This study shows significant regional differences in strain during ramped isometric contractions for the patellar tendon. Lower proximal strains in the posterior tendon compared to the anterior region may be associated with the suggestion of "stress shielding" as an etiological factor in insertional tendinopathy.

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The work describes an automated method of tracking dynamic ultrasound images using a normalized cross-correlation algorithm, applied to the patellar and gastrocnemius tendon. Displacement was examined during active and passive tendon excursions using B-mode ultrasonography. In the passive test where two regions of interest (2-ROI) were tracked, the automated tracking algorithm showed insignificant deviations from relative zero displacement for the knee (0.01 ± 0.04 mm) and ankle (–0.02 ± 0.04 mm) (P> .05). Similarly, when tracking 1-ROI the passive tests showed no significant differences (P> .05) between automatic and manual methods, 7.50 ± 0.60 vs 7.66 ± 0.63 mm for the patellar and 11.28 ± 1.36 vs 11.17 ± 1.35 mm for the gastrocnemius tests. The active tests gave no significant differences (P> .05) between automatic and manual methods with differences of 0.29 ± 0.04 mm for the patellar and 0.26 ± 0.01 mm for the gastrocnemius. This study showed that automatic tracking of in vivo displacement of tendon during dynamic excursion under load is possible and valid when compared with the standardized method. This approach will save time during analysis and enable discrete areas of the tendon to be examined.

Facial expression recognition (FER) is the task of determining a person's current emotion. It plays an important role in healthcare, marketing, and counselling. With the advancement in deep learning algorithms like Convolutional Neural Network (CNN), the system's accuracy is improving. A hybrid CNN and k-Nearest Neighbour (KNN) model can improve FER's accuracy. This paper presents a hybrid CNN-KNN model for FER on the Raspberry Pi 4, where we use CNN for feature extraction. Subsequently, the KNN performs expression recognition. We use the transfer learning technique to build our system with an EfficientNet-Lite model. The hybrid model we propose replaces the Softmax layer in the EfficientNet with the KNN. We train our model using the FER-2013 dataset and compare its performance with different architectures trained on the same dataset. We perform optimization on the Fully Connected layer, loss function, loss optimizer, optimizer learning rate, class weights, and KNN distance function with the k-value. Despite running on the Raspberry Pi hardware with very limited processing power, low memory capacity, and small storage capacity, our proposed model achieves a similar accuracy of 75.26% (with a slight improvement of 0.06%) to the state-of-the-art's Ensemble of 8 CNN model.

In animal husbandry, the traceability of individual cattle, their health information, and performance records greatly depend on computer vision and image processing-based approaches. However, some of these approaches perform below expectations in obtaining real-time information about individual cattle. No doubt, inaccurate segmentation and incomplete extraction of each cattle object from an image are notable contributory factors. As accurate segmentation is a prerequisite for obtaining real-time information about individual cattle, and since the algorithm of Mask R-CNN relies on the algorithm of simultaneous localization and mapping (SLAM), for the construction of the semantic map, which sometimes exchanges image background for the foreground, there is a need to enhance the available approaches towards achieving precision animal husbandry. To achieve this, an enhanced Mask R-CNN instance segmentation method is proposed to support indistinct boundaries and irregular shapes of cattle bodies. The methods employed in the research are in multiple folds: (1) Pre-enhancement of the image using generalized color Fourier descriptors (GCFD); (2) Provision of optimal filter size that was smaller than ResNet101 (the backbone of Mask R-CNN) for the extraction of smaller and composite features;(3) Utilization of multiscale semantic features using region proposals; (4) A fully connected layer of existing Mask R-CNN integrated with a sub-network for enhanced segmentation and (5) Post-enhancement of the image using Grabcut. Experiments on the datasets of cattle images produced better results when compared to other state-of-the-art methods with 0.93 mAP.