Wearable Computing With Distributed Deep Learning Hierarchy: A Study of Fall Detection
Xiaoye Qian, Huan Chen, Haotian Jiang, Justin Green, Haoyou Cheng, Ming-Chun Huang
Abstract
With the development of technologies, an increasing number of wearable devices that are currently at the heart of the development of the Internet of Things are used around the world. The concerns about privacy, in particular healthcare wearable devices, are exacerbated as the requirement for self-health monitoring increases. In addition, many sources of data are geographically separated and might not be allowed to release due to patients or regulatory constraints. Accordingly, in this paper, a distributed hierarchical deep learning system is proposed. The proposed system applying a distributed hierarchical neural network over a cloud server and smartphones. The system enables multiple smartphones to train a shared consensus model collaboratively while keeping the private data locally to protect data privacy, and the system takes advantage of the abundant computational resources on the cloud server to lower the computational overhead on smartphones. The proposed system is demonstrated by a fall detection study which is the common healthcare issue among human beings. The patients’ data are collected from multiple wearable devices including the smartphone, the smartwatch, and the smart insoles. The experimental results show that the distributed hierarchical deep learning system can reproduce the accuracy, specificity, precision, and sensitivity of centralized machine learning while preserving privacy.