Privacy-preserving activity and health monitoring on databox

Abstract

Activity recognition using deep learning and sensor data can help monitor activities and health conditions of people who need assistance in their daily lives. Deep Neural Network (DNN) models to infer the activities require data collected by in-home sensory devices. These data are often sent to a centralised cloud to be used for training the model. Centralising the data introduces privacy risks. The collected data contain sensitive information about the subjects. The cloud-based approach increases the risk that the data be stored and reused for other purposes without the owner's control. We propose a system that uses edge devices to implement activity and health monitoring locally and applies federated learning to facilitate the training process. The devices use the Databox platform to manage sensor data collected in people's homes, conduct activity recognition locally, and collaboratively train a DNN model without transferring the collected data into the cloud. We illustrate the applicability of the processing time of activity recognition on edge devices. We use a hierarchical model in which a global model is generated in the cloud, without requiring the raw data, and local models are trained on edge devices. The activity inference accuracy of the global model converges to a sufficient level after a few rounds of communication between edge devices and the cloud.