scholarly journals Experimental Study of a Deep-Learning RGB-D Tracker for Virtual Remote Human Model Reconstruction

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Shahram Payandeh ◽  
Jeffrey Wael
Keyword(s):  
Experimental Study ◽  
Deep Learning ◽  
Tracking System ◽  
Living Environment ◽  
The Body ◽  
Human Model ◽  
Virtual Human ◽  
Generic Model ◽  
Model Reconstruction ◽  
Position Information

Tracking movements of the body in a natural living environment of a person is a challenging undertaking. Such tracking information can be used as a part of detecting any onsets of anomalies in movement patterns or as a part of a remote monitoring environment. The tracking information can be mapped and visualized using a virtual avatar model of the tracked person. This paper presents an initial novel experimental study of using a commercially available deep-learning body tracking system based on an RGB-D sensor for virtual human model reconstruction. We carried out our study in an indoor environment under natural conditions. To study the performance of the tracker, we experimentally study the output of the tracker which is in the form of a skeleton (stick-figure) data structure under several conditions in order to observe its robustness and identify its drawbacks. In addition, we show and study how the generic model can be mapped for virtual human model reconstruction. It was found that the deep-learning tracking approach using an RGB-D sensor is susceptible to various environmental factors which result in the absence and presence of noise in estimating the resulting locations of skeleton joints. This as a result introduces challenges for further virtual model reconstruction. We present an initial approach for compensating for such noise resulting in a better temporal variation of the joint coordinates in the captured skeleton data. We explored how the extracted joint position information of the skeleton data can be used as a part of the virtual human model reconstruction.

scholarly journals Ensemble of Deep Learning Models for Sleep Apnea Detection: An Experimental Study

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5425
Author(s):  
Debadyuti Mukherjee ◽  
Koustav Dhar ◽  
Friedhelm Schwenker ◽  
Ram Sarkar
Keyword(s):  
Experimental Study ◽  
Deep Learning ◽  
Sleep Apnea ◽  
Choquet Integral ◽  
The Body ◽  
Majority Voting ◽  
Detection Accuracy ◽  
Learning Models ◽  
Ensemble Techniques ◽  
Obstructive Sleep

Sleep Apnea is a breathing disorder occurring during sleep. Older people suffer most from this disease. In-time diagnosis of apnea is needed which can be observed by the application of a proper health monitoring system. In this work, we focus on Obstructive Sleep Apnea (OSA) detection from the Electrocardiogram (ECG) signals obtained through the body sensors. Our work mainly consists of an experimental study of different ensemble techniques applied on three deep learning models—two Convolutional Neural Network (CNN) based models, and a combination of CNN and Long Short-Term Memory (LSTM) models, which were previously proposed in the OSA detection domain. We have chosen four ensemble techniques—majority voting, sum rule and Choquet integral based fuzzy fusion and trainable ensemble using Multi-Layer Perceptron (MLP) for our case study. All the experiments are conducted on the benchmark PhysioNet Apnea-ECG Database. Finally, we have achieved highest OSA detection accuracy of 85.58% using the MLP based ensemble approach. Our best result is also able to surpass many of state-of-the-art methods.

scholarly journals Validation method for body-mounted sensor attachments

2020 ◽  
Vol 6 (2) ◽  
Author(s):  
Katharina Schmidt ◽  
David Hochmann
Keyword(s):  
Evaluation Method ◽  
Tracking System ◽  
The Body ◽  
Exploratory Research ◽  
Body Segment ◽  
Validation Method ◽  
Sensor Position ◽  
Validation Parameters ◽  
Measurement Units ◽  
Soft Tissue Artifact

AbstractSmall sensor devices like inertial measurement units enable mobile movement and gait analysis, whereby existing systems differ in data acquisition, data processing, and gait parameter calculation. Concerning the validation, recent studies focus on the captured motion and the influence of sensor positioning with respect to the accuracy of the computed biomechanical parameters in comparison to a reference system. Although soft tissue artifact is a major source of error for skin-mounted sensors, there are no investigations regarding the relative movement between the body segment and sensor attachment itself. The aim of this study is to find an evaluation method and to determine parameters that allow the validation of various sensor attachment types and different sensor positionings. The analysis includes the comparison between an adhesive and strap attachment variant as well as the frontal and lateral sensor placement. To validate different attachments, an optical marker-based tracking system was used to measure the body segment and sensor position during movement. The distance between these two positions was calculated and analyzed to determine suitable validation parameters. Despite the exploratory research, the results suggest a feasible validation method to detect differences between the attachments, independent of the sensor type. To have representative and statistically validated results, further studies that involve more participants are necessary.

