Results 31 to 40 of about 6,920 (193)

Grimage

open access: yesACM SIGGRAPH 2007 emerging technologies, 2007
Grimage glues multi-camera 3D modeling, physical simulation and parallel execution for a new immersive experience. Put your hands or any object into the interaction space. It is instantaneously modeled in 3D and injected into a virtual world populated with solid and soft objects. Push them, catch them and squeeze them.
Allard, Jérémie   +4 more
openaire   +2 more sources

Monocular 3D Human Pose Markerless Systems for Gait Assessment

open access: yesBioengineering, 2023
Gait analysis plays an important role in the fields of healthcare and sports sciences. Conventional gait analysis relies on costly equipment such as optical motion capture cameras and wearable sensors, some of which require trained assessors for data ...
Xuqi Zhu   +6 more
doaj   +1 more source

Markerless Human Motion Analysis

open access: yes, 2022
Measuring and understanding human motion is crucial in several domains, ranging from neuroscience, to rehabilitation and sports biomechanics. Quantitative information about human motion is fundamental to study how our Central Nervous System controls and organizes movements to functionally evaluate motor performance and deficits.
openaire   +2 more sources

Automated Gait Analysis Based on a Marker-Free Pose Estimation Model

open access: yesSensors, 2023
Gait analysis is an essential tool for detecting biomechanical irregularities, designing personalized rehabilitation plans, and enhancing athletic performance.
Chang Soon Tony Hii   +7 more
doaj   +1 more source

A SWOT Analysis of Portable and Low-Cost Markerless Motion Capture Systems to Assess Lower-Limb Musculoskeletal Kinematics in Sport

open access: yesFrontiers in Sports and Active Living, 2022
Markerless motion capture systems are promising for the assessment of movement in more real world research and clinical settings. While the technology has come a long way in the last 20 years, it is important for researchers and clinicians to understand ...
Cortney Armitano-Lago   +2 more
doaj   +1 more source

Level of Agreement between the MotionMetrix System and an Optoelectronic Motion Capture System for Walking and Running Gait Measurements

open access: yesSensors, 2023
Markerless motion capture systems (MCS) have been developed as an alternative solution to overcome the limitations of 3D MCS as they provide a more practical and efficient setup process given, among other factors, the lack of sensors attached to the body.
Diego Jaén-Carrillo   +6 more
doaj   +1 more source

Markerless Augmented Advertising for Sports Videos [PDF]

open access: yes, 2019
Markerless augmented reality can be a challenging computer vision task, especially in live broadcast settings and in the absence of information related to the video capture such as the intrinsic camera parameters. This typically requires the assistance of a skilled artist, along with the use of advanced video editing tools in a post-production ...
Hallee E. Wong   +6 more
openaire   +2 more sources

Validity of AI-Based Gait Analysis for Simultaneous Measurement of Bilateral Lower Limb Kinematics Using a Single Video Camera

open access: yesSensors, 2023
Accuracy validation of gait analysis using pose estimation with artificial intelligence (AI) remains inadequate, particularly in objective assessments of absolute error and similarity of waveform patterns.
Takumi Ino   +6 more
doaj   +1 more source

Characterization of Infants’ General Movements Using a Commercial RGB-Depth Sensor and a Deep Neural Network Tracking Processing Tool: An Exploratory Study

open access: yesSensors, 2022
Cerebral palsy, the most common childhood neuromotor disorder, is often diagnosed through visual assessment of general movements (GM) in infancy. This skill requires extensive training and is thus difficult to implement on a large scale.
Diletta Balta   +8 more
doaj   +1 more source

Robotic Control for Human–Robot Collaborative Assembly Based on Digital Human Model and Reinforcement Learning

open access: yesAdvanced Robotics Research, EarlyView.
This work presents a robotic control method for human–robot collaborative assembly based on a biomechanics‐constrained digital human model. Reinforcement learning is used to generate physiologically plausible human motion trajectories, which are integrated into a virtual environment for robot control learning.
Bitao Yao   +4 more
wiley   +1 more source

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