A Novel Model to Generate Heterogeneous and Realistic Time-Series Data for Post-Stroke Rehabilitation Assessment.

The application of machine learning-based tele-rehabilitation faces the challenge of limited availability of data. To overcome this challenge, data augmentation techniques are commonly employed to generate synthetic data that reflect the configurations of real data. One such promising data augmentation technique is the Generative Adversarial Network (GAN). However, GANs have been found to suffer from mode collapse, a common issue where the generated data fails to capture all the relevant information from the original dataset. In this paper, we aim to address the problem of mode collapse in GAN-based data augmentation techniques for post-stroke assessment. We applied the GAN to generate synthetic data for two post-stroke rehabilitation datasets and observed that the original GAN suffered from mode collapse, as expected. To address this issue, we propose a Time Series Siamese GAN (TS-SGAN) that incorporates a Siamese network and an additional discriminator. Our analysis, using the longest common sub-sequence (LCSS), demonstrates that TS-SGAN generates data uniformly for all elements of two testing datasets, in contrast to the original GAN. To further evaluate the effectiveness of TS-SGAN, we encode the generated dataset into images using Gramian Angular Field and classify them using ResNet-18. Our results show that TS-SGAN achieves a significant accuracy increase of classification accuracy (35.2%-42.07%) for both selected datasets. This represents a substantial improvement over the original GAN.

Monocular 3D Human Pose Markerless Systems for Gait Assessment.

Gait analysis plays an important role in the fields of healthcare and sports sciences. Conventional gait analysis relies on costly equipment such as optical motion capture cameras and wearable sensors, some of which require trained assessors for data collection and processing. With the recent developments in computer vision and deep neural networks, using monocular RGB cameras for 3D human pose estimation has shown tremendous promise as a cost-effective and efficient solution for clinical gait analysis. In this paper, a markerless human pose technique is developed using motion captured by a consumer monocular camera (800 × 600 pixels and 30 FPS) for clinical gait analysis. The experimental results have shown that the proposed post-processing algorithm significantly improved the original human pose detection model (BlazePose)'s prediction performance compared to the gold-standard gait signals by 10.7% using the MoVi dataset. In addition, the predicted T2 score has an excellent correlation with ground truth (r = 0.99 and y = 0.94x + 0.01 regression line), which supports that our approach can be a potential alternative to the conventional marker-based solution to assist the clinical gait assessment.

A comprehensive evaluation of state-of-the-art time-series deep learning models for activity-recognition in post-stroke rehabilitation assessment.

The recent COVID-19 pandemic has further high-lighted the need for improving tele-rehabilitation systems. One of the common methods is to use wearable sensors for monitoring patients and intelligent algorithms for accurate and objective assessments. An important part of this work is to develop an efficient evaluation algorithm that provides a high-precision activity recognition rate. In this paper, we have investigated sixteen state-of-the-art time-series deep learning algorithms with four different architectures: eight convolutional neural networks configurations, six recurrent neural networks, a combination of the two and finally a wavelet-based neural network. Additionally, data from different sensors' combinations and placements as well as different pre-processing algorithms were explored to determine the optimal configuration for achieving the best performance. Our results show that the XceptionTime CNN architecture is the best performing algorithm with normalised data. Moreover, we found out that sensor placement is the most important attribute to improve the accuracy of the system, applying the algorithm on data from sensors placed on the waist achieved a maximum of 42% accuracy while the sensors placed on the hand achieved 84%. Consequently, compared to current results on the same dataset for different classification categories, this approach improved the existing state of the art accuracy from 79% to 84%, and from 80% to 90% respectively.

Quantitative assessment of collagenase blends for human islet isolation.

BACKGROUND: The variability in collagenase blends has been speculated as the single most important determinant of the success or failure in isolated islet yields in clinical islet transplantation. Examination of the formulation and potency of the widely used Liberase HI enzyme blend will uncover possible sources of imprecision. METHODS: High performance liquid chromatography (HPLC) and kinetic measurements of collagenase and protease activity were used to assess potency. Between four and nine clinical lots were assessed for various parameters such as relative formulation of collagenase isoforms, and recovered collagenase and protease potencies postreconstitution. RESULTS: Six vials from a single typical lot had a mean enzyme content of 489+/-62.5 mg (mean+/-SEM; range 398-610 mg). The mean recovered collagenase activity was 2235+/-310 Wünsch units (WU)/vial (range 1794-2968 WU/vial). The percent coefficients of variation for collagenase and protease activity in these vials were 17.4%, and 13.4%, respectively. The increase in the presence of the collagenase Ib (CIb) isoform detected by HPLC analysis was related to the chronological order of the date of manufacture. The CIb isoform was found to have a reduced specific activity compared to intact collagenase I (CI) (3.8+/-1.2 WU/mg vs. 2.1+/-0.7 WU/mg, P < 0.05). The presence of CIb was related to reduced islet yields in twelve human isolations studied. CONCLUSIONS: Variation in potency was observed between, and within lots of Liberase HI in this study. Differences in relative collagenase isoform composition may also affect the stability and potency characteristics of these blends.