Human activity recognition through deep learning: Leveraging unique and common feature fusion in wearable multi-sensor systems

This publication has 33 references indexed in Scilit:

• Tool wear predicting based on multi-domain feature fusion by deep convolutional neural network in milling operations
  Journal of Intelligent Manufacturing, 2019
• A Semisupervised Recurrent Convolutional Attention Model for Human Activity Recognition
  IEEE Transactions on Neural Networks and Learning Systems, 2019
• Deep learning and its applications to machine health monitoring
  Mechanical Systems and Signal Processing, 2019
• Machine and deep learning for sport-specific movement recognition: a systematic review of model development and performance
  Journal of Sports Sciences, 2018
• Time Series FeatuRe Extraction on basis of Scalable Hypothesis tests (tsfresh – A Python package)
  Neurocomputing, 2018
• Feature selection in machine learning prediction systems for renewable energy applications
  Renewable and Sustainable Energy Reviews, 2018
• Trends Supporting the In-Field Use of Wearable Inertial Sensors for Sport Performance Evaluation: A Systematic Review
  Sensors, 2018
• Multi-sensor fusion in body sensor networks: State-of-the-art and research challenges
  Information Fusion, 2017
• A Deep Learning Approach to on-Node Sensor Data Analytics for Mobile or Wearable Devices
  IEEE Journal of Biomedical and Health Informatics, 2016
• Activity recognition using cell phone accelerometers
  ACM SIGKDD Explorations Newsletter, 2011