Design and Performance Evaluation of An Arduino Based Activity Tracker

Fitness band is an activity tracker that monitors the overall health of the wearer and helps us to predict the fitness plan to be followed on the basis of the number of footstep of the wearer over span of time. The band is connected to an android app where it can show user the various stats monitored by the band and the possible health plan which can be used to achieve the necessary health goals. The fitness band will also help in informing family members in case of some medical emergency

Utilizing Radiomic Feature Analysis For Automated MRI Keypoint Detection: Enhancing Graph Applications

Graph neural networks (GNNs) present a promising alternative to CNNs and transformers in certain image processing applications due to their parameter-efficiency in modeling spatial relationships. Currently, a major area of research involves the converting non-graph input data for GNN-based models, notably in scenarios where the data originates from images. One approach involves converting images into nodes by identifying significant keypoints within them. Super-Retina, a semi-supervised technique, has been utilized for detecting keypoints in retinal images. However, its limitations lie in the dependency on a small initial set of ground truth keypoints, which is progressively expanded to detect more keypoints. Having encountered difficulties in detecting consistent initial keypoints in brain images using SIFT and LoFTR, we proposed a new approach: radiomic feature-based keypoint detection. Demonstrating the anatomical significance of the detected keypoints was achieved by showcasing their efficacy in improving registration processes guided by these keypoints. Subsequently, these keypoints were employed as the ground truth for the keypoint detection method (LK-SuperRetina). Furthermore, the study showcases the application of GNNs in image matching, highlighting their superior performance in terms of both the number of good matches and confidence scores. This research sets the stage for expanding GNN applications into various other applications, including but not limited to image classification, segmentation, and registration

Improving Aerial Instance Segmentation in the Dark with Self-Supervised Low Light Enhancement (Student Abstract)

Low light conditions in aerial images adversely affect the performance of several vision based applications. There is a need for methods that can efficiently remove the low light attributes and assist in the performance of key vision tasks. In this work, we propose a new method that is capable of enhancing the low light image in a self-supervised fashion, and sequentially apply detection and segmentation tasks in an end-to-end manner. The proposed method occupies a very small overhead in terms of memory and computational power over the original algorithm and delivers superior results. Additionally, we propose the generation of a new low light aerial dataset using GANs, which can be used to evaluate vision based networks for similar adverse conditions

Ultrasound-guided quadratus lumborum block versus ilioinguinal–iliohypogastric nerve block with wound infiltration for postoperative analgesia in unilateral inguinal surgeries: A randomised controlled trial

Background and Aims: Ultrasound (US)-guided quadratus lumborum (QL) block is an abdominal field block that has high efficacy in providing postoperative analgesia for abdominal surgeries. This study was undertaken to compare the US-guided QL block with ilioinguinal–iliohypogastric (IIIH) nerve block and local wound infiltration in unilateral inguinal surgeries, in terms of analgesia and overall patient satisfaction. Methods: This randomised controlled trial was conducted in two groups of thirty each. After the completion of surgery under spinal anaesthesia, patients in Group QL received 20 ml of inj. ropivacaine 0.5% while patients in Group IL received 10 ml of inj. ropivacaine 0.5% at the ilioinguinal–iliohypogastric nerve site and 10 ml of inj. ropivacaine 0.5% that was locally infiltrated at the surgical site. Duration of analgesia, Visual Analogue Scale (VAS) score, total requirement of analgesic dosage in the first 24 hours, and patient satisfaction score were compared in both the groups. Statistical analysis was performed using unpaired student's t test and Chi-squared test with IBM SPSS Statistics version 21 software. Results: Duration of analgesia was significantly higher in Group QL (544.83 ± 60.22 min) when compared with Group IL (350.67 ± 67.97 min ; P < 0.0001). VAS scores and analgesic requirements were also lower in Group QL. The patient satisfaction score was significantly higher in Group QL (3.93 ± 0.91) when compared to Group IL (3.4 ± 1.0; P < 0.05). Conclusion: US-guided QL block significantly prolongs the duration and quality of postoperative analgesia, thereby reducing analgesic consumption and increasing overall patient satisfaction