Wireless Sensing and Analytics
7.5 Discussions
7.5.3 Detecting Dynamic Event
In the current event detector of TRIMS, the training database is built upon static CSI measurements collected for each events. Each dynamic event can be decomposed into several intermediate states sampled during its occurrence. Because the intermediate state can be viewed as static, the presented algorithm can be applied to detect the occurrence of its intermediate states. Consequently, the state transition that depicts the occurrence of dynamic events can be captured, and thus dynamic events can also be monitored by the presented system.
7.5.4 Identifying Motion
In this chapter, the presented motion detector in TRIMS manages to detect the incidents of motion. Nevertheless, it is worthwhile to study how to utilize the TR technique to extract the characteristics of a motion with Wi-Fi signals, e.g., the direction and the velocity. The potentiality of extracting motion information and even identifying motion with commercial Wi-Fi devices is beneficial to various applications like elderly assistance and life monitoring.
7.5.5 Potential of TRIMS
The presented TRIMS is not confined by Wi-Fi and can be applied to other wireless tech- nologies as long as CSI with enough resolution can be obtained. The spatial resolution of CSI is determined by the transmission bandwidth of the radio frequency (RF) device.
Ultra-wideband (UWB) communication whose bandwidth exceeds 500 MHz can pro- vide CSI of a finer spatial resolution and enable a better discrimination than Wi-Fi does. However, UWB-based indoor monitoring systems require to deploy specially designed RF devices, and the coverage is small. On the other hand, as demonstrated by experiments in this chapter, with the help of TRIMS, commercial Wi-Fi devices with only a 40 MHz bandwidth can support high-accuracy indoor monitoring. Due to the explosive popularity of wireless devices, increasing wireless traffic clogs Wi-Fi and collisions delay the CSI probing with an unknown offset, which introduces difficulty to real-time wireless sensing systems. Taking advantage of the presented smoothing algorithm, TRIMS is robust to nonuniform CSI probing and packages loss. Moreover, thanks to the ubiquitous deployment of Wi-Fi, the presented system is ready and can be easily put into practice for smart home indoor monitoring. In general, the presented system can be integrated with all kinds of wireless technologies where CSI with enough resolution is accessible.
7.6 Summary
In this chapter, we presented a smart radio system, TRIMS, for real-time indoor monitor- ing, which utilizes TR technique to exploit the information in multipath propagations.
Moreover, the statistical behavior of intraclass TRRS is analyzed theoretically. An
7.6 Summary 167
event detector is built, where different indoor events are differentiated and quan- titatively evaluated through TRRS statistics of the associated CSI. Furthermore, a motion detector is designed in TRIMS to detect the existence of dynamics in the environment. The performance of TRIMS is studied through extensive experiments, which are conducted with TRIMS’s prototype implemented on a single pair of commodity Wi-Fi devices. Experimental results demonstrate that TRIMS addresses the problem of recognizing different indoor events in real-time. We also evaluate the performance through a 2-week monitoring test in a single-family house with normal resident activities. TRIMS succeeds in achieving high accuracy in long-term indoor monitoring experiments, demonstrating its prominent and promising role in future intelligent Wi-Fi-based low-complexity smart radios. For related references, readers can refer to [41].
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Recognition
In this chapter, we show the existence of human radio biometrics and present a human identification system that can discriminate individuals even through the walls in a non- line-of-sight (NLOS) condition. Using commodity Wi-Fi devices, the presented sys- tem captures the channel state information (CSI) and extracts human radio biometric information from Wi-Fi signals using time-reversal (TR) technique. By leveraging the fact that broadband wireless CSI has a significant number of multipaths, which can be altered by human body interferences, the presented system can recognize individ- uals in the TR domain without line-of-sight radio. We built a prototype of the TR human identification system using standard Wi-Fi chipsets with 3×3 multiple-input and multiple-output (MIMO) transmission. The performance of the presented system is evaluated and validated through multiple experiments. In general, the TR human identification system achieves an accuracy of 98.78% for identifying about a dozen individuals using a single transmitter and receiver pair. Thanks to the ubiquitousness of Wi-Fi, the presented system shows the promise for future low-cost, low-complexity reliable human identification applications based on radio biometrics.