Hybrid CNN-LSTM for Predicting Diabetes: A Review.

BACKGROUND: Diabetes is a common and deadly chronic disease caused by high blood glucose levels that can cause heart problems, neurological damage, and other illnesses. Through the early detection of diabetes, patients can live healthier lives. Many machine learning and deep learning techniques have been applied for noninvasive diabetes prediction. The results of some studies have shown that the CNN-LSTM method, a combination of CNN and LSTM, has good performance for predicting diabetes compared to other deep learning methods. METHOD: This paper reviews CNN-LSTM-based studies for diabetes prediction. In the CNNLSTM model, the CNN includes convolution and max pooling layers and is applied for feature extraction. The output of the max-pooling layer was fed into the LSTM layer for classification. DISCUSSION: The CNN-LSTM model performed well in extracting hidden features and correlations between physiological variables. Thus, it can be used to predict diabetes. The CNNLSTM model, like other deep neural network architectures, faces challenges such as training on large datasets and biological factors. Using large datasets can further improve the accuracy of detection. CONCLUSION: The CNN-LSTM model is a promising method for diabetes prediction, and compared with other deep-learning models, it is a reliable method.

Elbow angle generation during activities of daily living using a submovement prediction model.

The present study aimed to develop a realistic model for the generation of human activities of daily living (ADL) movements. The angular profiles of the elbow joint during functional ADL tasks such as eating and drinking were generated by a submovement-based closed-loop model. First, the ADL movements recorded from three human participants were broken down into logical phases, and each phase was decomposed into submovement components. Three separate artificial neural networks were trained to learn the submovement parameters and were then incorporated into a closed-loop model with error correction ability. The model was able to predict angular trajectories of human ADL movements with target access rate = 100%, VAF = 98.9%, and NRMSE = 4.7% relative to the actual trajectories. In addition, the model can be used to provide the desired target for practical trajectory planning in rehabilitation systems such as functional electrical stimulation, robot therapy, brain-computer interface, and prosthetic devices.

The Effects of Upper Limb Motor Recovery on Submovement Characteristics among the Patients with Stroke: A Meta-Analysis.

OBJECTIVE: To evaluate the evidence related to the effect of upper limb motor recovery on submovement characteristics, including duration, amplitude, overlap, interpeak distance, and the number of submovements in stroke patients using a meta-analysis. TYPE OF STUDY: Meta-analysis. LITERATURE SURVEY: The literature search was restricted to articles written in English published from inception to October 2018 in Web of Science, PubMed, Science Direct, IEEE Explore, MEDLINE, CDSR, Scopus, Compendex, Wiley Online Library, Springer Link, and REHABDATA. METHODOLOGY: Studies were included if they encompassed adult participants with a clinical diagnosis of stroke who underwent upper limb rehabilitation and if they assessed and reported submovement characteristics as the outcome measures in pre- and posttreatment stages. Changes in submovement characteristics between pre- and postinterventions were compared using the standardized mean difference (SMD). Finally, a test for heterogeneity and publication bias was implemented for all meta-analyses. SYNTHESIS: Among the 188 retrieved articles, seven of them (one randomized controlled trial, six pre-post) involving 259 patients were selected for meta-analysis. Based on the results, the overall observed changes in all meta-analyses were statistically significant. In total, submovement amplitude (SMD 0.624, 95% confidence interval [CI] [0.356, 0.893]), duration (SMD 0.61, 95% CI [0.332, 0.888]), and overlap (SMD 0.928, 95% CI [0.768, 1.088]) increased whereas interpeak distance (SMD -0.278, 95% CI [-0.42, -0.137]), and the total number of submovements (SMD -0.804, 95% CI [-1.069, -0.538]) decreased. CONCLUSIONS: The submovements appeared to become longer, fewer, and more overlapped with motor recovery. Based on the results, the ability of the neural system to blend submovements increased in both acute/subacute and chronic patients during recovery. Therefore, assessing the submovements during recovery can be a new quantitative measure of motor improvement, providing another means of comparing rehabilitation interventions and individualizing therapy for stroke patients.