Embodying the culture of achievement: Culture between illness and perfection is a ‘thin line’ – obtaining the ideal female body as an act of achievement

2021 ◽  
pp. 1354067X2110040
Author(s):  
Josefine Dilling ◽  
Anders Petersen
Keyword(s):  
Experimental Study ◽  
Human Body ◽  
Female Body ◽  
The Body ◽  
Thin Line ◽  
Complex Processes ◽  
The Individual ◽  
Different Levels ◽  
The Ideal ◽  
Body Ideals

In this article, we argue that certain behaviour connected to the attempt to attain contemporary female body ideals in Denmark can be understood as an act of achievement and, thus, as an embodiment of the culture of achievement, as it is characterised in Præstationssamfundet, written by the Danish sociologist Anders Petersen (2016) Hans Reitzels Forlag . Arguing from cultural psychological and sociological standpoints, this article examines how the human body functions as a mediational tool in different ways from which the individual communicates both moral and aesthetic sociocultural ideals and values. Complex processes of embodiment, we argue, can be described with different levels of internalisation, externalisation and materialisation, where the body functions as a central mediator. Analysing the findings from a qualitative experimental study on contemporary body ideals carried out by the Danish psychologists Josefine Dilling and Maja Trillingsgaard, this article seeks to anchor such theoretical claims in central empirical findings. The main conclusions from the study are used to structure the article and build arguments on how expectations and ideals expressed in an achievement society become embodied.

scholarly journals Deep Learning-Based Congestion Detection at Urban Intersections

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2052
Author(s):  
Xinghai Yang ◽  
Fengjiao Wang ◽  
Zhiquan Bai ◽  
Feifei Xun ◽  
Yulin Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Optical Flow ◽  
Traffic Congestion ◽  
Detection Algorithm ◽  
Input Image ◽  
Vehicle Speed ◽  
Position Information ◽  
Traffic State ◽  
State Discrimination ◽  
Discrimination Method

In this paper, a deep learning-based traffic state discrimination method is proposed to detect traffic congestion at urban intersections. The detection algorithm includes two parts, global speed detection and a traffic state discrimination algorithm. Firstly, the region of interest (ROI) is selected as the road intersection from the input image of the You Only Look Once (YOLO) v3 object detection algorithm for vehicle target detection. The Lucas-Kanade (LK) optical flow method is employed to calculate the vehicle speed. Then, the corresponding intersection state can be obtained based on the vehicle speed and the discrimination algorithm. The detection of the vehicle takes the position information obtained by YOLOv3 as the input of the LK optical flow algorithm and forms an optical flow vector to complete the vehicle speed detection. Experimental results show that the detection algorithm can detect the vehicle speed and traffic state discrimination method can judge the traffic state accurately, which has a strong anti-interference ability and meets the practical application requirements.

scholarly journals Deep-Learning-Based Pupil Center Detection and Tracking Technology for Visible-Light Wearable Gaze Tracking Devices

2021 ◽  
Vol 11 (2) ◽  
pp. 851
Author(s):  
Wei-Liang Ou ◽  
Tzu-Ling Kuo ◽  
Chin-Chieh Chang ◽  
Chih-Peng Fan
Keyword(s):  
Deep Learning ◽  
Visible Light ◽  
Tracking System ◽  
Recall Rate ◽  
Detection Accuracy ◽  
Learning Technology ◽  
Tracking Errors ◽  
Gaze Tracking ◽  
Detection Technology ◽  
Pupil Tracking

