Traditional one-time user authentication processes might cause friction and unfavorable user experience in many widely-used applications. This is a severe problem in particular for security-sensitive facilities if an adversary could obtain unauthorized privileges after a user’s initial login. Recently, continuous user authentication (CA) has shown its great potential by enabling seamless user authentication with few active participation. We devise a low-cost system exploiting a user’s pulsatile signals from the photoplethysmography (PPG) sensor in commercial wrist-worn wearables for CA. Compared to existing approaches, our system requires zero user effort and is applicable to practical scenarios with non-clinical PPG measurements having motion artifacts (MA). We explore the uniqueness of the human cardiac system and design an MA filtering method to mitigate the impacts of daily activities. Furthermore, we identify general fiducial features and develop an adaptive classifier using the gradient boosting tree (GBT) method. As a result, our system can authenticate users continuously based on their cardiac characteristics so little training effort is required. Experiments with our wrist-worn PPG sensing platform on 20 participants under practical scenarios demonstrate that our system can achieve a high CA accuracy of over 90% and a low false detection rate of 4% in detecting random attacks.
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Robust planar dynamic pivoting by regulating inertial and grip forces
In this paper, we investigate the planar dynamic pivoting problem, in which a pinched object is reoriented to a desired pose through wrist swing motion and grip force regulation. Traditional approaches based on friction compensation do not work well for this problem, as we observe the torsional friction at the contact has large uncertainties during pivoting. In addition, the discontinuities of friction and the lower bound constraint on the grip force all make dynamic pivoting a challenging task for robots. To address these problems, we propose a robust control strategy that directly uses friction as a key input for dynamic pivoting, and show that active friction control by regulating the grip force significantly improves system stability. In particular, we embed a Lyapunov-based control law into a quadratic programming framework, which also ensures real-time computational speed and the existence of a solution. The proposed algorithm has been validated on our dynamic pivoting robot that emulates human wrist-finger configuration and motion. The object orientation can quickly converge to the target even under considerable uncertainties from friction and object grasping position, where traditional methods fail.
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- NSF-PAR ID:
- 10167959
- Date Published:
- Journal Name:
- Algorithmic Foundations of Robotics XII
- Volume:
- 13
- Page Range / eLocation ID:
- 464 - 479
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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