Tietoturva-aiheisia poimintoja IEEE Xploresta ja Dark Readingistä. Esim. "Gesture recognition using Wifi" ja "Human Recognition Through a Wall".

On automatizing recognition of multiple human activities using ultrasonic sensor grid (2017)

The proposed method using array of heterogeneous ultrasonic sensors is found capable of detecting standing, sitting and falling of a person, and also the movements in different directions. (id: 7945440)

Human Recognition Through a Wall (2017)

The existence of human radio biometrics and present a human identification system can discriminate individuals even through the walls in a non-line-of-sight condition. Using commodity WiFi devices, the proposed system captures the channel state information (CSI) and extracts human radio biometric information from WiFi signals using time-reversal (TR) technique. It achieves an accuracy of 98.78% for identifying individuals using a single transmitter. (id: 7803604)

An Algorithm for Through-Wall Detection Using Ultra-Wideband Impulse Radar (2017)

The subject is remotely sensed by extracting micro-motion information, such as the respiration and heartbeat frequencies. The challenge is to extract this information due to the low signal to noise and clutter ratio in typical disaster environments. (id: 8063889)

Fine-grained gesture recognition using WiFi (2016)

Low-cost system, WiFinger, takes advantages of the detailed channel state information (CSI) available from commodity WiFi devices and the prevalence of WiFi infrastructure. It senses and identifies subtle movements of finger gestures by examining the unique patterns exhibited in the detailed CSI. Experimental evaluation in a home environment demonstrates that our system can achieve over 93% recognition accuracy. (id: 7562082)

WiGest: A ubiquitous WiFi-based gesture recognition system (2015)

WiGest is unique in using standard WiFi equipment, with no modifications, and no training for gesture recognition. The system identifies different signal change primitives, from which we construct mutually independent gesture families. These families can be mapped to distinguishable application actions. We address various challenges including cleaning the noisy signals, gesture type and attributes detection, reducing false positives due to interfering humans, and adapting to changing signal polarity. Results show that WiGest detects the basic primitives with an accuracy of 87.5% using a single AP only, including through-the-wall non-line-of-sight scenarios. This accuracy increases to 96% using three overheard APs. (id: 7218525)

WiFi-Based Gesture Recognition System (2015)

The vision-based approaches fail to work well in poor light conditions and the sensor-based ones require users to wear devices. Compared with existing Radio Frequency (RF)-based systems, WiG stands out for its systematic simplicity, extremely low cost and high practicability. The results demonstrate that WiG can achieve an average recognition accuracy of 92% in line-of-sight scenario and average accuracy of 88% in the none-line-of sight scenario. (id: 7288485)

Recognizing Keystrokes Using WiFi (2017)

The intuition is that while typing a certain key, the hands and fingers of a user move in a unique formation and direction and thus generate a unique pattern in the time-series of channel state information (CSI) values, which we call CSI-waveform for that key. WiKey consists of two commercial off-the-shelf WiFi devices, a sender (such as TP-Link TL-WR1043ND WiFi router) and a receiver (such as Lenovo X200 laptop). The sender continuously emits signals and the receiver continuously receives signals. (id: 7875144)

Non-Invasive Detection of Moving and Stationary Human With WiFi (2015)

For stationary people, existing approaches often employ a prerequisite scenario-tailored calibration of channel profile in human-free environments. DeMan is capable of simultaneously detecting moving and stationary people with only a small number of prior measurements for model parameter determination, yet without the cumbersome scenario-specific calibration. (id: 7102722)

WiFi-ID: Human Identification Using WiFi (2016)

There is strong evidence that suggests that all humans have a unique gait. An individual’s gait will thus create unique perturbations in the WiFi spectrum. We propose a system called WiFi-ID that analyses the channel state information to extract unique features that are representative of the walking style of that individual and thus allow us to uniquely identify that person. (id: 7536315)