Background and objectives: Patients suffering from neurological diseases may
develop dysarthria, a motor speech disorder affecting the execution of speech.
Close and quantitative monitoring of dysarthria evolution is crucial for
enabling clinicians to promptly implement patient management strategies and
maximizing effectiveness and efficiency of communication functions in term of
restoring, compensating or adjusting. In the clinical assessment of orofacial
structures and functions, at rest condition or during speech and non-speech
movements, a qualitative evaluation is usually performed, throughout visual
observation. Methods: To overcome limitations posed by qualitative assessments,
this work presents a store-and-forward self-service telemonitoring system that
integrates, within its cloud architecture, a convolutional neural network (CNN)
for analyzing video recordings acquired by individuals with dysarthria. This
architecture, called facial landmark Mask RCNN, aims at locating facial
landmarks as a prior for assessing the orofacial functions related to speech
and examining dysarthria evolution in neurological diseases. Results: When
tested on the Toronto NeuroFace dataset, a publicly available annotated dataset
of video recordings from patients with amyotrophic lateral sclerosis (ALS) and
stroke, the proposed CNN achieved a normalized mean error equal to 1.79 on
localizing the facial landmarks. We also tested our system in a real-life
scenario on 11 bulbar-onset ALS subjects, obtaining promising outcomes in terms
of facial landmark position estimation. Discussion and conclusions: This
preliminary study represents a relevant step towards the use of remote tools to
support clinicians in monitoring the evolution of dysarthria.

针对神经系统疾病患者可能发展为言语运动障碍疾病，本研究提出了一种存储和转发的自助式远程监测系统，该系统集成了卷积神经网络（CNN），通过分析患有言语运动障碍疾病者的视频录像实现面部特征的定位，并能监测疾病的进展，为临床医生提供支持和帮助。

一种基于存储转发的云计算远程监测系统，用于自动评估神经病变中失语症演变的视频记录分析

A store-and-forward cloud-based telemonitoring system for automatic  assessing dysarthria evolution in neurological diseases from video-recording  analysis

The need for remote tools for healthcare monitoring has never been more
apparent. Camera measurement of vital signs leverages imaging devices to
compute physiological changes by analyzing images of the human body. Building
on advances in optics, machine learning, computer vision and medicine these
techniques have progressed significantly since the invention of digital
cameras. This paper presents a comprehensive survey of camera measurement of
physiological vital signs, describing they vital signs that can be measured and
the computational techniques for doing so. I cover both clinical and
non-clinical applications and the challenges that need to be overcome for these
applications to advance from proofs-of-concept. Finally, I describe the current
resources (datasets and code) available to the research community and provide a
comprehensive webpage (this https URL) with links to these
resource and a categorized list of all the papers referenced in this article.

摄像头技术可以通过分析人体图像以计算生理变化，因此在遥测医疗监测方面有着极大的应用潜力，同时也存在一定的挑战和需要解决的问题。该研究论文综述了这一技术的现状、能够测量的生理指标及其计算方法，并涵盖了临床和非临床应用。