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1.
J Acoust Soc Am ; 148(4): 2096, 2020 10.
Artigo em Inglês | MEDLINE | ID: mdl-33138536

RESUMO

Brass wind instruments with long sections of cylindrical pipe, such as trumpets and trombones, sound "brassy" when played at a fortissimo level due to the generation of a shock front in the instrument. It has been suggested that these shock fronts may increase the spread of COVID-19 by propelling respiratory particles containing the SARS-CoV-2 virus several meters due to particle entrainment in the low pressure area behind the shocks. To determine the likelihood of this occurring, fluorescent particles, ranging in size from 10-50 µm, were dropped into the shock regions produced by a trombone, a trumpet, and a shock tube. Preliminary results indicate that propagation of small airborne particles by the shock fronts radiating from brass wind instruments is unlikely.


Assuntos
Betacoronavirus/patogenicidade , Infecções por Coronavirus/transmissão , Exposição por Inalação/prevenção & controle , Música , Pneumonia Viral/transmissão , Isolamento Social , Aerossóis , Infecções por Coronavirus/prevenção & controle , Infecções por Coronavirus/virologia , Desenho de Equipamento , Interações Hospedeiro-Patógeno , Humanos , Movimento (Física) , Pandemias/prevenção & controle , Tamanho da Partícula , Pneumonia Viral/prevenção & controle , Pneumonia Viral/virologia
2.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 4024-4029, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018882

RESUMO

This paper presents a novel method for tracking gaiting-based (changing contacts, reciprocal, cyclical) withinhand manipulation strategies of a human hand. We present a kinematic model that relies on data collected from 6-DOF magnetic sensors attached to 7 external sites on the hand. The sensors are calibrated by three procedures-sensor-to-fingertip, constrained fingertip workspace limits, and flat hand configuration. Subjects rotated two cubes of different sizes around the 3 object-centric axes, while a synchronized camera recorded the object motion. Hand motions were segmented and then averaged using dynamic time warping (DTW) to yield a representative time-series motion primitive for the given task. The hand movements of two subjects during cube rotation tasks were reconstructed using a 22-degree of freedom (DOF) hand kinematic model. Based on a qualitative evaluation of the joint movements, intrasubject correlations of joint angles were found.


Assuntos
Marcha , Mãos , Fenômenos Biomecânicos , Humanos , Movimento (Física) , Movimento
3.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 4038-4041, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018885

RESUMO

The current work presents the development and technical validation, in terms of accuracy and latency, of a low-cost portable device that allows identifying possible risks of falling in people when they realize spinal trunk lateral movements. The device is comprised of an Inertial Measurement Unit (IMU) located on the lower back. Measurements are processed to get meaningful parameters such as rotation angles of the back when realizing lateral movements. In order to give performance feedback while doing the test, this device includes a Microcontroller as Raspberry Pi to return visual feedback to the person. The critical system feature is the latency of the system since getting the data of a movement until showing that on the feedback screen. For that reason, before to start assessing people, we propose a technical method using the Mikrolar Hexapod Robot R3000 for validating the system developed by simulating the movement of the back and recording it with a video camera to apply an offline Motion-to-Photon Latency analysis.


Assuntos
Movimento , Tronco , Retroalimentação Sensorial , Movimento (Física) , Coluna Vertebral
4.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 4114-4117, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018903

RESUMO

Assessment of pulmonary function is vital for early detection of chronic diseases such as chronic obstructive pulmonary disease (COPD) in home healthcare. However, monitoring of pulmonary function is often omitted owing to the heavy burden that the use of specific medical devices places on the patients. In this study, we developed a non-contact spirometer using a time-of-flight sensor that measures very small displacements caused by chest wall motion during breathing. However, this sensor occasionally failed when estimating the values from breathing waveforms because their shape depends on the subject test experience. As a result, further measurements were required to address motion artifacts. To accomplish high accuracy estimation in the face of these factors, we developed methods to estimate parameters from a part of the waveform and remove outliers from multiple-region measurements. According to laboratory experiments, the proposed system achieved an absolute error of 5.26 % and a correlation coefficient of 0.88. This study also addressed the limitations of depth sensor measurements, thereby contributing to the implementation of high-accuracy COPD screening.


