BERT for Activity Recognition Using Sequences of Skeleton Features and Data Augmentation with GAN

Recently, the scientific community has placed great emphasis on the recognition of human activity, especially in the area of health and care for the elderly. There are already practical applications of activity recognition and unusual conditions that use body sensors such as wrist-worn devices or neck pendants. These relatively simple devices may be prone to errors, might be uncomfortable to wear, might be forgotten or not worn, and are unable to detect more subtle conditions such as incorrect postures. Therefore, other proposed methods are based on the use of images and videos to carry out human activity recognition, even in open spaces and with multiple people. However, the resulting increase in the size and complexity involved when using image data requires the use of the most recent advanced machine learning and deep learning techniques. This paper presents an approach based on deep learning with attention to the recognition of activities from multiple frames. Feature extraction is performed by estimating the pose of the human skeleton, and classification is performed using a neural network based on Bidirectional Encoder Representation of Transformers (BERT). This algorithm was trained with the UP-Fall public dataset, generating more balanced artificial data with a Generative Adversarial Neural network (GAN), and evaluated with real data, outperforming the results of other activity recognition methods using the same dataset.This research was supported in part by the Chilean Research and Development Agency (ANID) under Project FONDECYT 1191188, The National University of Distance Education under Projects 2021V/-TAJOV/00 and OPTIVAC 096-034091 2021V/PUNED/008, and the Ministry of Science and Innovation of Spain under Project PID2019-108377RB-C32

Human Activity Recognition by Sequences of Skeleton Features

In recent years, much effort has been devoted to the development of applications capable of detecting different types of human activity. In this field, fall detection is particularly relevant, especially for the elderly. On the one hand, some applications use wearable sensors that are integrated into cell phones, necklaces or smart bracelets to detect sudden movements of the person wearing the device. The main drawback of these types of systems is that these devices must be placed on a person’s body. This is a major drawback because they can be uncomfortable, in addition to the fact that these systems cannot be implemented in open spaces and with unfamiliar people. In contrast, other approaches perform activity recognition from video camera images, which have many advantages over the previous ones since the user is not required to wear the sensors. As a result, these applications can be implemented in open spaces and with unknown people. This paper presents a vision-based algorithm for activity recognition. The main contribution of this work is to use human skeleton pose estimation as a feature extraction method for activity detection in video camera images. The use of this method allows the detection of multiple people’s activities in the same scene. The algorithm is also capable of classifying multi-frame activities, precisely for those that need more than one frame to be detected. The method is evaluated with the public UP-FALL dataset and compared to similar algorithms using the same dataset.This research was supported in part by the Chilean Research and Development Agency (ANID) under Project FONDECYT 1191188. The National University of Distance Education under Project 2021V/-TAJOV/00 and Ministry of Science and Innovation of Spain under Project PID2019-108377RB-C32

Fall detection and activity recognition using human skeleton features

Human activity recognition has attracted the attention of researchers around the world. This is an interesting problem that can be addressed in different ways. Many approaches have been presented during the last years. These applications present solutions to recognize different kinds of activities such as if the person is walking, running, jumping, jogging, or falling, among others. Amongst all these activities, fall detection has special importance because it is a common dangerous event for people of all ages with a more negative impact on the elderly population. Usually, these applications use sensors to detect sudden changes in the movement of the person. These kinds of sensors can be embedded in smartphones, necklaces, or smart wristbands to make them “wearable” devices. The main inconvenience is that these devices have to be placed on the subjects’ bodies. This might be uncomfortable and is not always feasible because this type of sensor must be monitored constantly, and can not be used in open spaces with unknown people. In this way, fall detection from video camera images presents some advantages over the wearable sensor-based approaches. This paper presents a vision-based approach to fall detection and activity recognition. The main contribution of the proposed method is to detect falls only by using images from a standard video-camera without the need to use environmental sensors. It carries out the detection using human skeleton estimation for features extraction. The use of human skeleton detection opens the possibility for detecting not only falls but also different kind of activities for several subjects in the same scene. So this approach can be used in real environments, where a large number of people may be present at the same time. The method is evaluated with the UP-FALL public dataset and surpasses the performance of other fall detection and activities recognition systems that use that dataset

Study of the implementation of nanoparticles for the detection of arsenic in water

En el presente trabajo se lleva a cabo un estudio sobre la detección de arsénico en el agua implementando nanopartículas, ya que el uso de partículas en tamaño nanométrico ha demostrado ser una técnica apta para detectar arsénico en el agua debido a su alta complejidad de detección. Se elabora el presente estudio con el fin de que este tipo de técnica sea implementada por todos los países, en especial los países con alto riesgo de contaminación por arsénico. La información sobre la presencia de arsénico en el agua, sus consecuencias y métodos de detección se toma de aplicaciones y estudios realizados en otros países, se identifican los procedimientos requeridos para lograr la detección de arsénico presente en el agua con nanopartículas y se realiza la simulación de las nano partículas de arsénico, alúmina activada, nZVI/AC y perlas poliméricas; Con lo cual se analiza cómo se comportan en el agua, como la afectan y el método más adecuado.In the present work, a study is carried out on the detection of arsenic in water by applying nanoparticles, since the use of particles in nanometric size has proved to be a technique to detect arsenic in water due to high detection complexity. The present study is elaborated so that this type of technique is implemented by all the countries, especially the countries with high risk of contamination by arsenic. The information on the presence of arsenic in water, its consequences and methods of detection are based on applications and studies conducted in other countries, identify the procedures necessary to achieve the detection of arsenic present in water with nanoparticles and simulation of nanoparticles Arsenic, activated alumina, nZVI / AC and polymer beads; This discusses how they behave in the water, how they affect and the most appropriate method

BERT for Activity Recognition Using Sequences of Skeleton Features and Data Augmentation with GAN

Recently, the scientific community has placed great emphasis on the recognition of human activity, especially in the area of health and care for the elderly. There are already practical applications of activity recognition and unusual conditions that use body sensors such as wrist-worn devices or neck pendants. These relatively simple devices may be prone to errors, might be uncomfortable to wear, might be forgotten or not worn, and are unable to detect more subtle conditions such as incorrect postures. Therefore, other proposed methods are based on the use of images and videos to carry out human activity recognition, even in open spaces and with multiple people. However, the resulting increase in the size and complexity involved when using image data requires the use of the most recent advanced machine learning and deep learning techniques. This paper presents an approach based on deep learning with attention to the recognition of activities from multiple frames. Feature extraction is performed by estimating the pose of the human skeleton, and classification is performed using a neural network based on Bidirectional Encoder Representation of Transformers (BERT). This algorithm was trained with the UP-Fall public dataset, generating more balanced artificial data with a Generative Adversarial Neural network (GAN), and evaluated with real data, outperforming the results of other activity recognition methods using the same dataset