Using machine learning to improve the discriminative power of the FERD screener in classifying patients with schizophrenia and healthy adults

Test-Retest Reliability and Responsiveness of the Machine Learning-Based Short-Form of the Berg Balance Scale in Persons With Stroke

OBJECTIVE

To examine the test-retest reliability, responsiveness, and clinical utility of the machine learning-based short form of the Berg Balance Scale (BBS-ML) in persons with stroke.

DESIGN

Repeated-measures design.

SETTING

A department of rehabilitation in a medical center.

PARTICIPANTS

This study recruited 2 groups: 50 persons who were more than 6 months post-stroke to examine the test-retest reliability, and 52 persons who were within 3 months post-stroke to examine the responsiveness. Test-retest reliability was investigated by administering assessments twice at a 2-week interval. Responsiveness was investigated by gathering data at admission and discharge from the hospital.

INTERVENTIONS

Not applicable.

MAIN OUTCOME MEASURE

BBS-ML.

RESULTS

The BBS-ML exhibited excellent test-retest reliability (intraclass correlation coefficient=0.99), acceptable minimal random measurement error (minimal detectable change %=13.6%), and good responsiveness (Kazis' effect size and standardized response mean values≥1.34). On average, the participants completed the BBS-ML in around 6 minutes per administration.

CONCLUSIONS

Our findings indicate that the BBS-ML appears an efficient measure with excellent test-retest reliability and responsiveness. Moreover, the BBS-ML may be used as a substitute for the original BBS to monitor the progress of balance function in persons with stroke.

Improving Facial Emotion Recognition Using Residual Autoencoder Coupled Affinity Based Overlapping Reduction

Emotion recognition using facial images has been a challenging task in computer vision. Recent advancements in deep learning has helped in achieving better results. Studies have pointed out that multiple facial expressions may present in facial images of a particular type of emotion. Thus, facial images of a category of emotion may have similarity to other categories of facial images, leading towards overlapping of classes in feature space. The problem of class overlapping has been studied primarily in the context of imbalanced classes. Few studies have considered imbalanced facial emotion recognition. However, to the authors’ best knowledge, no study has been found on the effects of overlapped classes on emotion recognition. Motivated by this, in the current study, an affinity-based overlap reduction technique (AFORET) has been proposed to deal with the overlapped class problem in facial emotion recognition. Firstly, a residual variational autoencoder (RVA) model has been used to transform the facial images to a latent vector form. Next, the proposed AFORET method has been applied on these overlapped latent vectors to reduce the overlapping between classes. The proposed method has been validated by training and testing various well known classifiers and comparing their performance in terms of a well known set of performance indicators. In addition, the proposed AFORET method is compared with already existing overlap reduction techniques, such as the OSM, ν-SVM, and NBU methods. Experimental results have shown that the proposed AFORET algorithm, when used with the RVA model, boosts classifier performance to a greater extent in predicting human emotion using facial images.