Validation of a Platform for the Electrostatic Characterization of Textile

Floor covering samples of different thickness, pile height, pile design, materials, construction methods, and applied finishes were selected for electrostatic characterization with a standard plotter platform and a newly designed digital platform. There is an existing standard ISO 6356 in which the voltage generated by a human walking on the carpet is measured with human involvement under controlled conditions. A walking person performs the original test procedure to generate the electrostatic charge and manually calculates results. In contrast, the newly designed system does not require a person to calculate peaks and valleys for the generated electrostatic charges, which offers advantages in terms of accuracy, consistency, and reproducibility, and eliminates human error. The electronic platform is extended with an automated foot for a fully automated test, called “automatic mode”, that has a fixed capacitive and resistive circuit, in replace of human body resistance, and capacitance that varies from person to person and over time. The procedure includes both the old and new platforms, where the new platform is placed in a “human walking” mode to compare the two and validate the new device. Next, all the floor coverings are tested in automatic mode with the automated foot to compare and validate results. We conclude that the new testing device can fully characterize the electrostatic behavior of textile without the involvement of a human, which offers advantages in terms of accuracy, consistency, and reproducibility.

Exploring Multiple Antennas for Long-range WiFi Sensing

Despite extensive research effort on contactless WiFi sensing over the past few years, there are still significant barriers hindering its wide application. One key issue is the limited sensing range due to the intrinsic nature of employing the weak target-reflected signal for sensing and therefore the sensing range is much smaller than the communication range. In this work, we address this challenging issue, moving WiFi sensing one step closer to real-world adoption. The key idea is to effectively utilize the multiple antennas widely available on commodity WiFi access points to simultaneously strengthen the target-reflected signal and reduce the noise. Although traditional beamforming schemes can help increase the signal strength, they are designed for communication and can not be directly applied to benefit sensing. To effectively increase the WiFi sensing range using multiple antennas, we first propose a new metric that quantifies the signal sensing capability. We then propose novel signal processing methods, which lay the theoretical foundation to support beamforming-based long-range WiFi sensing. To validate the proposed idea, we develop two sensing applications: fine-grained human respiration monitoring and coarse-grained human walking tracking. Extensive experiments show that: (i) the human respiration sensing range is significantly increased from the state-of-the-art 6-8 m to 11 m;1 and (ii) human walking can be accurately tracked even when the target is 18 m away from the WiFi transceivers, outperforming the sensing range of the state-of-the-art by 50%.

Stack LSTM-Based User Identification Using Smart Shoes with Accelerometer Data

In this study, we propose a long short-term memory (LSTM)-based user identification method using accelerometer data from smart shoes. In general, for the user identification with human walking data, we require a pre-processing stage in order to divide human walking data into individual steps. Next, user identification can be made with divided step data. In these approaches, when there exist partial data that cannot complete a single step, it is difficult to apply those data to the classification. Considering these facts, in this study, we present a stack LSTM-based user identification method for smart-shoes data. Rather than using a complicated analysis method, we designed an LSTM network for user identification with accelerometer data of smart shoes. In order to learn partial data, the LSTM network was trained using walking data with random sizes and random locations. Then, the identification can be made without any additional analysis such as step division. In the experiments, user walking data with 10 m were used. The experimental results show that the average recognition rate was about 93.41%, 97.19%, and 98.26% by using walking data of 2.6, 3.9, and 5.2 s, respectively. With the experimental results, we show that the proposed method can classify users effectively.