NON CONTACT MEASUREMENT & INSPECTION FEATURE
two scenarios: activity recognition and through-wall respiration sensing. Activity recognition: A group of gestures common in residential healthcare have been studied. Figure 3 shows the extracted Doppler-time spectral map from the two
IQ samples and delivers them to the LabVIEW application for fast signal processing. LabVIEW delivers flexibility for rapid prototyping, so the team can adjust signal processing parameters to meet their exact requirements, whilst making the best use of multicore processing technologies. The researchers could dynamically change the data arrays they worked with, or alter the integration time and batch size of their analysis routines to adapt the system to slow and fast movements. Also, LabVIEW delivers an intuitive software environment, which empowered the team to quickly integrate signal processing code, presented as subVIs in LabVIEW, for experimenting with new algorithms. In summary, NI offers an ideal platform to develop, test, and verify cutting-edge pre-commercial concepts.
EXPERIMENTAL RESULTS Based on the prototype system, the team verified conceptual passive WiFi sensing in
Figure 3: Recognition of six daily activities based on passive WiFi sensing
sensors during each gesture cycle. The researchers then applied SRC classification based on the PCA features of each Doppler signature. Respiration sensing: The team
Figure 4: Through-wall respiration detection with USRP-based passive WiFi sensing
demonstrated a through-wall breathing detection. To observe the clear periodical signal varying caused by breath, the researchers used a Hampel filter to remove superfluous information and outliers.
CONCLUSION AND IMPACT Compared to the established monitoring techniques, passive WiFi sensing displays obvious advantages: • Contactless and pervasive: The ability to identify activities anywhere WiFi connectivity is available, without the need for any subjects to carry devices. • Diverse and accurate information: The detection of many activities from respiration to body gestures, from casual day-to-day operations to severe events. • Unobtrusive: Because activity
information is obtained from reflected RF signals and not images or video streams, the team significantly reduced concerns over subject privacy. USRP and LabVIEW has accelerated the team’s groundbreaking research into passive WiFi sensing, leading to innovative advancements, collaborations, more than 15 research publications, and patents. More importantly, the NI platform helped the researchers realise the practical, commercial application of passive WiFi sensing, and how this technology can positively impact on people’s lives.
NI
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INSTRUMENTATION | MARCH 2018 15
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