Passive radio sensing technique is a well established research topic where radio-frequency (RF) devices are used as realtime virtual probes that are able to detect the presence and the movement(s) of one or more (non instrumented) targets. However, radio sensing methods usually employ frequencies in the unlicensed 2.4-5 GHz bands where multipath effects strongly limit their accuracy, thus reducing their wide acceptance. On the contrary, sub-terahertz (sub-THz) radiation, due to its very short wavelength and reduced multipath effects, is well suited for high-resolution body occupancy detection and vision applications. In this paper, for the first time, we adopt radio devices emitting in the 100 GHz band to process an image of the environment for body motion discrimination inside a workspace area. Movement detection is based on the realtime analysis of body-induced signatures that are estimated from sub-THz measurements and then processed by specific neural network-based classifiers. Experimental trials are employed to validate the proposed methods and compare their performances with application to industrial safety monitoring.
PASSIVE DETECTION AND DISCRIMINATION OF BODY MOVEMENTS IN THE SUB-THZ BAND: A CASE STUDY
Sanaz Kianoush;Stefano Savazzi;Vittorio Rampa
2019
Abstract
Passive radio sensing technique is a well established research topic where radio-frequency (RF) devices are used as realtime virtual probes that are able to detect the presence and the movement(s) of one or more (non instrumented) targets. However, radio sensing methods usually employ frequencies in the unlicensed 2.4-5 GHz bands where multipath effects strongly limit their accuracy, thus reducing their wide acceptance. On the contrary, sub-terahertz (sub-THz) radiation, due to its very short wavelength and reduced multipath effects, is well suited for high-resolution body occupancy detection and vision applications. In this paper, for the first time, we adopt radio devices emitting in the 100 GHz band to process an image of the environment for body motion discrimination inside a workspace area. Movement detection is based on the realtime analysis of body-induced signatures that are estimated from sub-THz measurements and then processed by specific neural network-based classifiers. Experimental trials are employed to validate the proposed methods and compare their performances with application to industrial safety monitoring.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.