In the past decades, there have been numerous advancements in the field of technology. This has led to many scientific breakthroughs in the field of medical sciences. In this, rapidly transforming world we are having a difficult time and the problem of fatigue is becoming prevalent. So, this study aimed to understand what is fatigue, its repercussions, and techniques to detect it using machine learning (ML) approaches. This paper introduces, discusses methods and recent advancements in the field of fatigue detection. Further, we categorized the methods that can be used to detect fatigue into four diverse groups, that is, mathematical models, rule‐based implementation, ML, and deep learning. This study presents, compares, and contrasts various algorithms to find the most promising approach that can be used for the detection of fatigue. Finally, the paper discusses the possible areas for improvement.
Machine learning has been proven to be a game-changing technology in every domain since the late 20th century. There have been many advancements in healthcare not only for the diagnosis of disease but advanced in the prognosis of the diseases. Artificial intelligence/machine learning (AI/ML) has progressed a lot in the medical domain in just a couple of decades and played a very important role in exploring human data to understand human body behavior better than ever before, for predicting and classifying all kinds of medical images or videos. A recent and best-used application is detecting COVID-19 by just checking the chest x-ray in a very accurate manner that can be used without human presence and stop the spread of the virus resulting in fewer doctors getting affected. It is known as generative adversarial networks. Some of the types of GANs used for differentiate domains without human supervision and many such mutations of GANs are useful in the health sector. This is simply a quick review of various technologies that will become more in-depth as time goes on.
Coconut is a multipurpose fruit with high economic value and since it is unique to the landscape of Kerala, it plays an important role in the economy of the state. Skilled labour is one of the key components in coconut farming and lack of its availability can hurt its business. Even if this requirement is met, currently practiced traditional methods for plucking the fruit requires the labour to climb the tree which involves a huge risk factor given the height of the tree they have to scale. There are tools that assist in the climb but they can only reduce the risk factor by a small margin. Robotic harvesting is one of the key solutions to the aforementioned problem as it has the ability to perform accurate coconut plucking since it relies on cutting edge object detection modules, it can provide deep insights into the quality of coconuts to be yielded and also excel at working in remote conditions. The primary aim of this paper is to cover the development of a fast as well as accurate perception module for detection of coconuts, which will serve as a strong foundation for any robotic implementation. In this study we try to explore and compare multiple deep learning based object detection frameworks such as Single Shot Detector and YOLO for efficient and accurate deployment on various edge devices like Raspberry Pi and Nvidia jetson nano by using state of the art methods such as quantization aware training, inference accelerators, multiple augmentation strategies (cutmix, mosaic) for best results. We have also curated a novel, manually annotated dataset of drone based coconut videos (effective/usable content of 30 minutes) in order to capture the naturally setting of coconuts i.e. the true distribution of image data containing background noises, occlusion, shadow as well as natural lighting conditions. The peak performance achieved in our study was a frame rate of 12.7 with a mean average precision of 0.4 by using a tiny YOLOv4 on an Nvidia Jetson Nano.
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