skip to main content


Title: MilliPose: Facilitating Full Body Silhouette Imaging from Millimeter-Wave Device
This paper proposes MilliPose, a system that facilitates full human body silhouette imaging and 3D pose estimation from millimeterwave (mmWave) devices. Unlike existing vision-based motion capture systems, MilliPose is not privacy-invasive and is capable of working under obstructions, poor visibility, and low light conditions. MilliPose leverages machine-learning models based on conditional Generative Adversarial Networks and Recurrent Neural Network to solve the challenges of poor resolution, specularity, and variable reflectivity with existing mmWave imaging systems. Our preliminary results show the efficacy of MilliPose in accurately predicting body joint locations under natural human movement.  more » « less
Award ID(s):
1910853 2018966
PAR ID:
10296788
Author(s) / Creator(s):
;
Date Published:
Journal Name:
Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. We propose MiShape, a millimeter-wave (mmWave) wireless signal based imaging system that generates high-resolution human silhouettes and predicts 3D locations of body joints. The system can capture human motions in real-time under low light and low-visibility conditions. Unlike existing vision-based motion capture systems, MiShape is privacy non-invasive and can generalize to a wide range of motion tracking applications at-home. To overcome the challenges with low-resolution, specularity, and aliasing in images from Commercial-Off-The-Shelf (COTS) mmWave systems, MiShape designs deep learning models based on conditional Generative Adversarial Networks and incorporates the rules of human biomechanics. We have customized MiShape for gait monitoring, but the model is well adaptive to any tracking applications with limited fine-tuning samples. We experimentally evaluate MiShape with real data collected from a COTS mmWave system for 10 volunteers, with diverse ages, gender, height, and somatotype, performing different poses. Our experimental results demonstrate that MiShape delivers high-resolution silhouettes and accurate body poses on par with an existing vision-based system, and unlocks the potential of mmWave systems, such as 5G home wireless routers, for privacy-noninvasive healthcare applications. 
    more » « less
  2. In this work, we proposeMiSleep, a deep learning augmented millimeter-wave (mmWave) wireless system to monitor human sleep posture by predicting the 3D location of the body joints of a person during sleep. Unlike existing vision- or wearable-based sleep monitoring systems,MiSleepis not privacy-invasive and does not require users to wear anything on their body.MiSleepleverages knowledge of human anatomical features and deep learning models to solve challenges in existing mmWave devices with low-resolution and aliased imaging, and specularity in signals.MiSleepbuilds the model by learning the relationship between mmWave reflected signals and body postures from thousands of existing samples. Since a practical sleep also involves sudden toss-turns, which could introduce errors in posture prediction,MiSleepdesigns a state machine based on the reflected signals to classify the sleeping states into rest or toss-turn, and predict the posture only during the rest states. We evaluateMiSleepwith real data collected from Commercial-Off-The-Shelf mmWave devices for 8 volunteers of diverse ages, genders, and heights performing different sleep postures. We observe thatMiSleepidentifies the toss-turn events start time and duration within 1.25 s and 1.7 s of the ground truth, respectively, and predicts the 3D location of body joints with a median error of 1.3 cm only and can perform even under the blankets, with accuracy on par with the existing vision-based system, unlocking the potential of mmWave systems for privacy-noninvasive at-home healthcare applications.

     
    more » « less
  3. This paper proposes SquiggleMilli, a system that approximates traditional Synthetic Aperture Radar (SAR) imaging on mobile millimeter-wave (mmWave) devices. The system is capable of imaging through obstructions, such as clothing, and under low visibility conditions. Unlike traditional SAR that relies on mechanical controllers or rigid bodies, SquiggleMilli is based on the hand-held, fluidic motion of the mmWave device. It enables mmWave imaging in hand-held settings by re-thinking existing motion compensation, compressed sensing, and voxel segmentation. Since mmWave imaging suffers from poor resolution due to specularity and weak reflectivity, the reconstructed shapes could be imperceptible by machines and humans. To this end, SquiggleMilli designs a machine learning model to recover the high spatial frequencies in the object to reconstruct an accurate 2D shape and predict its 3D features and category. We have customized SquiggleMilli for security applications, but the model is adaptable to other applications with limited training samples. We implement SquiggleMilli on off-the-shelf components and demonstrate its performance improvement over the traditional SAR qualitatively and quantitatively. 
    more » « less
  4. Rehabilitation is a crucial process for patients suffering from motor disorders. The current practice is performing rehabilitation exercises under clinical expert supervision. New approaches are needed to allow patients to perform prescribed exercises at their homes and alleviate commuting requirements, expert shortages, and healthcare costs. Human joint estimation is a substantial component of these programs since it offers valuable visualization and feedback based on body movements. Camera-based systems have been popular for capturing joint motion. However, they have high-cost, raise serious privacy concerns, and require strict lighting and placement settings. We propose a millimeter-wave (mmWave)-based assistive rehabilitation system (MARS) for motor disorders to address these challenges. MARS provides a low-cost solution with a competitive object localization and detection accuracy. It first maps the 5D time-series point cloud from mmWave to a lower dimension. Then, it uses a convolution neural network (CNN) to estimate the accurate location of human joints. MARS can reconstruct 19 human joints and their skeleton from the point cloud generated by mmWave radar. We evaluate MARS using ten specific rehabilitation movements performed by four human subjects involving all body parts and obtain an average mean absolute error of 5.87 cm for all joint positions. To the best of our knowledge, this is the first rehabilitation movements dataset using mmWave point cloud. MARS is evaluated on the Nvidia Jetson Xavier-NX board. Model inference takes only 64 s and consumes 442 J energy. These results demonstrate the practicality of MARS on low-power edge devices. 
    more » « less
  5. Using millimeter wave (mmWave) signals for imaging has an important advantage in that they can penetrate through poor environmental conditions such as fog, dust, and smoke that severely degrade optical-based imaging systems. However, mmWave radars, contrary to cameras and LiDARs, suffer from low angular resolution because of small physical apertures and conventional signal processing techniques. Sparse radar imaging, on the other hand, can increase the aperture size while minimizing the power consumption and read out bandwidth. This paper presents CoIR, an analysis by synthesis method that leverages the implicit neural network bias in convolutional decoders and compressed sensing to perform high accuracy sparse radar imaging. The proposed system is data set-agnostic and does not require any auxiliary sensors for training or testing. We introduce a sparse array design that allows for a 5.5× reduction in the number of antenna elements needed compared to conventional MIMO array designs. We demonstrate our system's improved imaging performance over standard mmWave radars and other competitive untrained methods on both simulated and experimental mmWave radar data. 
    more » « less