Using RF transmissions from IoT devices for occupancy detection and activity recognition

TY - JOUR

T1 - Using RF transmissions from IoT devices for occupancy detection and activity recognition

AU - Li, Wenda

AU - Vishwakarma, Shelly

AU - Tang, Chong

AU - Woodbridge, Karl

AU - Piechocki, Robert J.

AU - Chetty, Kevin

PY - 2022/2/1

Y1 - 2022/2/1

N2 - IoT ecosystems consist of a range of smart devices that generated a plethora of Radio Frequency (RF) transmissions. This provides an attractive opportunity to exploit already-existing signals for various sensing applications such as e-Healthcare, security and smart home. In this paper, we present Passive IoT Radar (PIoTR), a system that passively uses RF transmissions from IoT devices for human monitoring. PIoTR is designed based on passive radar technology, with a generic architecture to utilize various signal sources including the WiFi signal and wireless energy at the Industrial, Scientific and Medical (ISM) band. PIoTR calculates the phase shifts caused by human motions and generates Doppler spectrogram as the representative. To verify the proposed concepts and test in a more realistic environment, we evaluate PIoTR with four commercial IoT devices for home use. Depending on the effective signal and power strength, PIoTR performs two modes: coarse sensing and fine-grained sensing. Experimental results show that PIoTR can achieve an average of 91% in occupancy detection (coarse sensing) and 91.3% in activity recognition (fine-grained sensing).

AB - IoT ecosystems consist of a range of smart devices that generated a plethora of Radio Frequency (RF) transmissions. This provides an attractive opportunity to exploit already-existing signals for various sensing applications such as e-Healthcare, security and smart home. In this paper, we present Passive IoT Radar (PIoTR), a system that passively uses RF transmissions from IoT devices for human monitoring. PIoTR is designed based on passive radar technology, with a generic architecture to utilize various signal sources including the WiFi signal and wireless energy at the Industrial, Scientific and Medical (ISM) band. PIoTR calculates the phase shifts caused by human motions and generates Doppler spectrogram as the representative. To verify the proposed concepts and test in a more realistic environment, we evaluate PIoTR with four commercial IoT devices for home use. Depending on the effective signal and power strength, PIoTR performs two modes: coarse sensing and fine-grained sensing. Experimental results show that PIoTR can achieve an average of 91% in occupancy detection (coarse sensing) and 91.3% in activity recognition (fine-grained sensing).

U2 - 10.1109/jsen.2021.3134895

DO - 10.1109/jsen.2021.3134895

M3 - Article

SN - 1530-437X

VL - 22

SP - 2484

EP - 2495

JO - IEEE Sensors Journal

JF - IEEE Sensors Journal

IS - 3

ER -