通过视觉语言模型减少体育活动研究中的注释负担
Abram Schönfeldt1, Benjamin Maylor1, Xiaofang Chen2
1Department of Population Health, University of Oxford, Oxford, UK.
Scientific reports
|October 24, 2025
概括
开源视觉语言模型可以帮助从可穿戴摄像头数据中注释身体活动强度,减少类似人群中静坐行为的手动注释负担. 这有助于验证可穿戴设备的健康研究.
科学领域:
- 可穿戴式传感器技术的技术.
- 计算机视觉 计算机视觉 计算机视觉
- 机器学习用于健康.
背景情况:
- 可穿戴设备数据对于验证测量方法和开发用于健康研究的新机器学习模型至关重要.
- 人类对身上的摄像头数据的注释是一种常见但劳动密集型的方法,用于标记体育活动行为.
研究的目的:
- 调查开源视觉语言模型 (VLMs) 从可穿戴相机图像中注释身体活动强度的准确性.
- 评估VLM是否可以减少基于可穿戴相机的验证研究中的注释负担.
主要方法:
- 通过使用两个自由生活研究 (英国牛津郡;中国四川) 的数据,比较了三种VLM和两种歧视模型 (DM).
- 利用来自Autographer可穿戴摄像机的数据,跨越了161名和111名参与者的研究.
- 对预测活动强度类 (久坐,轻度,中度至强度的体力活动) 的评估模型性能.
主要成果:
- 在牛津郡的研究中,最佳的VLM和微调的DM在久坐行为方面表现相似 (F1中位数:VLM=0.89,DM=0.91).
- 两种模型类型的轻度和中度至强度体力活动的性能都下降了.
- 当应用于外部四川研究时,模型性能显著下降,这表明普遍性存在挑战.
结论:
- 自由可用的计算机视觉模型显示出在类似人群中的可穿戴摄像头图像中注释静止行为的潜力.
- 这些模型可以帮助降低体育活动研究中基准真相数据收集的注释负担.
- 需要进行进一步的研究,以提高模型在不同人群和活动类型中的通用性.
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