Assuntos
Doença Pulmonar Obstrutiva Crônica , Respiração , Artefatos , Humanos , Movimento (Física) , Doença Pulmonar Obstrutiva Crônica/diagnóstico , Espirometria
5.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 803-807, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018107

RESUMO

Motion rehabilitation is increasingly required owing to an aging population and suffering of stroke, which means human motion analysis must be valued. Based on the concept mentioned above, a deep-learning-based system is proposed to track human motion based on three-dimensional (3D) images in this work; meanwhile, the features of traditional red green blue (RGB) images, known as two-dimensional (2D) images, were used as a comparison. The results indicate that 3D images have an advantage over 2D images due to the information of spatial relationships, which implies that the proposed system can be a potential technology for human motion analysis applications.


Assuntos
Algoritmos , Aprendizado Profundo , Idoso , Humanos , Imageamento Tridimensional , Movimento (Física)
6.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 940-943, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018139

RESUMO

Motion artifact contamination may adversely affect the interpretation of biological signals. The development of algorithms to detect, identify, quantify, and mitigate motion artifact is typically performed using a ground truth signal contaminated with previously recorded motion artifact, or simulated motion artifact. The diversity of available motion artifact recordings is limited, and the rationales for existing models of motion artifact are poorly described. In this paper we developed an autoregressive (AR) model of motion artifact based on data collected from 6 subjects walking at slow, medium, and fast paces. The AR model was evaluated for its ability to generate diverse data that replicated the properties of the experimental data. The simulated motion artifact data was successful at learning key time domain and frequency domain properties, including the mean, variance, and power spectrum of the data, but was ineffective for imitating the morphology and probability distribution of the motion artifact data (kurtosis % error of 100.9-103.6%). More sophisticated models of motion artifact may be necessary to develop simulations of motion artifact.


Assuntos
Artefatos , Processamento de Sinais Assistido por Computador , Algoritmos , Movimento (Física) , Caminhada
7.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 1194-1197, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018201

RESUMO

Over the last few years, camera-based estimation of vital signs referred to as imaging photoplethysmography (iPPG) has garnered significant attention due to the relative simplicity, ease, unobtrusiveness and flexibility offered by such measurements. It is expected that iPPG may be integrated into a host of emerging applications in areas as diverse as autonomous cars, neonatal monitoring, and telemedicine. In spite of this potential, the primary challenge of non-contact camera-based measurements is the relative motion between the camera and the subjects. Current techniques employ 2D feature tracking to reduce the effect of subject and camera motion but they are limited to handling translational and in-plane motion. In this paper, we study, for the first-time, the utility of 3D face tracking to allow iPPG to retain robust performance even in presence of out-of-plane and large relative motions. We use a RGB-D camera to obtain 3D information from the subjects and use the spatial and depth information to fit a 3D face model and track the model over the video frames. This allows us to estimate correspondence over the entire video with pixel-level accuracy, even in the presence of out-of-plane or large motions. We then estimate iPPG from the warped video data that ensures per-pixel correspondence over the entire window-length used for estimation. Our experiments demonstrate improvement in robustness when head motion is large.


Assuntos
Algoritmos , Fotopletismografia , Face , Monitorização Fisiológica , Movimento (Física)
8.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 2027-2030, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018402

RESUMO

Ultrasound elastography is used to estimate the mechanical properties of the tissue by monitoring its response to an internal or external force. Different levels of deformation are obtained from different tissue types depending on their mechanical properties, where stiffer tissues deform less. Given two radio frequency (RF) frames collected before and after some deformation, we estimate displacement and strain images by comparing the RF frames. The quality of the strain image is dependent on the type of motion that occurs during deformation. In-plane axial motion results in high-quality strain images, whereas out-of-plane motion results in low-quality strain images. In this paper, we introduce a new method using a convolutional neural network (CNN) to determine the suitability of a pair of RF frames for elastography in only 5.4 ms. Our method could also be used to automatically choose the best pair of RF frames, yielding a high-quality strain image. The CNN was trained on 3,818 pairs of RF frames, while testing was done on 986 new unseen pairs, achieving an accuracy of more than 91%. The RF frames were collected from both phantom and in vivo data.


Assuntos
Técnicas de Imagem por Elasticidade , Algoritmos , Movimento (Física) , Redes Neurais de Computação , Imagens de Fantasmas
9.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 2142-2146, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018430

RESUMO

Block matching techniques have been studied exhaustively for motion estimation in Ultrasound (US) images. Exhaustive Search (ES) is the most commonly used search algorithm for block matching in US images. However, ES can be computationally expensive and slow. In this paper, a faster search algorithm called the Adaptive Rood Pattern Search (ARPS) is adopted to US images along with subpixel matching to reduce the computational cost and enhance block matching. Both ES and ARPS were applied in the context of block matching based 2D speckle tracking and were compared using Number of Computations per Frame (NCF), Computational Time per Frame (CTF) and Root Mean Squared Error (RMSE) as metrics. Our simulations and experimental results proved that ARPS outperformed ES by a substantial margin. Adaptation of this technique could help improve the performance of real-time motion estimation drastically.


