Facial expressions to identify post-stroke: A pilot study.

BACKGROUND AND OBJECTIVE: Timely stroke treatment can limit brain damage and improve outcomes, which depends on early recognition of the symptoms. However, stroke cases are often missed by the first respondent paramedics. One of the earliest external symptoms of stroke is based on facial expressions. METHODS: We propose a computerized analysis of facial expressions using action units to distinguish between Post-Stroke and healthy people. Action units enable analysis of subtle and specific facial movements and are interpretable to the facial expressions. The RGB videos from the Toronto Neuroface Dataset, which were recorded during standard orofacial examinations of 14 people with post-stroke (PS) and 11 healthy controls (HC) were used in this study. Action units were computed using XGBoost which was trained using HC, and classified using regression analysis for each of the nine facial expressions. The analysis was performed without manual intervention. RESULTS: The results were evaluated using leave-one-our validation. The accuracy was 82% for Kiss and Spread, with the best sensitivity of 91% in the differentiation of PS and HC. The features corresponding to mouth muscles were most suitable. CONCLUSIONS: This pilot study has shown that our method can detect PS based on two simple facial expressions. However, this needs to be tested in real-world conditions, with people of different ethnicities and smartphone use. The method has the potential for a computerized assessment of the videos for use by the first respondents using a smartphone to perform screening tests, which can facilitate the timely start of the treatment.

Human Factors in Sensory Feedback for Haptic Gloves.

Manual therapy training requires close proximity between the clinical teacher and students, which limits the training of people in remote and rural regions. Video-based online training can provide visual but not tactile information, which is also essential for manual therapies. This project describes the development and testing of an inexpensive sensor glove developed using commercially available sensors, suitable for monitoring the shape and force applied by the hand of a person delivering a spinal manipulation. Its focus was the development of software to provide the human user with tactile information that is usually acquired intuitively in face-to-face teaching. Though rigorous assessment of the glove's application showed errors at low levels of force in actual force measurement and interpretation by users, these errors were reduced at higher levels of force. Trainers of spinal manipulation reported the device to be very useful and suitable for the purpose. We conclude that this glove has the potential for being used for online training of students.Clinical Impact: The outcome of this study shows the feasibility of developing an inexpensive haptic glove using proprietary software for online training of students of manual therapy.

Application of StyleGAN Architecture for Generating Venous Leg Ulcer Images.

Leg ulcers caused by impaired venous blood return are the most typical chronic wound form and have a significant negative impact on the lives of people living with these wounds. Thus, it is important to provide early assessment and appropriate treatment of the wounds to promote their healing in the normal trajectory. Gathering quality wound data is an important component of good clinical care, enabling monitoring of healing progress. This data can also be useful to train machine learning algorithms with a view to predicting healing. Unfortunately, a high volume of good-quality data is needed to create datasets of suitable volume from people with wounds. In order to improve the process of gathering venous leg ulcer (VLU) data we propose the generative adversarial network based on StyleGAN architecture to synthesize new images from original samples. We utilized a dataset that was manually collected as part of a longitudinal observational study of VLUs and successfully synthesized new samples. These synthesized samples were validated by two clinicians. In future work, we plan to further process these new samples to train a fully automated neural network for ulcer segmentation.

Tabular data augmentation for video-based detection of hypomimia in Parkinson's disease.

BACKGROUND AND OBJECTIVE: This paper presents a method for the computerized detection of hypomimia in people with Parkinson's disease (PD). It overcomes the difficulty of the small and unbalanced size of available datasets. METHODS: A public dataset consisting of features of the video recordings of people with PD with four facial expressions was used. Synthetic data was generated using a Conditional Generative Adversarial Network (CGAN) for training augmentation. After training the model, Test-Time Augmentation was performed. The classification was conducted using the original test set to prevent bias in the results. RESULTS: The employment of CGAN followed by Test-Time Augmentation led to an accuracy of classification of the videos of 83%, specificity of 82%, and sensitivity of 85% in the test set that the prevalence of PD was around 7% and where real data was used for testing. This is a significant improvement compared with other similar studies. The results show that while the technique was able to detect people with PD, there were a number of false positives. Hence this is suitable for applications such as population screening or assisting clinicians, but at this stage is not suitable for diagnosis. CONCLUSIONS: This work has the potential for assisting neurologists to perform online diagnose and monitoring their patients. However, it is essential to test this for different ethnicity and to test its repeatability.