RF-Wri: An Efficient Framework for RF-Based Device-Free Air-Writing Recognition

Machines are becoming indispensable in our lives and the requirements of the human-machine interactions are increasing. Conventional devices, such as a keyboard or touch screen, may not be preferred in future's entertainment, home, and industrial applications. Device-free (non-contact) solutions will be even more popular. These solutions often use visual and acoustic technologies which have some disadvantages. The use of radio frequency (RF) waves for human-machine interaction such as air-writing (Wri), is a new and challenging problem. We propose a device-free machine learning-based air-writing recognition framework called RF-Wri which can effectively distinguish 26 capital letters. Two-channel low-cost software-defined radios (SDR) and oppositely polarized antennas are used to provide polarization diversity which makes the accuracy of classification superior. Another critical novelty is the usage of Discrete Cosine Transform (DCT) coefficients as new features to represent RF waveform which provides writing speed and user invariant recognition. Discrete Wavelet Transform (DWT) filters and letter segmentation algorithm are used for signal de-noising and separating the air-writing activities, respectively. It is shown that Support Vector Machine (SVM) can successfully classify the measured RF waves of air-written letters. It is verified with various real measurements that the proposed framework, RF-Wri, achieves 95.15% accuracy in the classification of all 26 air-written letters and outperforms the fairly new WiFi-based air-writing recognition approaches.

Automated summary

In the future, device-free technologies such as RF waves will be more commonly used for human-machine interactions, requiring machine learning and polarization diversity to improve accuracy. New feature extraction methods and signal processing techniques can make device-free air-writing recognition more effective.