Assuntos
Algoritmos , Movimento (Física) , Ultrassonografia
10.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 2703-2706, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018564

RESUMO

This work is aimed to establish engineering theories of the coupled longitudinal and radial motion of the arterial wall. By treating the arterial wall as a piano string in the longitudinal direction and as a viscoelastic material in the circumferential direction, and considering pulsatile pressure and wall shear stress from axial blood flow in an artery, the fully-formed governing equations of the coupled motion of the arterial wall are obtained and are related to the engineering theories of axial blood flow for a unified engineering understanding of blood circulation in the cardiovascular (CV) system. The longitudinal wall motion and the radial wall motion are essentially a longitudinal elastic wave and a transverse elastic wave, respectively, traveling along the arterial tree, with their own propagation velocities dictated by the physical properties and geometrical parameters of the arterial wall. The longitudinal initial tension is essential for generating a transverse elastic wave in the arterial wall to accompany the pulsatile pressure wave in axial blood flow. Under aging and subclinical atherosclerosis, propagation of the two elastic waves and coupling of the two elastic waves weakens and consequently might undermine blood circulation.


Assuntos
Artérias , Fenômenos Fisiológicos Sanguíneos , Movimento (Física) , Estresse Mecânico
11.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 3015-3018, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018640

RESUMO

Electroencephalogram (EEG) based braincomputer interface (BCI) systems are useful tools for clinical purposes like neural prostheses. In this study, we collected EEG signals related to grasp motions. Five healthy subjects participated in this experiment. They executed and imagined five sustained-grasp actions. We proposed a novel data augmentation method that increases the amount of training data using labels obtained from electromyogram (EMG) signals analysis. For implementation, we recorded EEG and EMG simultaneously. The data augmentation over the original EEG data concluded higher classification accuracy than other competitors. As a result, we obtained the average classification accuracy of 52.49(±8.74)% for motor execution (ME) and 40.36(±3.39)% for motor imagery (MI). These are 9.30% and 6.19% higher, respectively than the result of the comparable methods. Moreover, the proposed method could minimize the need for the calibration session, which reduces the practicality of most BCIs. This result is encouraging, and the proposed method could potentially be used in future applications such as a BCI-driven robot control for handling various daily use objects.


Assuntos
Interfaces Cérebro-Computador , Eletroencefalografia , Força da Mão , Movimento (Física) , Movimento
12.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 3126-3129, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018667

RESUMO

Continuous and accurate decoding of intended motions is critical for human-machine interactions. Here, we developed a novel approach for real-time continuous prediction of forces in individual fingers using parallel convolutional neural networks (CNNs). We extracted populational motor unit discharge frequency using CNNs in a parallel structure without spike sorting. The CNN parameters were trained based on two features from high-density electromyogram (HD-EMG), namely temporal energy heatmaps and frequency spectrum maps. The populational motor unit discharge frequency was then used to continuously predict finger forces based on a linear regression model. The force prediction performance was compared with a motor unit decomposition method and the conventional EMG amplitude-based method. Our results showed that the correlation coefficient between the predicted and the recorded forces of the CNN approach was on average 0.91, compared with the offline decomposition method of 0.89, the online decomposition method of 0.82, and the EMG amplitude method of 0.81. Additionally, the CNN based approach showed generalizable performance, with CNN trained on one finger applicable to a different finger. The outcomes suggest that our CNN based algorithm can offer an accurate and efficient force decoding method for human-machine interactions.


Assuntos
Dedos , Redes Neurais de Computação , Algoritmos , Eletromiografia , Humanos , Movimento (Física)
13.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 3318-3322, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018714

RESUMO

Vestibular perception is useful to maintain heading direction and successful spatial navigation. In this study, we present a novel equipment capable of delivering both rotational and translational movements, namely the RT-Chair. The system comprises two motors and it is controlled by the user via MATLAB. To validate the measurability of vestibular perception with the RT-chair, we ran a threshold measurement experiment with healthy participants. Our results show thresholds comparable to previous literature, thus confirming the validity of the system to measure vestibular perception.


