实时自动检测老年人的手势在家庭和临床设置。

IF 4.5 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Guan Huang, Son N Tran, Quan Bai, Jane Alty
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引用次数: 1

摘要

由于COVID-19大流行,迫切需要临床医生和神经科学家能够远程评估手部运动的方法。这将有助于检测和监测在老年人中特别普遍的退行性脑疾病。随着计算机摄像机的广泛使用,基于视觉的实时手势检测方法将促进家庭和临床环境中的在线评估。然而,在快速移动的手部数据采集中,运动模糊是最具挑战性的问题之一。这项研究的目的是开发一种基于计算机视觉的方法,利用在现实生活中收集的视频数据,准确地检测老年人的手势。我们邀请50岁以上的成年人在家中或诊所完成有效的手部运动测试(快速手指敲击和手开合)。数据的收集没有研究者的监督,通过一个网站程序使用标准的笔记本电脑和台式相机。我们对图像进行处理和标记,将数据分别分为训练、验证和测试,然后分析不同的网络结构对手势的检测效果。我们招募了1900名成年人(年龄在50-90岁之间)作为TAS测试项目的一部分,并开发了utas7k - 7071个手势图像的新数据集,将4:1分为清晰:运动模糊图像。我们的新网络RGRNet在清晰图像上实现了0.782的平均精度(mAP),优于最先进的网络结构(YOLOV5-P6, mAP 0.776)和mAP 0.771在模糊图像上。一个新的强大的实时自动化网络,可以从单个摄像机检测静态手势,RGRNet,和一个新的数据库,包括最大范围的单个手,UTAS7k,都显示出强大的医疗和研究应用潜力。补充信息:在线版本包含补充资料,下载地址:10.1007/s00521-022-08090-8。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Real-time automated detection of older adults' hand gestures in home and clinical settings.

Real-time automated detection of older adults' hand gestures in home and clinical settings.

Real-time automated detection of older adults' hand gestures in home and clinical settings.

Real-time automated detection of older adults' hand gestures in home and clinical settings.

There is an urgent need, accelerated by the COVID-19 pandemic, for methods that allow clinicians and neuroscientists to remotely evaluate hand movements. This would help detect and monitor degenerative brain disorders that are particularly prevalent in older adults. With the wide accessibility of computer cameras, a vision-based real-time hand gesture detection method would facilitate online assessments in home and clinical settings. However, motion blur is one of the most challenging problems in the fast-moving hands data collection. The objective of this study was to develop a computer vision-based method that accurately detects older adults' hand gestures using video data collected in real-life settings. We invited adults over 50 years old to complete validated hand movement tests (fast finger tapping and hand opening-closing) at home or in clinic. Data were collected without researcher supervision via a website programme using standard laptop and desktop cameras. We processed and labelled images, split the data into training, validation and testing, respectively, and then analysed how well different network structures detected hand gestures. We recruited 1,900 adults (age range 50-90 years) as part of the TAS Test project and developed UTAS7k-a new dataset of 7071 hand gesture images, split 4:1 into clear: motion-blurred images. Our new network, RGRNet, achieved 0.782 mean average precision (mAP) on clear images, outperforming the state-of-the-art network structure (YOLOV5-P6, mAP 0.776), and mAP 0.771 on blurred images. A new robust real-time automated network that detects static gestures from a single camera, RGRNet, and a new database comprising the largest range of individual hands, UTAS7k, both show strong potential for medical and research applications.

Supplementary information: The online version contains supplementary material available at 10.1007/s00521-022-08090-8.

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来源期刊
Neural Computing & Applications
Neural Computing & Applications 工程技术-计算机:人工智能
CiteScore
11.40
自引率
8.30%
发文量
1280
审稿时长
6.9 months
期刊介绍: Neural Computing & Applications is an international journal which publishes original research and other information in the field of practical applications of neural computing and related techniques such as genetic algorithms, fuzzy logic and neuro-fuzzy systems. All items relevant to building practical systems are within its scope, including but not limited to: -adaptive computing- algorithms- applicable neural networks theory- applied statistics- architectures- artificial intelligence- benchmarks- case histories of innovative applications- fuzzy logic- genetic algorithms- hardware implementations- hybrid intelligent systems- intelligent agents- intelligent control systems- intelligent diagnostics- intelligent forecasting- machine learning- neural networks- neuro-fuzzy systems- pattern recognition- performance measures- self-learning systems- software simulations- supervised and unsupervised learning methods- system engineering and integration. Featured contributions fall into several categories: Original Articles, Review Articles, Book Reviews and Announcements.
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