RF-IDraw

Abstract

Prior work in RF-based positioning has mainly focused on discovering the absolute location of an RF source, where state-of-theart systems can achieve an accuracy on the order of tens of centimeters using a large number of antennas. However, many applications in gaming and gesture based interface see more benefits in knowing the detailed shape of a motion. Such trajectory tracing requires a resolution several fold higher than what existing RF-based positioning systems can offer.This paper shows that one can provide a dramatic increase in trajectory tracing accuracy, even with a small number of antennas. The key enabler for our design is a multi-resolution positioning technique that exploits an intrinsic tradeoff between improving the resolution and resolving ambiguity in the location of the RF source. The unique property of this design is its ability to precisely reconstruct the minute details in the trajectory shape, even when the absolute position might have an offset. We built a prototype of our design with commercial off-the-shelf RFID readers and tags and used it to enable a virtual touch screen, which allows a user to interact with a desired computing device by gesturing or writing her commands in the air, where each letter is only a few centimeters wide.

Human Activity Sensing with Wireless Signals: A Survey

“…Label as the frequency sweep bandwidth and as the half cycle of the wave generated. According to the triangle similarity of Equation (10), is calculated as Equation (11). Figure 9 shows the principle of FMCW chirp, where the transceiver sends out signals repeatedly swept across a specific bandwidth.…”

“…KL divergence is defined in Equation 37, which represents the loss of information when fitting the real probability distribution P using the theoretical distribution Q. KL divergence threshold can cut out fitness activities and rest intervals. These fitness exercises contain concentration standing bicep curl, seated triceps press, and flat bench bicep curl, which includes a unique arm pattern in each action [10].…”

“…These above features are usually used by machine learning classifiers (SVM, HMM, random forest, etc.). Besides, many studies [10,19,20,39,55,74,81,89,91] with template matching classification often use the entire time-domain waveform (amplitude-waveform, phase-waveform, etc.) as a feature.…”

“…The long-range and high-efficiency of active RFID makes it essential in some applications that require high-performance and large-scale properties. Since the human body reflects the RFID signals, many studies apply RFID in human activity sensing [10][11][12][13]. For example, FEMO [14] calculates phase difference by measuring the phase of the reflected signals of the tags mounted on the dumbbells and establishes the relationship between the fitness actions and the phase differences.…”

Holography of Wi-fi Radiation

“…Beyond its fundamental interest, indoor imaging by arbitrary wireless signals appears attractive for a variety of applications. These range from localization of radio-frequency tags in internet-of-things settings 7,8,[12][13][14] over 3D motion capture for gaming [15][16][17] to through-wallimaging of moving targets for security enforcement 11,[18][19][20][21] . Unfortunately, it is complicated by one major challenge: multipath reflections -radiation scattered from walls and other surrounding objects outside the viewing area -blur radar signals and their echoes in indoor environments 14 .…”