M
Muhammad Shahzad
Researcher at University of the Sciences
Publications - 363
Citations - 6586
Muhammad Shahzad is an academic researcher from University of the Sciences. The author has contributed to research in topics: Medicine & Biology. The author has an hindex of 31, co-authored 228 publications receiving 4323 citations. Previous affiliations of Muhammad Shahzad include University of Engineering and Technology, Lahore & University of Health Sciences Lahore.
Papers
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Proceedings ArticleDOI
Understanding and Modeling of WiFi Signal Based Human Activity Recognition
TL;DR: CARM is a CSI based human Activity Recognition and Monitoring system that quantitatively builds the correlation between CSI value dynamics and a specific human activity and recognizes a given activity by matching it to the best-fit profile.
Proceedings ArticleDOI
Keystroke Recognition Using WiFi Signals
TL;DR: It is shown for the first time that WiFi signals can also be exploited to recognize keystrokes, which is critical for ensuring the security of computer systems and the privacy of human users as what being typed could be passwords or privacy sensitive information.
Proceedings ArticleDOI
Gait recognition using wifi signals
TL;DR: The intuition is that due to the differences in gaits of different people, the WiFi signal reflected by a walking human generates unique variations in the Channel State Information on the WiFi receiver, so WifiU is proposed, which uses commercial WiFi devices to capture fine-grained gait patterns to recognize humans.
Journal ArticleDOI
Device-Free Human Activity Recognition Using Commercial WiFi Devices
TL;DR: A Channel State Information (CSI)-based human Activity Recognition and Monitoring system (CARM) based on a CSI-speed model that quantifies the relation between CSI dynamics and human movement speeds and human activities.
Proceedings ArticleDOI
Secure unlocking of mobile touch screen devices by simple gestures: you can see it but you can not do it
TL;DR: Unlike existing authentication schemes for touch screen devices, which use what user inputs as the authentication secret, GEAT authenticates users mainly based on how they input, using distinguishing features such as finger velocity, device acceleration, and stroke time.