Background: Pain assessment in individuals with neurological conditions,
especially those with limited self-report ability and altered facial
expressions, presents challenges. Existing measures, relying on direct
observation by caregivers, lack sensitivity and specificity. In cerebral palsy,
pain is a common comorbidity and a reliable evaluation protocol is crucial.
Thus, having an automatic system that recognizes facial expressions could be of
enormous help when diagnosing pain in this type of patient.
Objectives: 1) to build a dataset of facial pain expressions in individuals
with cerebral palsy, and 2) to develop an automated facial recognition system
based on deep learning for pain assessment addressed to this population.
Methods: Ten neural networks were trained on three pain image databases,
including the UNBC-McMaster Shoulder Pain Expression Archive Database, the
Multimodal Intensity Pain Dataset, and the Delaware Pain Database.
Additionally, a curated dataset (CPPAIN) was created, consisting of 109
preprocessed facial pain expression images from individuals with cerebral
palsy, categorized by two physiotherapists using the Facial Action Coding
System observational scale.
Results: InceptionV3 exhibited promising performance on the CP-PAIN dataset,
achieving an accuracy of 62.67% and an F1 score of 61.12%. Explainable
artificial intelligence techniques revealed consistent essential features for
pain identification across models.
Conclusion: This study demonstrates the potential of deep learning models for
robust pain detection in populations with neurological conditions and
communication disabilities. The creation of a larger dataset specific to
cerebral palsy would further enhance model accuracy, offering a valuable tool
for discerning subtle and idiosyncratic pain expressions. The insights gained
could extend to other complex neurological conditions.

研究使用深度学习技术构建面向脑瘫患者的自动面部表情识别系统，以提高神经病变和沟通障碍患者疼痛识别的准确性和敏感性。

基于深度学习的自动面部识别系统用于成年脑性麻痹病人的疼痛评估

Automated facial recognition system using deep learning for pain  assessment in adults with cerebral palsy

To make the earlier medical intervention of infants' cerebral palsy (CP),
early diagnosis of brain damage is critical. Although general movements
assessment(GMA) has shown promising results in early CP detection, it is
laborious. Most existing works take videos as input to make fidgety
movements(FMs) classification for the GMA automation. Those methods require a
complete observation of videos and can not localize video frames containing
normal FMs. Therefore we propose a novel approach named WO-GMA to perform FMs
localization in the weakly supervised online setting. Infant body keypoints are
first extracted as the inputs to WO-GMA. Then WO-GMA performs local
spatio-temporal extraction followed by two network branches to generate pseudo
clip labels and model online actions. With the clip-level pseudo labels, the
action modeling branch learns to detect FMs in an online fashion. Experimental
results on a dataset with 757 videos of different infants show that WO-GMA can
get state-of-the-art video-level classification and cliplevel detection
results. Moreover, only the first 20% duration of the video is needed to get
classification results as good as fully observed, implying a significantly
shortened FMs diagnosis time. Code is available at:
this https URL.

本研究提出了一种名为 WO-GMA 的新方法，可以在弱监督在线设置中定位儿童普通动作，从而实现早期诊断儿童脑瘫的目的，该方法可以极大地缩短诊断时间。

婴儿一般动作的弱监督在线检测

Weakly Supervised Online Action Detection for Infant General Movements

Video recording is a widely used method for documenting infant and child
behaviours in research and clinical practice. Video data has rarely been shared
due to ethical concerns of confidentiality, although the need of shared
large-scaled datasets remains increasing. This demand is even more imperative
when data-driven computer-based approaches are involved, such as screening
tools to complement clinical assessments. To share data while abiding by
privacy protection rules, a critical question arises whether efforts at data
de-identification reduce data utility? We addressed this question by showcasing
the Prechtl's general movements assessment (GMA), an established and globally
practised video-based diagnostic tool in early infancy for detecting
neurological deficits, such as cerebral palsy. To date, no shared
expert-annotated large data repositories for infant movement analyses exist.
Such datasets would massively benefit training and recalibration of human
assessors and the development of computer-based approaches. In the current
study, sequences from a prospective longitudinal infant cohort with a total of
19451 available general movements video snippets were randomly selected for
human clinical reasoning and computer-based analysis. We demonstrated for the
first time that pseudonymisation by face-blurring video recordings is a viable
approach. The video redaction did not affect classification accuracy for either
human assessors or computer vision methods, suggesting an adequate and
easy-to-apply solution for sharing movement video data. We call for further
explorations into efficient and privacy rule-conforming approaches for
deidentifying video data in scientific and clinical fields beyond movement
assessments. These approaches shall enable sharing and merging stand-alone
video datasets into large data pools to advance science and public health.

对于记录婴幼儿行为的视频数据，为了保护隐私，在去识别化人脸信息时不会影响数据的可用性，这使得共享更成为可能。而共享这种数据将有助于人工和电脑视觉方法的培训和重新校准，并为进一步的科学和公共卫生研究提供帮助。

发展与行为科学中的开放视频数据共享

Open video data sharing in developmental and behavioural science

This work explores the problem of exercise quality measurement since it is
essential for effective management of diseases like cerebral palsy (CP). This
work examines the assessment of quality of large amplitude movement (LAM)
exercises designed to treat CP in an automated fashion. Exercise data was
collected by trained participants to generate ideal examples to use as a
positive samples for machine learning. Following that, subjects were asked to
deliberately make subtle errors during the exercise, such as restricting
movements, as is commonly seen in cases of patients suffering from CP. The
quality measurement problem was then posed as a classification to determine
whether an example exercise was either "good" or "bad". Popular machine
learning techniques for classification, including support vector machines
(SVM), single and doublelayered neural networks (NN), boosted decision trees,
and dynamic time warping (DTW), were compared. The AdaBoosted tree performed
best with an accuracy of 94.68% demonstrating the feasibility of assessing
exercise quality.

本文旨在研究运动质量测量问题，以有效管理像脑瘫（CP）之类的疾病。通过机器学习技术，将大幅度运动（LAM）与亚健康练习进行自动化评估，发现 Adaboosted Tree 表现最佳，证明了评估运动质量的可行性。