Dr. Terence Sim

Shakespeer: Bone Biometrics

ICPR, August 2022

Gucheng Wang, Jay Prakash, Terence Sim, Jun Han

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Securely and unobtrusively authenticating a user is an important problem given the pervasiveness of smartphones. Existing approaches, such as password, fingerprints, or facial recognition, are vulnerable to various attacks, and/or degrade usability. To overcome this problem, we propose Shakespeer, which differentiates users based on uniqueness in the propagation of haptic vibrations through hand, forearm muscles and bones. These vibrations are generated by the user’s smartphone and sensed by their smartphone and smartwatch. The unobtrusive haptic vibrational response makes this biometric feature hard to be replicated. Meanwhile, it provides the co-presence detection function, which allows the devices to confirm the co-presence on the user’s body.

EarWalk: Foot Posture from your earbuds

Hotmobile, March 2022

Nan Jiang, Terence Sim, Jun Han

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Stress on the knee --- caused by various factors including injuries, aging, and overweight --- is a major contributor to orthopedic disorders such as knee osteoarthritis (OA), a severe illness that can even lead to decreased ability to walk. One possible treatment for this problem is to have patients conduct gait modification, getting them to intentionally walk toe-in or toe-out, thereby reducing the stress on their knees. In this paper, we propose EarWalk, a novel solution that utilizes commodity wireless earables to provide constant and real-time feedback on the patients' gait modification. EarWalk leverages the built-in accelerometer in earables to sense and ultimately differentiate normal, toe-in, and toe-out gait postures due to the minute differences in their vibrations.

Action-Invariant Gait

IJCB, October 2022

Sanka Rasnayaka, Terence Sim

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Continuous Authentication (CA) is proposed as an alter-native authentication scheme for modern personal devices. Gait is a suitable biometric for CA due to its availability and low resource requirements. However, the drastic change in the gait pattern with changes in actions (such as walking, running or climbing stairs) and changes in terrain (such as walking on a flat surface or down an incline) makes it chal-lenging to deploy in a real-world CA system. We show that standard gait features are influenced by different actions. The gait pattern of an action is also in-fluenced by the actions performed before and immediately after. Therefore, gait features are usually not robust to these action variations. We propose action invariant gait features to address this robustness issue.