The field of assistive technology and sign language recognition is rapidly evolving, with a focus on developing innovative solutions to improve communication and interaction for individuals with disabilities. Recent research has explored the use of vision language models, mobile applications, and machine learning algorithms to enhance sign language recognition, translation, and generation. Notably, the integration of vision language models has shown promising results in improving the accuracy and efficiency of sign language recognition systems. Additionally, the development of mobile applications and platforms has increased accessibility and usability for individuals with disabilities. Overall, these advancements have the potential to significantly impact the lives of individuals with disabilities and improve their ability to communicate and interact with others. Noteworthy papers include: Vision Language Models for Dynamic Human Activity Recognition in Healthcare Settings, which demonstrates the effectiveness of vision language models in human activity recognition. AquaVLM, which presents a tap-and-send underwater communication system that automatically generates context-aware messages. Gestura, which proposes an end-to-end system for free-form gesture understanding using a pre-trained Large Vision-Language Model.