U-WeAr: User Recognition on Wearable Devices through Arm Gesture

The use of wearable devices equipped with inertial sensors has become increasingly pervasive. It has been widely demonstrated in the literature that inertial signals acquired by these sensors can be used by machine learning algorithms to predict actions performed and/or to recognize the identities of the person wearing the sensors. In this article, we present a hardware/software system for arm gesture recognition, identity recognition, and verification of a person based on inertial sensors. The hardware part is a custom wristband that consists of a computing unit, a wireless communication unit, and an inertial sensor. The software part is an algorithm based on recurrent neural networks that is able to process the signals coming from the sensor and to return a prediction. To validate the system, a dataset consisting of 25 symbols drawn with the arm is collected. These symbols are performed by 33 subjects. We conduct two evaluations: 1) performance evaluation for arm gesture recognition, user recognition and verification; and 2) usability assessment of the system. The performance of the three recognition tasks indicate that this system can be reliably applied in real environments with an accuracy above 96% for gesture recognition, an accuracy of about 85% for user identification, and an equal error rate of about 13% for user verification. The outcome of the usability test proves a great satisfaction from the users in terms of high simplicity in the use of the wristband and goodness of the machine learning predictions.