ABSTRACT

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.

摘要

跌倒是挑战老年人独立生活的最危及生命的事件之一。可穿戴传感器技术已成为跌倒检测的可行解决方案。然而，现有的跌倒检测模型要么专注于手动特征工程，要么缺乏可解释性。为了推进基于可穿戴传感器的健康管理的最新技术，我们遵循计算设计科学范式，并开发了一种深度学习模型，以根据可穿戴传感器数据检测跌倒。我们提出了一个基于分层注意力的卷积神经网络（HACNN）来优化模型的有效性。我们收集了两个大型公开可用的数据集来评估我们的跌倒检测模型。我们对我们提出的 HACNN 进行了广泛的评估，并讨论了一个案例研究来说明其优势和可解释性，这可以指导跌倒检测系统的未来设置。我们通过为慢性病管理启用可解释的跌倒检测，为信息系统 (IS) 知识库做出贡献。我们还通过在模型构建中提出可推广的设计原则，为设计科学理论做出贡献。