scholarly journals Applications of Pose Estimation in Human Health and Performance across the Lifespan

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7315
Author(s):  
Jan Stenum ◽  
Kendra M. Cherry-Allen ◽  
Connor O. Pyles ◽  
Rachel D. Reetzke ◽  
Michael F. Vignos ◽  
...  
Keyword(s):  
Human Health ◽  
Pose Estimation ◽  
Performance Optimization ◽  
Low Cost ◽  
Relevant Literature ◽  
Human Movement ◽  
Motor Assessment ◽  
Estimation Algorithms ◽  
Human Pose ◽  
And Performance

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.

An integrated vision-based system for efficient robot arm teleoperation

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Xin Wu ◽  
Canjun Yang ◽  
Yuanchao Zhu ◽  
Weitao Wu ◽  
Qianxiao Wei
Keyword(s):  
Pose Estimation ◽  
Low Cost ◽  
Extraction Process ◽  
Robot Arm ◽  
Mapping Procedure ◽  
Content Type ◽  
Qualitative And Quantitative ◽  
Motion Constraints ◽  
Teleoperation System ◽  
Human Pose

Purpose This paper aims to present a natural human–robot teleoperation system, which capitalizes on the latest advancements of monocular human pose estimation to simplify scenario requirements on heterogeneous robot arm teleoperation. Design/methodology/approach Several optimizations in the joint extraction process are carried on to better balance the performance of the pose estimation network. To bridge the gap between human joint pose in Cartesian space and heterogeneous robot joint angle pose in Radian space, a routinized mapping procedure is proposed. Findings The effectiveness of the developed methods on joint extraction is verified via qualitative and quantitative experiments. The teleoperation experiments on different robots validate the feasibility of the system controlling. Originality/value The proposed system provides an intuitive and efficient human–robot teleoperation method with low-cost devices. It also enhances the controllability and flexibility of robot arms by releasing human operator from motion constraints, paving a new way for effective robot teleoperation.

scholarly journals Motion-core assistive tools using pervasive embedded intelligence

2021 ◽  
Vol 2 (1) ◽  
pp. 10-21
Author(s):  
S. M. Namal Arosha Senanayake ◽  
Keyword(s):  
Performance Optimization ◽  
Movement Analysis ◽  
Human Movement ◽  
Decision Fusion ◽  
Human Motion ◽  
Test Subject ◽  
Data Mapping ◽  
Translation Techniques ◽  
And Performance ◽  
Embedded Intelligence

Real-time human movement monitoring anywhere at any time is time critical depending on core human motion activities, in particular nation’s valuable asserts; athletes and soldiers considered as reference standard of any society. Light weight wearable technologies are the key measurements and instruments system integrated to develop human motion-core assistive tools (MAT) using pervasive embedded intelligence. Unlike many existing motion analysis models, motion-core models are based on domain specific data service architectures beyond cloud technologies using inner data structures and data models created. Four layered micro system architecture that consists of sensing, networking, service and Motion-core IoT (MIoT) is proposed. Knowledge base was designed as a distributed and networked data center based on transient and resident data addressing modes in order to guarantee the secure data accessing, propagating, visualizing and control between these two modes of operations. While transient data change and avail in relevant clouds storages, corresponding resident data and processed data retain inside local servers or/and private clouds. Data mapping and translation techniques are applied for the formation of complete motion-core data packet related to the test subject under consideration. Thus, hybrid MIoT system is developed using 3D decision fusion models which are the internationally quantifiable standards for assessing human motion set by trainers, coachers, physiotherapists and orthopedics. MIoT built as motion-core assistive tools have been tested for rehabilitation monitoring, injury prevention and performance optimization of athletes, soldiers, and general public. The hybrid system introduced in this work is novel and proves lower down the latency and connectivity independence by allowing human movement analysis during daily active lifestyle.

scholarly journals Monitoring and controlling the speed and direction of a DC motor through FPGA and comparison of FPGA for speed and performance optimization

2021 ◽  
Vol 11 (5) ◽  
pp. 3903
Author(s):  
Huda M. Abdul Abbas ◽  
Raad Farhood Chisab ◽  
Mohannad Jabbar Mnati
Keyword(s):  
Real Time ◽  
Performance Optimization ◽  
Pulse Width Modulation ◽  
Electronic Device ◽  
Low Cost ◽  
Dc Motor ◽  
Know How ◽  
Field Programmable ◽  
Programmable Gate Arrays ◽  
And Performance

