With the flourish of the smart devices and their applications, controlling devices using gestures has attracted increasing attention for ubiquitous sensing and interaction. Recent works use acoustic signals to track hand movement and recognize gestures. However, they suffer from low robustness due to frequency selective fading, interference and insufficient training data. In this work, we propose RobuCIR, a robust contact-free gesture recognition system that can work under different usage scenarios with high accuracy and robustness. RobuCIR adopts frequency-hopping mechanism to mitigate frequency selective fading and avoid signal interference. To further increase system robustness, we investigate a series of data augmentation techniques based on a small volume of collected data to emulate different usage scenarios. The augmented data is used to effectively train neural network models and cope with various influential factors (e.g., gesture speed, distance to transceiver, etc.). Our experiment results show that RobuCIR can recognize 15 gestures and outperform state-of-the-art works in terms of accuracy and robustness.

Wireless sensing techniques for tracking human activities have been vigorously developed in recent years. Yet current RFID based human activity recognition techniques need either direct contact to human body (e.g., attaching RFIDs to users) or specialized hardware (e.g., software defined radios, antenna array). How to wirelessly track human activities using commodity RFID systems without attaching tags to users (i.e., a contact-free scenario) still faces lots of technical challenges. In this paper, we quantify the correlation between RF phase values and human activities by modeling intrinsic characteristics of signal reflection in contact-free scenarios. Based on the signal reflection model, we introduce TACT that can recognize human activities using commodity RFIDs without attaching any RFID tags to users. TACT first reliably detects the presence of human activities and segments phase values. Then, candidate phase segments are classified according to their coarse-grained features (e.g., moving speed, moving distance, activity duration) as well as their fine-grained feature of phase waveform. We deploy and leverage multiple tags to increase the coverage and enhance the robustness of the system. We implement TACT with commodity RFID systems. We invite 12 participants to evaluate our system in various scenarios. The experiment results show that TACT can recognize eight types of human activities with 93.5% precision under different and challenging experiment settings.

Breath monitoring helps assess the general personal health and gives clues to chronic diseases. Yet current breath monitoring technologies are inconvenient and intrusive. For instance, typical breath monitoring devices need to attach nasal probes or chest bands to users. Wireless sensing technologies have been applied to monitor breathing using radio waves without physical contact. Those wireless sensing technologies however require customized radios which are not readily available. More importantly, due to interference, such technologies do not work well with multiple users. With multiple users in presence, the detection accuracy of existing systems decreases dramatically. In this paper, we propose to monitor users? breathing using commercial-off-the-shelf (COTS) RFID systems. In our system, passive lightweight RFID tags are attached to users? clothes and backscatter radio waves, and commodity RFID readers report low level data (e.g., phase values). We detect the effective human respiration by analyzing the low level data reported by commodity readers. To enhance the measurement robustness, we synthesize data streams from an array of multiple tags to improve the monitoring accuracy. We implement a prototype the breath monitoring system with commodity RFID systems. The experiment results show that our system can simultaneously monitor breathing with high accuracy even with the presence of multiple users.

Vibration measurement is vital for fault diagnosis of structures (e.g., machines and civil structures). Different structure components undergo distinct vibration patterns, which jointly determine the structure's health condition, thus demanding simultaneous multi-point vibration monitoring. Existing solutions deploy multiple accelerometers along with their power supplies or laser vibrometers on the monitored object to measure multi-point vibration, which is inconvenient and costly. Cameras provide a less expensive solution while heavily relying on good lighting conditions. To overcome these limitations, we propose a cost-effective and passive system, called Multi-Vib, for precise multi-point vibration monitoring. Multi-Vib is implemented using a single mmWave radar to remotely and separately sense the vibration displacement of multiple points via signal reflection. However, simultaneously detecting and monitoring multiple points on a single object is a daunting task. This is because most radar signals are scattered away from vibration points due to their tilted locations and shapes by nature, causing an extremely weak reflected signal to the radar. To solve this issue, we dedicatedly design a physical marker placed on the target point, which can force the direction of the reflected signal towards the radar and significantly increase the reflected signal strength. Another practical issue is that the reflected signal from each point endures interferences and noises from the surroundings. Thus, we develop a series of effective signal processing methods to denoise the signal for accurate vibration frequency and displacement estimation. Extensive experimental results show that the average errors in multi-point vibration frequency and displacement estimation are around 0.16Hz and 14μm, respectively.

Liquid fraud has plagued people with huge health risks. Liquid fraud detection can help to reduce the risk of liquid hazards. However, existing systems that use biochemical tools or radio frequency signals for liquid sensing are either expensive, intrusive, or inconvenient for public use. In this article, we propose HearLiquid, a low-cost and nonintrusive liquid fraud detection system using commodity acoustic devices. Our insight comes from the fact that acoustic impedance of different liquids results in distinct absorption of the acoustic signal across different frequencies when it travels through the liquid. In specific, we extract the liquid’s acoustic absorption and transmission curve (AATC) over multiple frequencies of the acoustic signal for liquid fraud detection. However, accurately measuring the AATC faces multiple challenges. First, due to the hardware diversity and imperfection, different acoustic devices introduce diverse frequency responses, which brings significant deviations to AATCs of the same liquid. Second, different relative positions between acoustic devices and the liquid container result in variations in the AATC, making the detection result inaccurate. To overcome these challenges, we first calibrate the AATC using a dedicated reference AATC to remove the effect of hardware diversity. To bear the variations in AATCs measured from different relative positions, we apply a well-orchestrated data augmentation technique to automatically generate sufficient AATCs for different positions using a small number of collected data. Finally, AATCs are used to train the liquid detection model. We conduct extensive experiments on many important liquid fraud cases and achieve liquid detection accuracy of 92%–97%.