Fall detection is a major challenge for researchers. Indeed, a fall can cause injuries such as femoral neck fracture, brain hemorrhage, or skin burns, leading to significant pain. However, in some cases, trauma caused by an undetected fall can get worse with the time and conducts to painful end of life or even death. One solution is to detect falls efficiently to alert somebody (e.g., nurses) as quickly as possible. To respond to this need, we propose to detect falls in a real apartment of 40 square meters by exploiting three ultra-wideband radars and a deep neural network model. The deep neural network is composed of a convolutional neural network stacked with a long-short term memory network and a fully connected neural network to identify falls. In other words, the problem addressed in this paper is a binary classification attempting to differentiate fall and non-fall events. As it can be noticed in real cases, the falls can have different forms. Hence, the data to train and test the classification model have been generated with falls (four types) simulated by 10 participants in three locations in the apartment. Finally, the train and test stages have been achieved according to three strategies, including the leave-one-subject-out method. This latter method allows for obtaining the performances of the proposed system in a generalization context. The results are very promising since we reach almost 90% of accuracy.

A multitude of applications in engineering, ore processing, mineral exploration, and environmental science require grain recognition and the counting of minerals. Typically, this task is performed manually with the drawback of monopolizing both time and resources. Moreover, it requires highly trained personnel with a wealth of knowledge and equipment, such as scanning electron microscopes and optical microscopes. Advances in machine learning and deep learning make it possible to envision the automation of many complex tasks in various fields of science at an accuracy equal to human performance, thereby, avoiding placing human resources into tedious and repetitive tasks, improving time efficiency, and lowering costs. Here, we develop deep-learning algorithms to automate the recognition of minerals directly from the grains captured from optical microscopes. Building upon our previous work and applying state-of-the-art technology, we modify a superpixel segmentation method to prepare data for the deep-learning algorithms. We compare two residual network architectures (ResNet 1 and ResNet 2) for the classification and identification processes. We achieve a validation accuracy of 90.5% using the ResNet 2 architecture with 47 layers. Our approach produces an effective application of deep learning to automate mineral recognition and counting from grains while also achieving a better recognition rate than reported thus far in the literature for this process and other well-known, deep-learning-based models, including AlexNet, GoogleNet, and LeNet.

Identifying minerals is essential for geology, mineral exploration, engineering, and environmental sciences. Recent advances in machine learning have illustrated its potential as a fast, cost-effective, and reliable tool for identifying minerals from photographs or photomicrographs. However, in the recent literature, few studies have been dedicated to image acquisition. Machine learning generally requires reproducible, high-quality data to perform complicated tasks such as mineral identification to avoid common pitfalls. In this paper, we propose a practical image acquisition protocol for optical microscopes. This protocol focuses on ensuring reproducibility and enhancing image quality. To favor reproducibility, we detail dealing with camera errors, using reference color gauges, and establishing experimental parameters such as the external light source and temperature. For image enhancement, we explain the importance of lighting and its impact on machine learning precision, selection of the objective, and white balance calibration. In addition, we trialed the protocol on heavy mineral concentrate from till samples (20 species) with a typical deep learning model and it revealed that minor lighting modification (<5% difference in one channel) significantly increased misclassification rates: kyanite from 6.4% to 24.9% and monazite from 6.5% to 42.9%.