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Back pain is one of the leading causes of disabilityadjusted life year globally and the most common cause of low back pain is poor sitting posture. There are several actions that can be adopted proactively to avoid poor sitting posture induced back pain including behavioral change, regular exercise, and use of an ergonomic chair. However, these are either expensive and/or difficult to execute for prolonged periods. Sitting posture monitoring systems continuously observe the sitting pattern of a person in real-time and give feedback/alert poor sitting posture is observed. In this study, a real-time posture monitoring system has been designed and a functional prototype has been developed using simple electrical elements and an android application. Appropriate position of the sensor in the spine to measure the degree of bending and the threshold sensor values for good posture sittings have been determined based on the results from healthy volunteers of different ages and height. The android application continuously monitors the degree of bending and provides vibration when the bending reaches the threshold of bad posture or the duration of the sitting crosses the clinically recommended time limit to prevent prolonged sitting. Positive user feedback has been received in terms of comfortability, effectiveness, and satisfaction levels. The manufacturing cost of the developed monitoring system is minimal compared to the available expensive systems in the market and the cost would further go down if it is produced in bulk. This device efficiently monitors the sitting posture pattern to prevent back pain and within the affordable price range for the people from middle to under-developed countries.

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Thoracic disease detection from chest radiographs using deep learning methods has been an active area of research in the last decade. Most previous methods attempt to focus on the diseased organs of the image by identifying spatial regions responsible for significant contributions to the model's prediction. In contrast, expert radiologists first locate the prominent anatomical structures before determining if those regions are anomalous. Therefore, integrating anatomical knowledge within deep learning models could bring substantial improvement in automatic disease classification. This work proposes an anatomyaware attention-based architecture named Anatomy X-Net, that prioritizes the spatial features guided by the pre-identified anatomy regions. We leverage a semi-supervised learning method using the JSRT dataset containing organ-level annotation to obtain the anatomical segmentation masks (for lungs and heart) for the NIH and CheXpert datasets. The proposed Anatomy X-Net uses the pre-trained DenseNet-121 as the backbone network with two corresponding structured modules, the Anatomy Aware Attention (AAA) and Probabilistic Weighted Average Pooling (PWAP), in a cohesive framework for anatomical attention learning. Our proposed method sets new state-of-the-art performance on the official NIH test set with an AUC score of 0.8439, proving the efficacy of utilizing the anatomy segmentation knowledge to improve the thoracic disease classification. Furthermore, the Anatomy X-Net yields an averaged AUC of 0.9020 on the Stanford CheXpert dataset, improving on existing methods that demonstrate the generalizability of the proposed framework.

Despite the combined effort, the COVID-19 pandemic continues with a devastating effect on the healthcare system and the well-being of the world population. With a lack of RT-PCR testing facilities, one of the screening approaches has been the use of is chest radiography. In this paper, we propose an automatic chest x-ray image classification model that utilizes the pre-trained CNN architecture (DenseNet121, MobileNetV2) as a feature extractor, and wavelet transformation of the pre-processed images using the CLAHE algorithm and SOBEL edge detection. Our model can detect COVID-19 from x-ray images with high accuracy, sensitivity, specificity, and precision. The result analysis of different architectures and a comparison study of pre-processing techniques (Histogram Equalization and Edge Detection) are thoroughly examined. In this experiment, the Support Vector Machine (SVM) classifier fitted most accurately (accuracy 97.73%, sensitivity 97.84%, F1score 97.73%, specificity 97.73%, and precision 98.79%) with a wavelet and MobileNetV2 feature sets to identify COVID-19. The memory consumption is also examined to make the model more feasible for telemedicine and mobile healthcare application.