In Proceedings of the ACM Conference on Embedded Networked Sensor Systems - SenSys, pages 367-368, 2018. ACM Press. Paper Website abstract bibtex
Wearable systems are commonly used for fitness purpose as these devices provide activity measurements to motivate daily exercise. With aims to promote improved health, healthcare companies are incentivizing their customers with the amount of exercise that is performed and using readings from wearable devices as a way of proving that the individual met the requirements. However, these devices have a risk of user spoofing attacks as an unauthorized individual can utilize the system. To prevent misuse of the product to gain reward and ultimately promote daily exercise for various types of exercise reward systems, we propose a biometric gait identification approach using a smart earring that we design and develop. In this paper, we preliminary train and test the gait identification system by utilizing a transfer learning, which shows a 100% classification performance for eight participants. We expect the proposed gait identification technique will serve as essential building blocks for reliable exercise reward systems.