Assuntos
Percepção de Movimento , Navegação Espacial , Vestíbulo do Labirinto , Cabeça , Humanos , Movimento (Física)
14.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 3676-3679, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018798

RESUMO

Finger tapping test is an important neuropsychological test to evaluate human motor function. Most recent researches simplified the finger tapping motion as a scissors-like motion, though the rotation axis of the thumb was different from that of the forefinger. In this paper, we proposed a three-dimensional (3-D) finger tapping measurement system to obtain 3-D pattern features in finger tapping test for patients with Parkinson's disease (PD). The proposed system collected the motion of the thumb and the forefinger by nine-degrees-freedom sensors and calculated 3-D motion of finger tapping by an orientation estimation method and a 3-D finger-tapping kinematic model. We further extracted 3-D pattern features, i.e. motor coordination and relative thumb motion, from 3-D Finger Tapping motion. Moreover, we used the proposed system to collect the finger-tapping motion of 43 PD patients and 30 healthy controls in horizontal tasks and vertical tasks. The results indicated that 3-D pattern features showed a better performance than one-dimensional features in the identification of mild PD patients.Clinical Relevance- These three-dimensional pattern features could be used to evaluate finger tapping motion in a novel way, which could be used to better identify mild Parkinson's disease patients. Furthermore, the results showed that a combination of horizontal tasks and vertical tasks might be a better way to identify mild Parkinson's disease patients.


Assuntos
Doença de Parkinson , Fenômenos Biomecânicos , Dedos , Humanos , Movimento (Física) , Doença de Parkinson/diagnóstico , Polegar
15.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 3688-3691, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018801

RESUMO

Gait motion patterns such as step length, flexed posture, absent arm swing and bradykinesia, constitute the main source of information to describe and quantify Parkinson disease. Nevertheless, such quantification is commonly developed under marker based protocols, losing natural motion gestures, and only taking into account a limited description of the locomotion process. This work introduces a 3D convolutional gait representation, that uses markerless video sequences to automatically predict parkinsonian behaviours. A remarkable contribution herein presented is the quantification of spatio-temporal salient maps, that stand out body regions related with Parkinson disease, and result from activations that mainly contribute on the classification task. For doing so, a convolutional architecture is trained from a set of walking videos, recorded from parkinsonian and control subjects. Then, a prediction of disease is obtained according to motion patterns computed by convolutional learned scheme. Salience motion patterns are obtained by retro-propagating the output softmax network prediction over the video space. From a total of 22 patients, and a total of 176 video sequences, the proposed approach achieved an average accuracy score of 88%. Interestingly enough, the recovered salience maps focus the attention on relevant parkinsonian biomarkers such as the head motion and trunk posture, that namely is excluded on classical gait analysis.Clinical relevance- Salient spatio-temporal regions can potentially support and complement the diagnosis and the following of Parkinson's disease. Also, such complex relationships could potentially evolve in further understanding of this pathology.


Assuntos
Doença de Parkinson , Caminhada , Marcha , Análise da Marcha , Humanos , Movimento (Física)
16.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 3755-3758, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018818

RESUMO

Despite recent advancements in the field of pattern recognition-based myoelectric control, the collection of a high quality training set remains a challenge limiting its adoption. This paper proposes a framework for a possible solution by augmenting short training protocols with subject-specific synthetic electromyography (EMG) data generated using a deep generative network, known as SinGAN. The aim of this work is to produce high quality synthetic data that could improve classification accuracy when combined with a limited training protocol. SinGAN was used to generate 1000 synthetic windows of EMG data from a single window of six different motions, and results were evaluated qualitatively, quantitatively, and in a classification task. Qualitative assessment of synthetic data was conducted via visual inspection of principal component analysis projections of real and synthetic feature space. Quantitative assessment of synthetic data revealed 11 of 32 synthetic features had similar location and scale to real features (using univariate two-sample Lepage tests); whereas multivariate distributions were found to be statistically different (p <0.05). Finally, the addition of these synthetic data to a brief training set of real data significantly improved classification accuracy in a cross-validation testing scheme by 5.4% (p <0.001).


