Person Identification and Discovery With Wrist Worn Accelerometer Data

Internet of Things (IoT) devices with embedded accelerometers continue to grow in popularity. These are often attached to individuals, whether they are a mobile phone in a pocket or a smartwatch on a wrist, and are constantly capturing data of a personal nature. In this work we propose a method for person identification using accelerometer data via supervised machine learning techniques. Further, we introduce the first unsupervised method for discovering individuals using the same accelerometer. We report the performance both in terms of classification
and clustering using a publicly available dataset covering a large number of activities of daily living. While this has numerous benefits in tasks such as activity recognition and biometrics, this work also motivates the debate and discussion around privacy concerns of the analysis of accelerometer data.