19 research outputs found
Velocity-Based Channel Charting with Spatial Distribution Map Matching
Fingerprint-based localization improves the positioning performance in
challenging, non-line-of-sight (NLoS) dominated indoor environments. However,
fingerprinting models require an expensive life-cycle management including
recording and labeling of radio signals for the initial training and regularly
at environmental changes. Alternatively, channel-charting avoids this labeling
effort as it implicitly associates relative coordinates to the recorded radio
signals. Then, with reference real-world coordinates (positions) we can use
such charts for positioning tasks. However, current channel-charting approaches
lag behind fingerprinting in their positioning accuracy and still require
reference samples for localization, regular data recording and labeling to keep
the models up to date. Hence, we propose a novel framework that does not
require reference positions. We only require information from velocity
information, e.g., from pedestrian dead reckoning or odometry to model the
channel charts, and topological map information, e.g., a building floor plan,
to transform the channel charts into real coordinates. We evaluate our approach
on two different real-world datasets using 5G and distributed
single-input/multiple-output system (SIMO) radio systems. Our experiments show
that even with noisy velocity estimates and coarse map information, we achieve
similar position accuraciesComment: This work has been submitted to the IEEE for possible publication.
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Optimizing Multicarrier Multiantenna Systems for LoS Channel Charting
Channel charting (CC) consists in learning a mapping between the space of raw
channel observations, made available from pilot-based channel estimation in
multicarrier multiantenna system, and a low-dimensional space where close
points correspond to channels of user equipments (UEs) close spatially. Among
the different methods of learning this mapping, some rely on a distance measure
between channel vectors. Such a distance should reliably reflect the local
spatial neighborhoods of the UEs. The recently proposed phase-insensitive (PI)
distance exhibits good properties in this regards, but suffers from ambiguities
due to both its periodic and oscillatory aspects, making users far away from
each other appear closer in some cases. In this paper, a thorough theoretical
analysis of the said distance and its limitations is provided, giving insights
on how they can be mitigated. Guidelines for designing systems capable of
learning quality charts are consequently derived. Experimental validation is
then conducted on synthetic and realistic data in different scenarios
Self-Supervised and Invariant Representations for Wireless Localization
In this work, we present a wireless localization method that operates on
self-supervised and unlabeled channel estimates. Our self-supervising method
learns general-purpose channel features robust to fading and system
impairments. Learned representations are easily transferable to new
environments and ready to use for other wireless downstream tasks. To the best
of our knowledge, the proposed method is the first joint-embedding
self-supervised approach to forsake the dependency on contrastive channel
estimates. Our approach outperforms fully-supervised techniques in small data
regimes under fine-tuning and, in some cases, linear evaluation. We assess the
performance in centralized and distributed massive MIMO systems for multiple
datasets. Moreover, our method works indoors and outdoors without additional
assumptions or design changes
Model-Based Approaches to Channel Charting
We present new ways of producing a channel chart employing model-based
approaches. We estimate the angle of arrival theta and the distance between the
base station and the user equipment rho by employing our algorithms, inverse of
the root sum squares of channel coefficients (ISQ) algorithm, linear regression
(LR) algorithm, and the MUSIC/MUSIC (MM) algorithm. We compare these methods
with the training-based channel charting algorithms principal component
analysis (PCA), Samson's method (SM), and autoencoder (AE). We show that ISQ,
LR, and MM outperform all three in performance. The performance of MM is better
than LR and ISQ but it is more complex. ISQ and LR have similar performance
with ISQ having less complexity than LR. We also compare our algorithm MM with
and algorithm from the literature that uses the MUSIC algorithm jointly on
theta and rho. We call this algorithm the JM algorithm. JM performs very
slightly better than MM but at a substantial increase in complexity. Finally,
we introduce the rotate-and-sum (RS) algorithm which has about the same
performance as the MM and JM algorithms but is less complex due to the
avoidance of the eigenvector and eigenvalue analysis and a potential register
transfer logic (RTL) implementation.Comment: 28 pages, 13 figures, 6 table
Deep learning and wearable sensors for the diagnosis and monitoring of Parkinson’s disease: A systematic review
Parkinson’s disease (PD) is a neurodegenerative disorder that produces both motor and non-motor complications, degrading the quality of life of PD patients. Over the past two decades, the use of wearable devices in combination with machine learning algorithms has provided promising methods for more objective and continuous monitoring of PD. Recent advances in artificial intelligence have provided new methods and algorithms for data analysis, such as deep learning (DL).
The aim of this article is to provide a comprehensive review of current applications where DL algorithms are employed for the assessment of motor and nonmotor manifestations (NMM) using data collected via wearable sensors. This paper provides the reader with a summary of the current applications of DL and wearable devices for the diagnosis, prognosis, and monitoring of PD, in the hope of improving the adoption, applicability, and impact of both technologies as support tools.
Following PRISMA (Systematic Reviews and Meta-Analyses) guidelines, sixty-nine studies were selected and analyzed. For each study, information on sample size, sensor configuration, DL approaches, validation methods, and results according to the specific symptom under study were extracted and summarized. Furthermore, quality assessment was conducted according to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) method.
The majority of studies (74%) were published within the last three years, demonstrating the increasing focus on wearable technology and DL approaches for PD assessment. However, most papers focused on monitoring (59%) and computer-assisted diagnosis (37%), while few papers attempted to predict treatment response. Motor symptoms (86%) were treated much more frequently than NMM (14%). Inertial sensors were the most commonly used technology, followed by force sensors and microphones. Finally, convolutional neural networks (52%) were preferred to other DL approaches, while extracted features (38%) and raw data (37%) were similarly used as input for DL models.
The results of this review highlight several challenges related to the use of wearable technology and DL methods in the assessment of PD, despite the advantages this technology could bring in the development and implementation of automated systems for PD assessment
Intelligent Biosignal Processing in Wearable and Implantable Sensors
This reprint provides a collection of papers illustrating the state-of-the-art of smart processing of data coming from wearable, implantable or portable sensors. Each paper presents the design, databases used, methodological background, obtained results, and their interpretation for biomedical applications. Revealing examples are brain–machine interfaces for medical rehabilitation, the evaluation of sympathetic nerve activity, a novel automated diagnostic tool based on ECG data to diagnose COVID-19, machine learning-based hypertension risk assessment by means of photoplethysmography and electrocardiography signals, Parkinsonian gait assessment using machine learning tools, thorough analysis of compressive sensing of ECG signals, development of a nanotechnology application for decoding vagus-nerve activity, detection of liver dysfunction using a wearable electronic nose system, prosthetic hand control using surface electromyography, epileptic seizure detection using a CNN, and premature ventricular contraction detection using deep metric learning. Thus, this reprint presents significant clinical applications as well as valuable new research issues, providing current illustrations of this new field of research by addressing the promises, challenges, and hurdles associated with the synergy of biosignal processing and AI through 16 different pertinent studies. Covering a wide range of research and application areas, this book is an excellent resource for researchers, physicians, academics, and PhD or master students working on (bio)signal and image processing, AI, biomaterials, biomechanics, and biotechnology with applications in medicine