of understanding and interpreting human behaviors in the real world. The ability to decipher intricate details of human actions holds immense potential to improve system design, predictive modeling, data-informed decision-making, and real-time operational improvements across a wide variety of domains. Some examples of applications range from surveillance and real-time management of public spaces and infrastructure systems, to development of predictive modeling and robotic systems for individualized healthcare interventions, to implementing effective human–computer interaction in both professional and recreational settings. This paper provides a comprehensive survey of the current state of action recognition, focusing specifically on three open-world challenges: the integration of multimodalities, the ethical and social implications of these technologies, and the utilization of feedback mechanisms to enhance model performance. We delve into the evolution of action recognition, from early feature-based approaches to the deep learning revolution, emphasizing how the incorporation of multiple sensory modalities—such as visual, audio, and depth data as well as other cues—has advanced the field. Furthermore, we examine the ethical challenges associated with deploying these technologies in the public domain, particularly regarding privacy, bias, and societal impact, and discuss the need for responsible development and regulation. The third focus of the paper is the use of top-down and bottom-up feedback mechanisms within deep learning architectures, exploring how these strategies can mimic human cognitive processes to improve accuracy and reliability in action recognition systems. By identifying current gaps and proposing future research directions, this paper aims to inspire continued innovation in this dynamic and impactful field for intelligent systems.