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

• 成果类型: Article
• 署名作者: Yu, Shuo; Chai, Yidong; Chen, Hsinchun; Brown, Randall A.; Sherman, Scott J.; Nunamaker, Jay F. Jr Jr
• 署名单位: Texas Tech University System; Texas Tech University; Hefei University of Technology; University of Arizona; University of Arizona
• 刊物名称: JOURNAL OF MANAGEMENT INFORMATION SYSTEMS
• ISSN/ISSBN: 0742-1222
• DOI: 10.1080/07421222.2021.1990617
• 发表日期: 2021
• 页码: 1095-1121
• 关键词: social media; risk-factors; HEALTH; care; DESIGN; intelligence; TECHNOLOGY; INNOVATION; addiction; analytics

摘要

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.