Fall Detection with Wearable Sensors: A Hierarchical Attention-based Convolutional Neural Network Approach

Shuo Yu, Yidong Chai, Hsinchun Chen, Randall A. Brown, Scott J. Sherman, Jay F. Nunamaker

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


Falls are among the most life-threatening events that challenge senior citizens’ independent living. Wearable sensor technologies have emerged as a viable solution for fall detection. However, existing fall detection models either focus on manual feature engineering or lack explainability. To advance the state-of-the-art of wearable sensor-based health management, we follow the computational design science paradigm and develop a deep learning model to detect falls based on wearable sensor data. We propose a Hierarchical Attention-based Convolutional Neural Network (HACNN) to optimize the model effectiveness. We collected two large publicly available datasets to evaluate our fall detection model. We conduct extensive evaluations on our proposed HACNN and discuss a case study to illustrate its advantage and explainability, that could guide future set-ups for fall detection systems. We contribute to the information systems (IS) knowledge base by enabling explainable fall detection for chronic disease management. We also contribute to the design science theory by proposing generalizable design principles in model building.

Original languageEnglish (US)
Pages (from-to)1095-1121
Number of pages27
JournalJournal of Management Information Systems
Issue number4
StatePublished - 2021


  • chronic disease management
  • convolutional neural networks
  • design science
  • fall detection
  • hierarchical attention mechanism
  • learning systems explainability
  • wearable sensors

ASJC Scopus subject areas

  • Management Information Systems
  • Computer Science Applications
  • Management Science and Operations Research
  • Information Systems and Management


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