In this study, for the application of visible-light wearable eye trackers, a pupil tracking methodology based on deep-learning technology is developed. By applying deep-learning object detection technology based on the You Only Look Once (YOLO) model, the proposed pupil tracking method can effectively estimate and predict the center of the pupil in the visible-light mode. By using the developed YOLOv3-tiny-based model to test the pupil tracking performance, the detection accuracy is as high as 80%, and the recall rate is close to 83%. In addition, the average visible-light pupil tracking errors of the proposed YOLO-based deep-learning design are smaller than 2 pixels for the training mode and 5 pixels for the cross-person test, which are much smaller than those of the previous ellipse fitting design without using deep-learning technology under the same visible-light conditions. After the combination of calibration process, the average gaze tracking errors by the proposed YOLOv3-tiny-based pupil tracking models are smaller than 2.9 and 3.5 degrees at the training and testing modes, respectively, and the proposed visible-light wearable gaze tracking system performs up to 20 frames per second (FPS) on the GPU-based software embedded platform.

scholarly journals Using vision-based object detection for link quality prediction in 5.6-GHz channel

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Riichi Kudo ◽  
Kahoko Takahashi ◽  
Takeru Inoue ◽  
Kohei Mizuno
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Wireless Lan ◽  
Autonomous Robots ◽  
Learning Technologies ◽  
Link Quality ◽  
Automated Driving ◽  
Position Information ◽  
Prediction Scheme ◽  
Accurate Perception

Abstract Various smart connected devices are emerging like automated driving cars, autonomous robots, and remote-controlled construction vehicles. These devices have vision systems to conduct their operations without collision. Machine vision technology is becoming more accessible to perceive self-position and/or the surrounding environment thanks to the great advances in deep learning technologies. The accurate perception information of these smart connected devices makes it possible to predict wireless link quality (LQ). This paper proposes an LQ prediction scheme that applies machine learning to HD camera output to forecast the influence of surrounding mobile objects on LQ. The proposed scheme utilizes object detection based on deep learning and learns the relationship between the detected object position information and the LQ. Outdoor experiments show that LQ prediction proposal can well predict the throughput for around 1 s into the future in a 5.6-GHz wireless LAN channel.

scholarly journals Classification of Shoulder X-ray Images with Deep Learning Ensemble Models

2021 ◽  
Vol 11 (6) ◽  
pp. 2723
Author(s):  
Fatih Uysal ◽  
Fırat Hardalaç ◽  
Ozan Peker ◽  
Tolga Tolunay ◽  
Nil Tokgöz
Keyword(s):  
Deep Learning ◽  
Performance Test ◽  
The Body ◽  
Test Accuracy ◽  
Cohen’S Kappa ◽  
X Ray ◽  
Cohen's Kappa ◽  
Auc Value ◽  
Magnetic Resonance Imaging Mri ◽  
Fully Connected

Fractures occur in the shoulder area, which has a wider range of motion than other joints in the body, for various reasons. To diagnose these fractures, data gathered from X-radiation (X-ray), magnetic resonance imaging (MRI), or computed tomography (CT) are used. This study aims to help physicians by classifying shoulder images taken from X-ray devices as fracture/non-fracture with artificial intelligence. For this purpose, the performances of 26 deep learning-based pre-trained models in the detection of shoulder fractures were evaluated on the musculoskeletal radiographs (MURA) dataset, and two ensemble learning models (EL1 and EL2) were developed. The pre-trained models used are ResNet, ResNeXt, DenseNet, VGG, Inception, MobileNet, and their spinal fully connected (Spinal FC) versions. In the EL1 and EL2 models developed using pre-trained models with the best performance, test accuracy was 0.8455, 0.8472, Cohen’s kappa was 0.6907, 0.6942 and the area that was related with fracture class under the receiver operating characteristic (ROC) curve (AUC) was 0.8862, 0.8695. As a result of 28 different classifications in total, the highest test accuracy and Cohen’s kappa values were obtained in the EL2 model, and the highest AUC value was obtained in the EL1 model.

scholarly journals An Important Need to Monitor from an Early Age the Neurotoxins in the Blood or by an Equivalent Biomarker

2019 ◽  
Vol 16 (18) ◽  
pp. 3425 ◽  
Author(s):  
Keith Schofield
Keyword(s):  
Risk Factors ◽  
Young Women ◽  
Medical Profession ◽  
Living Environment ◽  
The Body ◽  
Excessive Amount ◽  
Early Age ◽  
Broad Distribution ◽  
Societal Changes ◽  
Collaborative Interaction

