Novel approaches to human activity recognition based on accelerometer data
Carregando...
Data
Título da Revista
ISSN da Revista
Título de Volume
Editor
Universidade Federal de Minas Gerais
Descrição
Tipo
Artigo de periódico
Título alternativo
Primeiro orientador
Membros da banca
Resumo
An increasing number of works have investigated the use of convolutional neural network (ConvNets) approaches to perform human activity recognition (HAR) based on wearable sensor data. These approaches present state-of-the-art results in HAR, outperforming traditional approaches, such as handcrafted methods and 1D convolutions. Motivated by this, in this work we propose a set of methods to enhance ConvNets for HAR. First, we propose a data augmentation which enables the ConvNets to learn more adequately the patterns of the signal. Second, we exploit the attitude estimation of the accelerometer data to devise a set of novel feature descriptors which allow the ConvNets to better discriminate the activities. Finally, we propose a novel ConvNet architecture to explore the patterns among the accelerometer axes throughout the layers that compose the network. We demonstrate that this is a simpler way of improving the activity recognition instead of proposing more complex architectures, serving as direction to future works with the purpose of building ConvNets architectures. The experimental results show that our proposed methods achieve notable improvements and outperform existing state-of-the-art methods.
Abstract
Assunto
Aprendizado do computador, Identificação de sistemas
Palavras-chave
Análise de dados sensoriais, Dados de acelerômetro, Reconhecimento de Atividades, Human activity recognition, Accelerometer data, Attitude estimation features, Convolutional neural networks, In the past decade, human activity recognition (HAR) hasbeen an active research topic, mostly because of its directapplications in person identification, health care,homeland security and smart environments. For this pur-pose, sensor-based data have been widely explored due totheir easy acquisition and fast processing in dedicated wear-able sensors. Recent technological advances haveallowed the employment of smartphones and smartwatchesto perform HAR, since these devices provide inertial sensorssuch as accelerometers, gyroscopes and barometers.
Citação
Curso
Endereço externo
https://link.springer.com/article/10.1007/s11760-018-1293-x