TY - GEN
T1 - Poster
T2 - 24th Annual International Conference on Mobile Computing and Networking, MobiCom 2018
AU - Zhao, Tianming
AU - Wang, Yan
AU - Liu, Jian
AU - Chen, Yingying
PY - 2018/10/15
Y1 - 2018/10/15
N2 - This paper presents a photoplethysmography (PPG)-based continuous user authentication (CA) system, which especially leverages the PPG sensors in wrist-worn wearable devices to identify users. We explore the uniqueness of the human cardiac system captured by the PPG sensing technology. Existing CA systems require either the dedicated sensing hardware or specific gestures, whereas our system does not require any users' interactions but only the wearable device, which has already been pervasively equipped with PPG sensors. Notably, we design a robust motion artifacts (MA) removal method to mitigate the impact of MA from wrist movements. Additionally, we explore the characteristic fiducial features from PPG measurements to efficiently distinguish the human cardiac system. Furthermore, we develop a cardiac-based classifier for user identification using the Gradient Boosting Tree (GBT). Experiments with the prototype of the wrist-worn PPG sensing platform and 10 participants in different scenarios demonstrate that our system can effectively remove MA and achieve a high average authentication success rate over 90%.
AB - This paper presents a photoplethysmography (PPG)-based continuous user authentication (CA) system, which especially leverages the PPG sensors in wrist-worn wearable devices to identify users. We explore the uniqueness of the human cardiac system captured by the PPG sensing technology. Existing CA systems require either the dedicated sensing hardware or specific gestures, whereas our system does not require any users' interactions but only the wearable device, which has already been pervasively equipped with PPG sensors. Notably, we design a robust motion artifacts (MA) removal method to mitigate the impact of MA from wrist movements. Additionally, we explore the characteristic fiducial features from PPG measurements to efficiently distinguish the human cardiac system. Furthermore, we develop a cardiac-based classifier for user identification using the Gradient Boosting Tree (GBT). Experiments with the prototype of the wrist-worn PPG sensing platform and 10 participants in different scenarios demonstrate that our system can effectively remove MA and achieve a high average authentication success rate over 90%.
KW - Continuous Authentication
KW - Photoplethysmography (PPG)
KW - Wearable Devices
UR - http://www.scopus.com/inward/record.url?scp=85056904925&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85056904925&partnerID=8YFLogxK
U2 - 10.1145/3241539.3267748
DO - 10.1145/3241539.3267748
M3 - Conference contribution
AN - SCOPUS:85056904925
T3 - Proceedings of the Annual International Conference on Mobile Computing and Networking, MOBICOM
SP - 783
EP - 785
BT - MobiCom 2018 - Proceedings of the 24th Annual International Conference on Mobile Computing and Networking
PB - Association for Computing Machinery
Y2 - 29 October 2018 through 2 November 2018
ER -