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首页> 外文会议>European conference on computer vision >A Multi-modal Machine Learning Approach and Toolkit to Automate Recognition of Early Stages of Dementia Among British Sign Language Users
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A Multi-modal Machine Learning Approach and Toolkit to Automate Recognition of Early Stages of Dementia Among British Sign Language Users

机译：一种多模态机器学习方法和工具包，以自动识别英国标志语言用户中痴呆症的早期阶段

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The ageing population trend is correlated with an increased prevalence of acquired cognitive impairments such as dementia. Although there is no cure for dementia, a timely diagnosis helps in obtaining necessary support and appropriate medication. Researchers are working urgently to develop effective technological tools that can help doctors undertake early identification of cognitive disorder. In particular, screening for dementia in ageing Deaf signers of British Sign Language (BSL) poses additional challenges as the diagnostic process is bound up with conditions such as quality and availability of interpreters, as well as appropriate questionnaires and cognitive tests. On the other hand, deep learning based approaches for image and video analysis and understanding are promising, particularly the adoption of Convolutional Neural Network (CNN), which require large amounts of training data. In this paper, however, we demonstrate novelty in the following way: a) a multi-modal machine learning based automatic recognition toolkit for early stages of dementia among BSL users in that features from several parts of the body contributing to the sign envelope, e.g., hand-arm movements and facial expressions, are combined, b) universality in that it is possible to apply our technique to users of any sign language, since it is language independent, c) given the trade-off between complexity and accuracy of machine learning (ML) prediction models as well as the limited amount of training and testing data being available, we show that our approach is not over-fitted and has the potential to scale up.
机译：老龄化人口趋势与获得的认知障碍等患病率增加相关，例如痴呆症。虽然痴呆症没有治愈，但及时诊断有助于获得必要的支持和适当的药物。研究人员迫切工作，开发有效的技术工具，可以帮助医生进行早期识别认知障碍。特别地，在英国手语的老化聋签署者中筛查痴呆症签署者（BSL）造成额外的挑战，因为诊断过程受到诸如口译员的质量和可用性的条件以及适当的调查问卷和认知测试。另一方面，基于深度学习的图像和视频分析和理解方法是有前途的，特别是采用卷积神经网络（CNN），这需要大量的培训数据。然而，在本文中，我们以下列方式证明了新颖的方式：a）基于多模态机器学习的自动识别工具，用于BSL用户中的痴呆症的早期阶段，其中来自身体的几个部分有助于标志信封，例如，手臂运动和面部表情组合，B）普遍性地，可以将我们的技术应用于任何手语的用户，因为它是语言独立的，c）在机器复杂性和准确性之间进行权衡学习（ML）预测模型以及可用的培训和测试数据有限，我们表明我们的方法没有过度装配，并且有可能扩展。

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