<span lang="EN-US">We are living in the 21<sup>st</sup> century, an era of acquiring necessity in one click. As we, all know that technology is continuously reviving to stay ahead of advancements taking place in this world of making things easier for mankind. Technology has been putting his part in introducing different projects as we have used the field programmable gate arrays (FPGAs) development board of low cost and programmable logic done by the new evolvable cyclone software is optimized for specific energy based on Altera Cyclone II (EP2C5T144) through which we can control the speed of any electronic device or any Motor Control IP product targeted for the fan and pump. Altera Cyclone FPGAs’ is a board through which we can monitor the speed and direction of the DC motor. As we know how to make understand, dynamic analog input using an A-to-D convertor and we know how to create pulse width modulation (PWM) output with FPGA. Therefore, by combining these two functions we can create an FPGA DC motor controller. Our paper is divided into three parts: First, all of us will attempt to imitate the issue and can try to look for its answer. Secondly, we will try to verify the solution for real-time. In addition, in the last step, we will verify the solution on the real-time measurements.</span>

scholarly journals Model-Based 3D Pose Estimation of a Single RGB Image Using a Deep Viewpoint Classification Neural Network

2019 ◽  
Vol 9 (12) ◽  
pp. 2478 ◽  
Author(s):  
Jui-Yuan Su ◽  
Shyi-Chyi Cheng ◽  
Chin-Chun Chang ◽  
Jing-Ming Chen
Keyword(s):  
Neural Network ◽  
Pose Estimation ◽  
Low Cost ◽  
Estimation Algorithm ◽  
Training Dataset ◽  
3D Pose Estimation ◽  
Estimation Algorithms ◽  
Model Based ◽  
3D Scene ◽  
Rgb Image

This paper presents a model-based approach for 3D pose estimation of a single RGB image to keep the 3D scene model up-to-date using a low-cost camera. A prelearned image model of the target scene is first reconstructed using a training RGB-D video. Next, the model is analyzed using the proposed multiple principal analysis to label the viewpoint class of each training RGB image and construct a training dataset for training a deep learning viewpoint classification neural network (DVCNN). For all training images in a viewpoint class, the DVCNN estimates their membership probabilities and defines the template of the class as the one of the highest probability. To achieve the goal of scene reconstruction in a 3D space using a camera, using the information of templates, a pose estimation algorithm follows to estimate the pose parameters and depth map of a single RGB image captured by navigating the camera to a specific viewpoint. Obviously, the pose estimation algorithm is the key to success for updating the status of the 3D scene. To compare with conventional pose estimation algorithms which use sparse features for pose estimation, our approach enhances the quality of reconstructing the 3D scene point cloud using the template-to-frame registration. Finally, we verify the ability of the established reconstruction system on publicly available benchmark datasets and compare it with the state-of-the-art pose estimation algorithms. The results indicate that our approach outperforms the compared methods in terms of the accuracy of pose estimation.

Design, Modeling, and Performance Optimization of a Reversible Heat Pump/Organic Rankine Cycle System for Domestic Application

2015 ◽  
Vol 138 (1) ◽  
Author(s):  
Sylvain Quoilin ◽  
Olivier Dumont ◽  
Kristian Harley Hansen ◽  
Vincent Lemort
Keyword(s):  
Heat Pump ◽  
Performance Optimization ◽  
Low Cost ◽  
Organic Rankine Cycle ◽  
Rankine Cycle ◽  
Operating Conditions ◽  
Working Fluid ◽  
Thermal Source ◽  
Cycle System ◽  
And Performance

In this paper, an innovative system combining a heat pump (HP) and an organic Rankine cycle (ORC) process is proposed. This system is integrated with a solar roof, which is used as a thermal source to provide heat in winter months (HP mode) and electricity in summer months (ORC mode) when an excess irradiation is available on the solar roof. The main advantage of the proposed unit is its similarity with a traditional HP: the HP/ORC unit only requires the addition of a pump and four-way valves compared to a simple HP, which can be achieved at a low cost. A methodology for the optimal sizing and design of the system is proposed, based on the optimization of both continuous parameters such as heat exchanger size or discrete variables such as working fluid. The methodology is based on yearly simulations, aimed at optimizing the system performance (the net yearly power generation) over its whole operating range instead of just nominal sizing operating conditions. The simulations allow evaluating the amount of thermal energy and electricity generated throughout the year, yielding a net electric power output of 3496 kWh throughout the year.