Assuntos
Eletromiografia , Detecção de Sinal Psicológico , Estudos de Viabilidade , Movimento (Física) , Análise de Componente Principal
17.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 4165-4168, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33018915

RESUMO

Wearable motion sensor-based complex activity recognition during working hours has recently been studied to evaluate and thereby improve worker productivity. In the application of this technique to practical fields, one of the biggest challenges is performing time-consuming modeling tasks such as data labeling and hand-crafted feature extraction. One way to enable faster modeling is to decrease the time required for the manual tasks by making use of unlabeled motion datasets and the characteristics of complex activities. In this study, we propose a working activity recognition method that combines unsupervised encoding of the activity patterns of motions (denoted as "atomic activities"), the representation of working activities by combination of atomic activities, and the integration of additional information such as sensor time. We evaluated our method using an actual dataset from the caregiving field and found that it had an equivalent recognition performance (70.3% macro F-measure) to conventional hand-crafted feature extraction method. This is also comparable to that of previous methods using large labeled datasets. We also found that our method could visualize daily work processes with the accuracy of 71.2%. These results indicate that the proposed method has the potential to contribute to the rapid implementation of working activity recognition in actual working fields.


Assuntos
Mãos , Atividades Humanas , Aprendizado de Máquina , Movimento , Carga de Trabalho , Humanos , Movimento (Física)
18.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 4745-4748, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33019051

RESUMO

Brain-computer interfaces (BCIs) allow for translating electroencephalogram (EEG) into control commands, e.g., to control a quadcopter. This study, we developed a practical BCI based on steady-state visually evoked potential (SSVEP) for continuous control of a quadcopter from the first-person perspective. Users watched with the video stream from a camera on the quadcopter. An innovative user interface was developed by embedding 12 SSVEP flickers into the video stream, which corresponded to the flight commands of 'take-off,' 'land,' 'hover,' 'keep-going,' 'clockwise,' 'counter-clockwise' and rectilinear motions in six directions, respectively. The command was updated every 400ms by decoding the collected EEG data using a combined classification algorithm based on task-related component analysis (TRCA) and linear discriminant analysis (LDA). The quadcopter flew in the 3-D space according to the control vector that was determined by the latest four commands. Three novices participated in this study. They were asked to control the quadcopter by either the brain or hands to fly through a circle and land on the target zone. As a result, the time consumption ratio of brain-control to hand-control was as low as 1.34, which means the BCI performance was close to hands. The information transfer rate reached a peak of 401.79 bits/min in the simulated online experiment. These results demonstrate the proposed SSVEP-BCI system is efficient for controlling the quadcopter.


Assuntos
Interfaces Cérebro-Computador , Eletroencefalografia , Potenciais Evocados , Potenciais Evocados Visuais , Humanos , Movimento (Física)
19.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 5789-5793, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33019290

RESUMO

Current clinical practice of measuring hand joint range of motion relies on a goniometer as it is inexpensive, portable, and easy to use, but it can only measure the static angle of a single joint at a time. To measure dynamic hand motion, a camera-based system that can perform markerless hand pose estimation is attractive, as the system is ubiquitous, low-cost, and non-contact. However, camera-based systems require line-of-sight, and tracking accuracy degrades when the joint is occluded from the camera view. Thus, we propose a multi-view setup using a readily available color camera from a single mobile phone, and plane mirrors to create multiple views of the hand. This setup eliminates the complexity of synchronizing multiple cameras and reduce the issue of occlusion. Experimental results show that the multi-view setup could help to reduce the error in measuring the flexion angle of finger joints. Dynamic hand pose estimation with object interaction is also demonstrated.


Assuntos
Articulações dos Dedos , Mãos , Movimento (Física) , Amplitude de Movimento Articular
20.
Annu Int Conf IEEE Eng Med Biol Soc ; 2020: 5900-5904, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-33019317

RESUMO

In this paper, we propose a novel approach for respiratory monitoring through the direct measurement of oral cavity pressure. To measure the oral cavity pressure, a pressure sensor is placed inside the oral cavity. The intraorally obtained pressure signals are analyzed in the time-domain and validated against the conventional respiration monitoring belt (reference measurement). Tests have been performed on four subjects (four tests on each subject) in stationary and non-stationary conditions to evaluate the usage of the system in real life. Measurement from the proposed system shows that our approach can monitor the respiration rate with an accuracy of 99% when compared to the reference measurement. Moreover, the system can effectively track the respiration pattern and can detect breathing events independent of breathing routes, i.e., the nasal and oral. It has the minimum susceptibility to motion artifacts. Therefore, it has potential to be used as a wearable monitoring system for day to day life.


Assuntos
Artefatos , Respiração , Monitorização Fisiológica , Movimento (Física) , Boca
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