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Special Issue "Intelligent Sensors for Human Motion Analysis"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".

Deadline for manuscript submissions: 15 March 2022.

Special Issue Editors

Dr. Tomasz Krzeszowski
E-Mail Website
Guest Editor
Faculty of Electrical and Computer Engineering, Rzeszow University of Technology, al. Powstańców Warszawy 12, 35-959 Rzeszow, Poland
Interests: human motion tracking; human body pose estimation; particle swarm optimization; parallel and distributed computing; gait recognition
Dr. Adam Świtoński
E-Mail Website
Guest Editor
Department of Graphics, Computer Vision and Digital Systems, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland
Interests: processing and classification of motion capture data; time series analysis; machine learning; computer vision
Dr. Michal Kepski
E-Mail Website
Guest Editor
Institute of Computer Science, University of Rzeszow, 1 Pigonia Str., 35-310 Rzeszow, Poland
Interests: fall detection; human motion tracking; action recognition
Prof. Dr. Carlos Tavares Calafate
E-Mail Website
Guest Editor

Special Issue Information

Dear Colleagues,

Current visual analysis of human motion is one of the most interesting and active research topics in computer vision. This great interest is due to the wide spectrum of promising applications in many areas such as surveillance systems, medicine, athletic performance analysis, human–computer interaction, virtual reality, etc. Human motion analysis concerns the detection, tracking, and recognition of people and their activities based on data recorded by various types of sensors. In these studies, RGB and depth cameras are used. Moreover, studies aimed at developing methods for gait and action recognition often use motion capture systems based on active or passive markers as well as IMU sensors. These systems are very challenging to develop and, at the same time, have great promise for addressing research problems, especially if only visual data are used. Therefore, we welcome the submission of high-quality publications from researchers working on human pose estimation and tracking in addition to related topics such as activity recognition, gait recognition, and human–computer interaction, to name but a few examples. More precisely, the relevant topics for this Special Issue include (but are not limited to):

  • Human pose estimation
  • Articulated pose tracking
  • Multi-person 3D pose estimation
  • Action recognition
  • Gait recognition
  • Gesture recognition
  • Human fall detection
  • Pose/shape modeling and rendering
  • Future 3D pose prediction
  • Human–computer interaction
  • Synthetic data and data annotation for 3D human pose
  • Application of human motion analysis methods (e.g., robotics, surveillance, medicine).

Dr. Tomasz Krzeszowski
Dr. Adam Świtoński
Dr. Michal Kepski
Prof. Dr. Carlos Tavares Calafate
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • human pose estimation
  • articulated pose tracking
  • human motion tracking
  • action recognition
  • gait recognition
  • gesture recognition
  • human fall detection
  • human–computer interaction
  • markerless motion capture
  • marker-based motion capture

Published Papers (8 papers)

