Temporal Approaches for Human Activity Recognition Using Inertial Sensors

  • Felipe Garcia USP
  • Caetano Ranieri USP
  • Roseli Romero USP

Resumo


Human Activity Recognition (HAR) involves classifying one person's activity based on sensor data. In this work, inertial data, collected mainly from wearable sensors, are used to recognize HAR by using Convolutional Neural Networks (CNN) to extract features from the raw sensor data. Additionally, Temporal Convolutional Networks (TCN) are applied to classify the extracted features, comparing the overall performance of those layers with Long Short-Term Memory (LSTM) recurrent neural network layers. Several experiments are performed and the results show that TCN based architectures are able to outperform LSTM based architectures in sequence modeling.
Palavras-chave: Feature extraction, Convolution, Wearable sensors, Computer architecture, Mathematical model, Robots
Publicado
23/10/2019
GARCIA, Felipe; RANIERI, Caetano; ROMERO, Roseli. Temporal Approaches for Human Activity Recognition Using Inertial Sensors. In: SIMPÓSIO BRASILEIRO DE ROBÓTICA E SIMPÓSIO LATINO AMERICANO DE ROBÓTICA (SBR/LARS), 16. , 2019, Rio Grande. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2019 . p. 120-124.