An overwhelming amount of evidence now suggests that some people are becoming overloaded with neurotoxins. This is mainly from changes in their living environment and style, coupled with the fact that all people are different and display a broad distribution of genetic susceptibilities. It is important for individuals to know where they lie concerning their ability to either reject or retain toxins. Everyone is contaminated with a certain baseline of toxins that are alien to the body, namely aluminum, arsenic, lead, and mercury. Major societal changes have modified their intake, such as vaccines in enhanced inoculation procedures and the addition of sushi into diets, coupled with the ever-present lead, arsenic, and traces of manganese. It is now apparent that no single toxin is responsible for the current neurological epidemics, but rather a collaborative interaction with possible synergistic components. Selenium, although also a neurotoxin if in an excessive amount, is always present and is generally more present than other toxins. It performs as the body’s natural chelator. However, it is possible that the formation rates of active selenium proteins may become overburdened by other toxins. Every person is different and it now appears imperative that the medical profession establish an individual’s neurotoxicity baseline. Moreover, young women should certainly establish their baselines long before pregnancy in order to identify possible risk factors.

Optimizing human model reconstruction from RGB-D images based on skin detection

2016 ◽  
Vol 20 (3) ◽  
pp. 159-172 ◽  
Author(s):  
Guang Chen ◽  
Jituo Li ◽  
Jiping Zeng ◽  
Bei Wang ◽  
Guodong Lu
Keyword(s):  
Human Model ◽  
Skin Detection ◽  
Model Reconstruction

MENGESAN KEWUJUDAN JIN BERDASARKAN PERUBAHAN WARNA AURA

2017 ◽  
Vol 5 (2) ◽  
pp. 36-49
Author(s):  
YOHAN KURNIAWAN ◽  
ALEXANDER STRAK ◽  
BURHAN BIN CHE DAUD ◽  
HISHAMUDDIN MD. SOM ◽  
ABDUL AZIZ BIN SUAIB
Keyword(s):  
Experimental Study ◽  
Data Collection ◽  
The Body ◽  
Data Collection Methods ◽  
Red Color ◽  
Holy Quran ◽  
The Holy Quran ◽  
Color Data ◽  
Source Of Information ◽  
Collection Methods

Kewujudan jin merupakan suatu kepercayaan yang telah lama wujud dalam kalangan masyarakat Melayu di Nusantara, khususnya di Malaysia. Kewujudan jin telah diterangkan secara jelas dalam kitab suci Al-Quran. Walaupun terdapat sumber maklumat yang sahih akan tetapi kewujudan jin ini belum dapat dibuktikan secara saintifi k. Kajian yang dijalankan ini bertujuan untuk mengenal pasti dan memahami fenomena kewujudan jin berdasarkan warna aura. Kajian yang dijalankan merupakan kajian eksperimental dan melibatkan seorang responden yang memiliki saka. Kaedah pengumpulan data yang digunakan dalam kajian ini adalah perubahan warna aura dan temu bual. Peralatan kajian yang digunakan adalahperalatan WinAura untuk mendapatkan data perubahan warna aura, dan peralatan rakaman untuk data temu bual. Hasil kajian mendapati kewujudan jin dalam badan seseorang ditandai dengan warna merah yang wujud secara tiba-tiba dan konsisten pada bahagian badan tertentu terutamanya pada bahagian dahi, tekak atau pada bahagian badan sebelah kiri. Hasil temu bual mendapati rawatan perubatan Islam yang dilakukan oleh responden mengatakan bahawa terdapat jin dalam diri responden. Kajian ini berjaya membuktikan kewujudan jin dalam diri responden berdasarkan perubahan warna aura.   The existence of the jinn is a phenomena that has long existed among the Malay community in Nusantara, especially in Malaysia. The existence of the jinn has been described clearly in the Holy Quran. Although there is a valid source of information, unfortunately the existence of the genie has not been scientifically proven. The study was aimed to identify and understand the phenomenon of jinn existence based on aura color. The study was an experimental study and involved a respondent who has saka. Data collection methods were used in this study was aura change color and interview. The equipments was used in this research were the WinAura machine to obtain of changing color data of the aura. The study found that the existence of genie in a person’s body was characterized by suddenly and consistent appear of red color in certain parts of the body, especially on the forehead, throat or on the left side of the body. The interviews also found that the characteristics and experience that the respondent’s surrounding and the there are energy or strength that followed respondent. This study proved the existence of supernatural beings (genie) based on aura change color.

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