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Research

Jump to: Review

Article
Human Action Recognition: A Paradigm of Best Deep Learning Features Selection and Serial Based Extended Fusion
Sensors 2021, 21(23), 7941; https://doi.org/10.3390/s21237941 - 28 Nov 2021
Cited by 2 | Viewed by 473
Abstract
Human action recognition (HAR) has gained significant attention recently as it can be adopted for a smart surveillance system in Multimedia. However, HAR is a challenging task because of the variety of human actions in daily life. Various solutions based on computer vision [...] Read more.
Human action recognition (HAR) has gained significant attention recently as it can be adopted for a smart surveillance system in Multimedia. However, HAR is a challenging task because of the variety of human actions in daily life. Various solutions based on computer vision (CV) have been proposed in the literature which did not prove to be successful due to large video sequences which need to be processed in surveillance systems. The problem exacerbates in the presence of multi-view cameras. Recently, the development of deep learning (DL)-based systems has shown significant success for HAR even for multi-view camera systems. In this research work, a DL-based design is proposed for HAR. The proposed design consists of multiple steps including feature mapping, feature fusion and feature selection. For the initial feature mapping step, two pre-trained models are considered, such as DenseNet201 and InceptionV3. Later, the extracted deep features are fused using the Serial based Extended (SbE) approach. Later on, the best features are selected using Kurtosis-controlled Weighted KNN. The selected features are classified using several supervised learning algorithms. To show the efficacy of the proposed design, we used several datasets, such as KTH, IXMAS, WVU, and Hollywood. Experimental results showed that the proposed design achieved accuracies of 99.3%, 97.4%, 99.8%, and 99.9%, respectively, on these datasets. Furthermore, the feature selection step performed better in terms of computational time compared with the state-of-the-art. Full article
(This article belongs to the Special Issue Intelligent Sensors for Human Motion Analysis)
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Article
Gap Reconstruction in Optical Motion Capture Sequences Using Neural Networks
Sensors 2021, 21(18), 6115; https://doi.org/10.3390/s21186115 - 12 Sep 2021
Viewed by 632
Abstract
Optical motion capture is a mature contemporary technique for the acquisition of motion data; alas, it is non-error-free. Due to technical limitations and occlusions of markers, gaps might occur in such recordings. The article reviews various neural network architectures applied to the gap-filling [...] Read more.
Optical motion capture is a mature contemporary technique for the acquisition of motion data; alas, it is non-error-free. Due to technical limitations and occlusions of markers, gaps might occur in such recordings. The article reviews various neural network architectures applied to the gap-filling problem in motion capture sequences within the FBM framework providing a representation of body kinematic structure. The results are compared with interpolation and matrix completion methods. We found out that, for longer sequences, simple linear feedforward neural networks can outperform the other, sophisticated architectures, but these outcomes might be affected by the small amount of data availabe for training. We were also able to identify that the acceleration and monotonicity of input sequence are the parameters that have a notable impact on the obtained results. Full article
(This article belongs to the Special Issue Intelligent Sensors for Human Motion Analysis)
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Article
Attention-Based 3D Human Pose Sequence Refinement Network
Sensors 2021, 21(13), 4572; https://doi.org/10.3390/s21134572 - 03 Jul 2021
Viewed by 783
Abstract
Three-dimensional human mesh reconstruction from a single video has made much progress in recent years due to the advances in deep learning. However, previous methods still often reconstruct temporally noisy pose and mesh sequences given in-the-wild video data. To address this problem, we [...] Read more.
Three-dimensional human mesh reconstruction from a single video has made much progress in recent years due to the advances in deep learning. However, previous methods still often reconstruct temporally noisy pose and mesh sequences given in-the-wild video data. To address this problem, we propose a human pose refinement network (HPR-Net) based on a non-local attention mechanism. The pipeline of the proposed framework consists of a weight-regression module, a weighted-averaging module, and a skinned multi-person linear (SMPL) module. First, the weight-regression module creates pose affinity weights from a 3D human pose sequence represented in a unit quaternion form. Next, the weighted-averaging module generates a refined 3D pose sequence by performing temporal weighted averaging using the generated affinity weights. Finally, the refined pose sequence is converted into a human mesh sequence using the SMPL module. HPR-Net is a simple but effective post-processing network that can substantially improve the accuracy and temporal smoothness of 3D human mesh sequences obtained from an input video by existing human mesh reconstruction methods. Our experiments show that the noisy results of the existing methods are consistently improved using the proposed method on various real datasets. Notably, our proposed method reduces the pose and acceleration errors of VIBE, the existing state-of-the-art human mesh reconstruction method, by 1.4% and 66.5%, respectively, on the 3DPW dataset. Full article
(This article belongs to the Special Issue Intelligent Sensors for Human Motion Analysis)
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Article
Design of a Plantar Pressure Insole Measuring System Based on Modular Photoelectric Pressure Sensor Unit
Sensors 2021, 21(11), 3780; https://doi.org/10.3390/s21113780 - 29 May 2021
Viewed by 996
Abstract
Accurately perceiving and predicting the parameters related to human walking is very important for man–machine coupled cooperative control systems such as exoskeletons and power prostheses. Plantar pressure data is rich in human gait and posture information and is an essential source of reference [...] Read more.
