Assessment and Optimization of 1D CNN Model for Human Activity Recognition

  • Rafael Schild Reusch Pontifícia Universidade Católica do Rio Grande do Sul
  • Leonardo Rezende Juracy Pontifícia Universidade Católica do Rio Grande do Sul
  • Fernando Gehm Moraes Pontifícia Universidade Católica do Rio Grande do Sul

Resumo


Artificial Intelligence (AI) solves complex tasks like human activity and speech recognition. Accuracy-driven AI models introduced new challenges related to their applicability in resource-scarce systems. In Human Activity Recognition (HAR), state-of-the-art presents proposals using complex multi-layer LSTM networks. The literature states that LSTM networks are suitable for treating temporal-series data, a key feature for HAR. Most works in the literature seek the best possible accuracy, with few evaluating the overall computational cost to run the inference phase. In HAR, low-power IoT devices such as wearable sensors are widely used as data-gathering devices, but little effort is made to deploy AI technology in these devices. Most studies suggest an approach using edge devices or cloud computing architectures, where the end-device task is to gather and send data to the edge/cloud device. Most voice assistants, such as Amazon's Alexa and Google, use this architecture. In real-life applications, mainly in the healthcare industry, relying only on edge/cloud devices is not acceptable since these devices are not always available or reachable. The objective of this work is to evaluate the accuracy of convolutional networks with a simpler architecture, using 1D convolution, for HAR. The motivation for using networks with simpler network architectures is the possibility of embedding them in power- and memory-constrained devices.
Palavras-chave: Artificial Intelligence, Machine Learning, Human Activity Recognition, 1D Convolutional CNNs
Publicado
21/11/2022
REUSCH, Rafael Schild; JURACY, Leonardo Rezende; MORAES, Fernando Gehm. Assessment and Optimization of 1D CNN Model for Human Activity Recognition. In: SIMPÓSIO BRASILEIRO DE ENGENHARIA DE SISTEMAS COMPUTACIONAIS (SBESC), 12. , 2022, Fortaleza/CE. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2022 . p. 116-122. ISSN 2237-5430.