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 reliably detect the effective human respiration corresponded signal and track periodic body movement due to inhaling and exhaling 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. Our design follows the standard EPC protocol which arbitrates collisions in the presence of multiple tags. We implement a prototype the breath monitoring system with commodity RFID systems. The experiment results show that the prototype system can simultaneously monitor breathing with high accuracy even with the presence of multiple users.
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.
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