Accurately perceiving and predicting the parameters related to human walking is very important for man–machine coupled cooperative control systems such as exoskeletons and power prostheses. Plantar pressure data is rich in human gait and posture information and is an essential source of reference information as the input of the exoskeleton control system. Therefore, the proper design of the pressure sensing insole and validation is a big challenge considering the requirements such as convenience, reliability, no interference and so on. In this research, we developed a low-cost modular sensing unit based on the principle of photoelectric sensing and designed a plantar pressure sensing insole to achieve the purpose of sensing human walking gait and posture information. On the one hand, the sensor unit is made of economy-friendly commercial flexible circuits and elastic silicone, and the mechanical and electrical characteristics of the modular sensor unit are evaluated by a self-developed pressure-related calibration system. The calibration results show that the modular sensor based on the photoelectric sensing principle has fast response and negligible hysteresis. On the other hand, we analyzed the area where the plantar pressure is densely distributed. One benefit of the modular sensing unit design is that it is rather convenient to fabricate different insole solutions, so we fabricated and compared several pressure-sensitive insole solutions in this preliminary study. During the dynamic locomotion experiments of wearing the pressure-sensing insole, the time series signal of each sensor unit was collected and analyzed. The results show that the pressure sensing insole based on the photoelectric effect can sense the distribution of the plantar pressure by capturing the deformation of the insole caused by the foot contact during locomotion, and provide reliable gait information for wearable applications. Full article
(This article belongs to the Special Issue Intelligent Sensors for Human Motion Analysis)
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Article
A Baseline for Cross-Database 3D Human Pose Estimation
Sensors 2021, 21(11), 3769; https://doi.org/10.3390/s21113769 - 28 May 2021
Viewed by 977
Abstract
Vision-based 3D human pose estimation approaches are typically evaluated on datasets that are limited in diversity regarding many factors, e.g., subjects, poses, cameras, and lighting. However, for real-life applications, it would be desirable to create systems that work under arbitrary conditions (“in-the-wild”). To [...] Read more.
Vision-based 3D human pose estimation approaches are typically evaluated on datasets that are limited in diversity regarding many factors, e.g., subjects, poses, cameras, and lighting. However, for real-life applications, it would be desirable to create systems that work under arbitrary conditions (“in-the-wild”). To advance towards this goal, we investigated the commonly used datasets HumanEva-I, Human3.6M, and Panoptic Studio, discussed their biases (that is, their limitations in diversity), and illustrated them in cross-database experiments (for which we used a surrogate for roughly estimating in-the-wild performance). For this purpose, we first harmonized the differing skeleton joint definitions of the datasets, reducing the biases and systematic test errors in cross-database experiments. We further proposed a scale normalization method that significantly improved generalization across camera viewpoints, subjects, and datasets. In additional experiments, we investigated the effect of using more or less cameras, training with multiple datasets, applying a proposed anatomy-based pose validation step, and using OpenPose as the basis for the 3D pose estimation. The experimental results showed the usefulness of the joint harmonization, of the scale normalization, and of augmenting virtual cameras to significantly improve cross-database and in-database generalization. At the same time, the experiments showed that there were dataset biases that could not be compensated and call for new datasets covering more diversity. We discussed our results and promising directions for future work. Full article
(This article belongs to the Special Issue Intelligent Sensors for Human Motion Analysis)
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Article
Non-Contact Monitoring and Classification of Breathing Pattern for the Supervision of People Infected by COVID-19
Sensors 2021, 21(9), 3172; https://doi.org/10.3390/s21093172 - 03 May 2021
Cited by 6 | Viewed by 1313
Abstract
During the pandemic of coronavirus disease-2019 (COVID-19), medical practitioners need non-contact devices to reduce the risk of spreading the virus. People with COVID-19 usually experience fever and have difficulty breathing. Unsupervised care to patients with respiratory problems will be the main reason for [...] Read more.
During the pandemic of coronavirus disease-2019 (COVID-19), medical practitioners need non-contact devices to reduce the risk of spreading the virus. People with COVID-19 usually experience fever and have difficulty breathing. Unsupervised care to patients with respiratory problems will be the main reason for the rising death rate. Periodic linearly increasing frequency chirp, known as frequency-modulated continuous wave (FMCW), is one of the radar technologies with a low-power operation and high-resolution detection which can detect any tiny movement. In this study, we use FMCW to develop a non-contact medical device that monitors and classifies the breathing pattern in real time. Patients with a breathing disorder have an unusual breathing characteristic that cannot be represented using the breathing rate. Thus, we created an Xtreme Gradient Boosting (XGBoost) classification model and adopted Mel-frequency cepstral coefficient (MFCC) feature extraction to classify the breathing pattern behavior. XGBoost is an ensemble machine-learning technique with a fast execution time and good scalability for predictions. In this study, MFCC feature extraction assists machine learning in extracting the features of the breathing signal. Based on the results, the system obtained an acceptable accuracy. Thus, our proposed system could potentially be used to detect and monitor the presence of respiratory problems in patients with COVID-19, asthma, etc. Full article
(This article belongs to the Special Issue Intelligent Sensors for Human Motion Analysis)
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Article
Combined Regularized Discriminant Analysis and Swarm Intelligence Techniques for Gait Recognition
Sensors 2020, 20(23), 6794; https://doi.org/10.3390/s20236794 - 27 Nov 2020
Viewed by 1051
Abstract
In the gait recognition problem, most studies are devoted to developing gait descriptors rather than introducing new classification methods. This paper proposes hybrid methods that combine regularized discriminant analysis (RDA) and swarm intelligence techniques for gait recognition. The purpose of this study is [...] Read more.
In the gait recognition problem, most studies are devoted to developing gait descriptors rather than introducing new classification methods. This paper proposes hybrid methods that combine regularized discriminant analysis (RDA) and swarm intelligence techniques for gait recognition. The purpose of this study is to develop strategies that will achieve better gait recognition results than those achieved by classical classification methods. In our approach, particle swarm optimization (PSO), grey wolf optimization (GWO), and whale optimization algorithm (WOA) are used. These techniques tune the observation weights and hyperparameters of the RDA method to minimize the objective function. The experiments conducted on the GPJATK dataset proved the validity of the proposed concept. Full article
(This article belongs to the Special Issue Intelligent Sensors for Human Motion Analysis)
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Review