scholarly journals Applying Vision-Based Pose Estimation in a Telerehabilitation Application

2021 ◽  
Vol 11 (19) ◽  
pp. 9132
Author(s):  
Francisca Rosique ◽  
Fernando Losilla ◽  
Pedro J. Navarro
Keyword(s):  
Augmented Reality ◽  
Range Of Motion ◽  
Pose Estimation ◽  
Low Cost ◽  
The Other ◽  
Final Conclusion ◽  
Pose Detection ◽  
Human Pose ◽  
Positive Results ◽  
The Web

In this paper, an augmented reality mirror application using vision-based human pose detection based on vision-based pose detection called ExerCam is presented. ExerCam does not need any special controllers or sensors for its operation, as it works with a simple RGB camera (webcam type), which makes the application totally accessible and low cost. This application also has a system for managing patients, tasks and games via the web, with which a therapist can manage their patients in a ubiquitous and totally remote way. As a final conclusion of the article, it can be inferred that the application developed is viable as a telerehabilitation tool, as it has the resource of a task mode for the calculation of the range of motion (ROM) and, on the other hand, a game mode to encourage patients to improve their performance during the therapy, with positive results obtained in this aspect.

scholarly journals Filling the Joints: Completion and Recovery of Incomplete 3D Human Poses

Technologies ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 97
Author(s):  
Dennis Bautembach ◽  
Iason Oikonomidis ◽  
Antonis Argyros
Keyword(s):  
Pose Estimation ◽  
Gradient Descent ◽  
Estimation Error ◽  
Matrix Completion ◽  
Human Pose Estimation ◽  
Estimation Algorithms ◽  
Post Process ◽  
Recovery Techniques ◽  
Human Pose ◽  
3D Human Pose Estimation

We present a comparative study of three matrix completion and recovery techniques based on matrix inversion, gradient descent, and Lagrange multipliers, applied to the problem of human pose estimation. 3D human pose estimation algorithms may exhibit noise or may completely fail to provide estimates for some joints. A post-process is often employed to recover the missing joints’ locations from the remaining ones, typically by enforcing kinematic constraints or by using a prior learned from a database of natural poses. Matrix completion and recovery techniques fall into the latter category and operate by filling-in missing entries of a matrix whose available/non-missing entries may be additionally corrupted by noise. We compare the performance of three such techniques in terms of the estimation error of their output as well as their runtime, in a series of simulated and real-world experiments. We conclude by recommending use cases for each of the compared techniques.

scholarly journals Can Markerless Pose Estimation Algorithms Estimate 3D Mass Centre Positions and Velocities during Linear Sprinting Activities?

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2889
Author(s):  
Laurie Needham ◽  
Murray Evans ◽  
Darren P. Cosker ◽  
Steffi L. Colyer
Keyword(s):  
Pose Estimation ◽  
Poor Performance ◽  
Estimation Algorithm ◽  
Specific Data ◽  
Estimation Algorithms ◽  
Mass Centre ◽  
Kalman Smoothing ◽  
The Mean ◽  
Human Pose ◽  
Signal Processing Methods

The ability to accurately and non-invasively measure 3D mass centre positions and their derivatives can provide rich insight into the physical demands of sports training and competition. This study examines a method for non-invasively measuring mass centre velocities using markerless human pose estimation and Kalman smoothing. Marker (Qualysis) and markerless (OpenPose) motion capture data were captured synchronously for sprinting and skeleton push starts. Mass centre positions and velocities derived from raw markerless pose estimation data contained large errors for both sprinting and skeleton pushing (mean ± SD = 0.127 ± 0.943 and −0.197 ± 1.549 m·s−1, respectively). Signal processing methods such as Kalman smoothing substantially reduced the mean error (±SD) in horizontal mass centre velocities (0.041 ± 0.257 m·s−1) during sprinting but the precision remained poor. Applying pose estimation to activities which exhibit unusual body poses (e.g., skeleton pushing) appears to elicit more erroneous results due to poor performance of the pose estimation algorithm. Researchers and practitioners should apply these methods with caution to activities beyond sprinting as pose estimation algorithms may not generalise well to the activity of interest. Retraining the model using activity specific data to produce more specialised networks is therefore recommended.

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