UI-GAN: Generative Adversarial Network-Based Anomaly Detection Using User Initial Information for Wearable Devices

Abstract

This article proposes an automatic fall detection method for a wearable device that can promptly alert caregivers when a fall is detected, which could reduce the injuries of elder people. To do this, we propose a novel generative adversarial network (GAN-) based fall detection method using a heart rate sensor and an accelerometer. Acquiring fall data compared with normal behavioral data can be an arduous process. Instead, we introduce a compelling GAN-based anomaly detection partially surrounded with User Initial information features (UI-GAN). Although GAN-based anomaly detection methods have been previously proposed, each model has its adequate suitability for each anomaly detection application. Therefore, this study firstly evaluates suitable GAN-based anomaly detection models for fall detection from among nine recently proposed GAN-based models. From UI-GAN, performance improvements are observed in the fall detection when using the UI information. To objectively demonstrate the competitive performance of UI-GAN, we compare it with eight other recently presented fall detection studies and have observed that it leads to better results. Lastly, since the target application in this study is the use of UI-GAN with a wearable device, the sufficiently satisfied latency of UI-GAN on the smartwatch is estimated. This study is the first attempt to use the initial information of users in GAN, and we hope that the effectiveness of the UI information is expected to be seen in other applications.