医疗场景下动态人类活动识别的视觉语言模型
Vision Language Models for Dynamic Human Activity Recognition in Healthcare Settings
AI总结：
针对VLMs在医疗远程健康监测的人类活动识别（HAR）中应用不足且输出难评估的问题，本文引入描述性字幕数据集并提出全面评估方法，实验表明VLMs性能可比甚至超过传统模型，为智能医疗系统整合VLMs提供基准与新可能。
摘要：
随着生成式AI不断发展，视觉语言模型（VLMs）已成为各类医疗应用中极具前景的工具。远程健康监测中的人类活动识别（HAR）是一个相对未被充分探索的领域。VLMs具有显著优势，包括更高的灵活性以及克服传统深度学习模型部分限制的能力。然而，将VLMs应用于HAR的关键挑战在于难以评估其动态且常为非确定性的输出。为填补这一空白，我们引入了一个描述性字幕数据集，并提出全面的评估方法来评估HAR中的VLMs。通过与最先进的深度学习模型进行对比实验，我们的研究结果表明，VLMs在准确性方面达到了可比性能，在某些情况下甚至超过了传统方法。这项工作提供了一个强有力的基准，并为将VLMs整合到智能医疗系统中开辟了新的可能性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
