Vision Language Models for Dynamic Human Activity Recognition in Healthcare Settings
This work addresses the problem of remote health monitoring for healthcare systems by providing a benchmark for VLM integration, though it is incremental as it builds on existing VLM applications.
The paper tackled the challenge of evaluating Vision Language Models (VLMs) for dynamic human activity recognition in healthcare by introducing a descriptive caption dataset and comprehensive evaluation methods, finding that VLMs achieve comparable or superior accuracy to state-of-the-art deep learning models.
As generative AI continues to evolve, Vision Language Models (VLMs) have emerged as promising tools in various healthcare applications. One area that remains relatively underexplored is their use in human activity recognition (HAR) for remote health monitoring. VLMs offer notable strengths, including greater flexibility and the ability to overcome some of the constraints of traditional deep learning models. However, a key challenge in applying VLMs to HAR lies in the difficulty of evaluating their dynamic and often non-deterministic outputs. To address this gap, we introduce a descriptive caption data set and propose comprehensive evaluation methods to evaluate VLMs in HAR. Through comparative experiments with state-of-the-art deep learning models, our findings demonstrate that VLMs achieve comparable performance and, in some cases, even surpass conventional approaches in terms of accuracy. This work contributes a strong benchmark and opens new possibilities for the integration of VLMs into intelligent healthcare systems.