GenHPE: Generative Counterfactuals for 3D Human Pose Estimation with Radio Frequency Signals

Human pose estimation (HPE) detects the positions of human body joints for various applications. Compared to using cameras, HPE using radio frequency (RF) signals is non-intrusive and more robust to adverse conditions, exploiting the signal variations caused by human interference. However, existing studies focus on single-domain HPE confined by domain-specific confounders, which cannot generalize to new domains and result in diminished HPE performance. Specifically, the signal variations caused by different human body parts are entangled, containing subject-specific confounders. RF signals are also intertwined with environmental noise, involving environment-specific confounders. In this paper, we propose GenHPE, a 3D HPE approach that generates counterfactual RF signals to eliminate domain-specific confounders. GenHPE trains generative models conditioned on human skeleton labels, learning how human body parts and confounders interfere with RF signals. We manipulate skeleton labels (i.e., removing body parts) as counterfactual conditions for generative models to synthesize counterfactual RF signals. The differences between counterfactual signals approximately eliminate domain-specific confounders and regularize an encoder-decoder model to learn domain-independent representations. Such representations help GenHPE generalize to new subjects/environments for cross-domain 3D HPE. We evaluate GenHPE on three public datasets from WiFi, ultra-wideband, and millimeter wave. Experimental results show that GenHPE outperforms state-of-the-art methods and reduces estimation errors by up to 52.2mm for cross-subject HPE and 10.6mm for cross-environment HPE.

RFTacho: Non-intrusive RF monitoring of rotating machines

Measuring rotation speed is essential to many engineering applications; it elicits faults undetectable by vibration monitoring alone and enhances the vibration signal analysis of rotating machines. Optical, magnetic or mechanical Tachometers are currently state-of-art. Their limitations are they require line-of-sight, direct access to the rotating object. This paper proposes RFTacho, a rotation speed measurement \emph{system} that leverages novel hardware and signal processing algorithms to produce highly accurate readings conveniently. RFTacho uses RF Orbital Angular Momentum (OAM) waves to measure rotation speed of multiple machines simultaneously with no requirements from the machine's properties. OAM antennas allow it to operate in high-scattering environments, commonly found in industries, as they are resilient to de-polarization compared to linearly polarized antennas. RFTacho achieves this by using two novel signal processing algorithms to extract the rotation speed of several rotating objects simultaneously amidst noise arising from high-scattering environments, non-line-of-sight scenarios and dynamic environmental conditions with a resolution of $1 rpm$. We test RFTacho on several real-world machines like fans, motors, air conditioners. Results show that RFTacho has avg. error of $<0.5\%$ compared to ground truth. We demonstrate RFTacho's simultaneous multiple-object measurement capability that other tachometers do not have. Initial experiments show that RFTacho can measure speeds as high as 7000 rpm (theoretically 60000 rpm) with high resiliency at different coverage distances and orientation angles, requiring only 150 mW transmit power while operating in the 5 GHz license-exempt band. RFTacho is the first RF-based sensing system that combines OAM waves and novel processing approaches to measure the rotation speed of multiple machines simultaneously in a non-intrusive way.

Resource Allocation for NOMA-based LPWA Networks Powered by Energy Harvesting

In this paper, we explore perpetual, scalable, Low-powered Wide-area networks (LPWA). Specifically we focus on the uplink transmissions of non-orthogonal multiple access (NOMA)-based LPWA networks consisting of multiple self-powered nodes and a NOMA-based single gateway. The self-powered LPWA nodes use the "harvest-then-transmit" protocol where they harvest energy from ambient sources (solar and radio frequency signals), then transmit their signals. The main features of the studied LPWA network are different transmission times-on-air, multiple uplink transmission attempts, and duty cycle restrictions. The aim of this work is to maximize the time-averaged sum of the uplink transmission rates by optimizing the transmission time-on-air allocation, the energy harvesting time allocation and the power allocation; subject to a maximum transmit power and to the availability of the harvested energy. We propose a low complex solution which decouples the optimization problem into three sub-problems: we assign the LPWA node transmission times (using either the fair or unfair approaches), we optimize the energy harvesting (EH) times using a one-dimensional search method, and optimize the transmit powers using a concave-convex (CCCP) procedure. In the simulation results, we focus on Long Range (LoRa) networks as a practical example LPWA network. We validate our proposed solution and we observe a $15\%$ performance improvement when using NOMA.