Deep-Learning-Enhanced Human Activity Recognition for Internet of Healthcare Things

Xiaokang Zhou, Wei Liang*, Kevin I.Kai Wang, Hao Wang, Laurence T. Yang, Qun Jin

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

197 Citations (Scopus)


Along with the advancement of several emerging computing paradigms and technologies, such as cloud computing, mobile computing, artificial intelligence, and big data, Internet of Things (IoT) technologies have been applied in a variety of fields. In particular, the Internet of Healthcare Things (IoHT) is becoming increasingly important in human activity recognition (HAR) due to the rapid development of wearable and mobile devices. In this article, we focus on the deep-learning-enhanced HAR in IoHT environments. A semisupervised deep learning framework is designed and built for more accurate HAR, which efficiently uses and analyzes the weakly labeled sensor data to train the classifier learning model. To better solve the problem of the inadequately labeled sample, an intelligent autolabeling scheme based on deep Q-network (DQN) is developed with a newly designed distance-based reward rule which can improve the learning efficiency in IoT environments. A multisensor based data fusion mechanism is then developed to seamlessly integrate the on-body sensor data, context sensor data, and personal profile data together, and a long short-term memory (LSTM)-based classification method is proposed to identify fine-grained patterns according to the high-level features contextually extracted from the sequential motion data. Finally, experiments and evaluations are conducted to demonstrate the usefulness and effectiveness of the proposed method using real-world data.

Original languageEnglish
Article number9055403
Pages (from-to)6429-6438
Number of pages10
JournalIEEE Internet of Things Journal
Issue number7
Publication statusPublished - 2020 Jul


  • Deep learning
  • Internet of Things (IoT)
  • human activity recognition (HAR)
  • reinforcement learning
  • smart healthcare
  • weakly labeled data

ASJC Scopus subject areas

  • Signal Processing
  • Information Systems
  • Hardware and Architecture
  • Computer Science Applications
  • Computer Networks and Communications


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