The effects of surface inclination on gastrocnemius, soleus and tibialis anterior muscle activation during gait.

BACKGROUND: Inclined walking is associated with multiple musculoskeletal benefits and is considered a therapeutic exercise. Various patterns of increased and decreased muscle activation with inclined surfaces have been observed in normal muscles, with more focus on the proximal lower limb musculature. OBJECTIVE: The aim of this study was to assess the differences in electromyographic activation of gastrocnemius, soleus, and tibialis anterior at various inclined surfaces during gait. METHODS: Fourteen healthy male participants aged between 17-30 years walked at a self-selected speed at motor driven treadmill on 0, 2 and 4 degrees of inclination. EMG activity of the muscles was recorded using the Delsys Trigno surface EMG system. RESULTS: Results showed that muscular activation of tibialis anterior significantly decreased with increase in the level of inclination (p< 0.05). However, soleus, gastrocnemius medialis and gastrocnemius lateralis showed no significant differences (p> 0.05) in their muscular activation, and no noticeable trends were found. Furthermore, no significant difference was found between all the muscles at ground level and inclined level 2 and 4. CONCLUSION: These differences in activation patterns found in distal extremity can be useful for designing rehabilitation protocols in sports training and for patients with neurological and musculoskeletal pathologies.

Melanoma segmentation using deep learning with test-time augmentations and conditional random fields.

In a computer-aided diagnostic (CAD) system for skin lesion segmentation, variations in shape and size of the skin lesion makes the segmentation task more challenging. Lesion segmentation is an initial step in CAD schemes as it leads to low error rates in quantification of the structure, boundary, and scale of the skin lesion. Subjective clinical assessment of the skin lesion segmentation results provided by current state-of-the-art deep learning segmentation techniques does not offer the required results as per the inter-observer agreement of expert dermatologists. This study proposes a novel deep learning-based, fully automated approach to skin lesion segmentation, including sophisticated pre and postprocessing approaches. We use three deep learning models, including UNet, deep residual U-Net (ResUNet), and improved ResUNet (ResUNet++). The preprocessing phase combines morphological filters with an inpainting algorithm to eliminate unnecessary hair structures from the dermoscopic images. Finally, we used test time augmentation (TTA) and conditional random field (CRF) in the postprocessing stage to improve segmentation accuracy. The proposed method was trained and evaluated on ISIC-2016 and ISIC-2017 skin lesion datasets. It achieved an average Jaccard Index of 85.96% and 80.05% for ISIC-2016 and ISIC-2017 datasets, when trained individually. When trained on combined dataset (ISIC-2016 and ISIC-2017), the proposed method achieved an average Jaccard Index of 80.73% and 90.02% on ISIC-2017 and ISIC-2016 testing datasets. The proposed methodological framework can be used to design a fully automated computer-aided skin lesion diagnostic system due to its high scalability and robustness.