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Review
Applications of Pose Estimation in Human Health and Performance across the Lifespan
Sensors 2021, 21(21), 7315; https://doi.org/10.3390/s21217315 - 03 Nov 2021
Cited by 1 | Viewed by 631
Abstract
The emergence of pose estimation algorithms represents a potential paradigm shift in the study and assessment of human movement. Human pose estimation algorithms leverage advances in computer vision to track human movement automatically from simple videos recorded using common household devices with relatively [...] Read more.
The emergence of pose estimation algorithms represents a potential paradigm shift in the study and assessment of human movement. Human pose estimation algorithms leverage advances in computer vision to track human movement automatically from simple videos recorded using common household devices with relatively low-cost cameras (e.g., smartphones, tablets, laptop computers). In our view, these technologies offer clear and exciting potential to make measurement of human movement substantially more accessible; for example, a clinician could perform a quantitative motor assessment directly in a patient’s home, a researcher without access to expensive motion capture equipment could analyze movement kinematics using a smartphone video, and a coach could evaluate player performance with video recordings directly from the field. In this review, we combine expertise and perspectives from physical therapy, speech-language pathology, movement science, and engineering to provide insight into applications of pose estimation in human health and performance. We focus specifically on applications in areas of human development, performance optimization, injury prevention, and motor assessment of persons with neurologic damage or disease. We review relevant literature, share interdisciplinary viewpoints on future applications of these technologies to improve human health and performance, and discuss perceived limitations. Full article
(This article belongs to the Special Issue Intelligent Sensors for Human Motion Analysis)
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