Human Motion Estimation Application for the Marker-Based Motion Capture Systems

Motion capture system can transform motion information of human body into digital data for analysis and processing in computer or cyberspace. It is an important medium to connect physical space and cyberspace. Aiming at the problems of high cost and high power consumption of existing inertial motion capture systems, this paper proposes a wearable wireless motion capture system based on nine-axis inertial sensor and Bluetooth Low Energy technology. The system can capture the motion trajectory of human body and realize real-time human motion display and data storage in computer. An explicit complementary filtering algorithm based on proportional integral model is used in the process of character motion update. Motion capture experiments are carried out on a human body and results show that the system has sound dynamic performance with the error less than 2 degrees and can achieve efficient human motion capture. The sampling frequency of the system reaches 125Hz and the working time is up to 10 hours. The system can serve as the basis for developing affordable human motion capture solutions that require long-term testing without precise kinematic analysis.

Manual work tasks involving awkward postures, repetitive motion, and physical exer-tion contribute significantly to musculoskeletal disorders (MSDs) in industrial settings. Marker-based (Mbased) motion capture systems (MoCaps) offer precise, quantitative insights into human movement, enabling more accurate ergonomic risk assessments. This review aims to evaluate the applications, effectiveness, opportunities, and limita-tions of optical Mbased MoCaps in the ergonomic analysis of industrial and physically intensive workplace activities. A systematic literature search was conducted for peer-reviewed studies published between 2015 and 2025 from four different databases. Articles were included if they applied optical Mbased MoCaps to assess ergonomic risks in industrial or manual task contexts and reported relevant kinematic or biome-chanical metrics. Only twelve studies satisfied the inclusion criteria. Mbased MoCaps were used in several human tasks, including manual material handling, overhead work, surgery, and assembly line activities and promising results were achieved. De-spite their accuracy and analytical value, Mbased systems faced limitations. However, Mbased MoCaps remain highly effective for ergonomic assessment in controlled in-dustrial settings. This review contributes by combining existing evidence, identifying underexplored challenges, and providing a valuable foundation for researchers and practitioners seeking to optimise ergonomic assessments and workplace interventions using advanced motion capture tools.

Most common methods for proper 3D capture of human movement require a laboratory environment and setting of markers, sensors accessories body segments. However, the high cost of these equipments is a limiting factor in our work environment. Motion capture systems like Microsoft's Kinect TM camera present alternative new approaches to motion capture technology. This paper proposes the design and implementation of a marker-less motion capture system of low cost, using the Microsofts Kinect camera. The system allows the capture of 20 joint points at 30 frames per second (fps) and exports the Cartesian coordinates (x, y, z) of each joint point for further analysis. Furthermore, the system plots in 3D the human skeleton tracking. The motion capture system was tested to imitate human movement with a planar robot of 9 degrees of freedom (dof), another application was to generate trajectories for a planar biped robot and the last application described is the control of final effector of virtual Puma robot from motion capture. Its data presents less accuracy and a lower rate than market equipment. On the other hand, the low-cost of Kinect sensor and easy installation to use in comparison with traditional methods, show it as an excellent alternative for robotic applications that do not require much accuracy.

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The motion of a real object model is reconstructed through measurements of the position, direction, and angle of moving objects in 3D space in a process called “motion capture.” With the development of inertial sensing technology, motion capture systems that are based on inertial sensing have become a research hot spot. However, the solution of motion attitude remains a challenge that restricts the rapid development of motion capture systems. In this study, a human motion capture system based on inertial sensors is developed, and the real-time movement of a human model controlled by real people’s movement is achieved. According to the features of the system of human motion capture and reappearance, a hierarchical modeling approach based on a 3D human body model is proposed. The method collects articular movement data on the basis of rigid body dynamics through a miniature sensor network, controls the human skeleton model, and reproduces human posture according to the features of human articular movement. Finally, the feasibility of the system is validated by testing of system properties via capture of continuous dynamic movement. Experiment results show that the scheme utilizes a real-time sensor network-driven human skeleton model to achieve the accurate reproduction of human motion state. The system also has good application value.

Microsoft Kinect sensors are being widely used as low-cost marker-less motion capture systems in various kinematic studies. Previous studies investigated the reliability and validity of Microsoft Kinect sensors by employing marker-based motion capture systems. Both systems employ IR emitters and detectors to track human posture and physical activities. This paper hypothesizes that the motion capture systems may interfere with Microsoft Kinect One sensor and influence the sensor’s performance in tracking the skeleton. Hence, this study investigated the impact of a motion capture system on the Microsoft Kinect v2 skeleton algorithm using a mannequin in the presence of eight Qualisys Oqus 300/310 cameras and retroreflective markers. It was found that the motion capture system introduced a destructive impact on the Microsoft Kinect v2 skeleton tracking algorithm. In addition, it was observed that retroreflective markers placed near the joints caused the Microsoft Kinect v2 to give an incorrect reading of estimate the joint position. The motion capture cameras thus caused a timevarying distortion of the Microsoft Kinect estimate of the joint position. It is believed that the inference can be reduced by decreasing the number of markers and avoiding facing the motion capture cameras in sight of Microsoft Kinect v2.

Kinematic motion analysis is widely used in health-care, sports medicine, robotics, biomechanics, sports science, etc. Motion capture systems are essential for motion analysis. There are three types of motion capture systems: marker-based capture, vision-based capture, and volumetric capture. Marker-based motion capture systems can achieve fairly accurate results but attaching markers to a body is inconvenient and time-consuming. Vision-based, marker-less motion capture systems are more desirable because of their non-intrusiveness and flexibility. Volumetric capture is a newer and more advanced marker-less motion capture system that can reconstruct realistic, full-body, animated 3D character models. But volumetric capture has rarely been used for motion analysis because volumetric motion data presents new challenges. We propose a new method for conducting kinematic motion analysis using volumetric capture data. This method consists of a three-stage pipeline. First, the motion is captured by a volumetric capture system. Then the volumetric capture data is processed using the Iterative Closest Points (ICP) algorithm to generate virtual markers that track the motion. Third, the motion tracking data is imported into the biomechanical analysis tool OpenSim for kinematic motion analysis. Our motion analysis method enables users to apply numerical motion analysis to the skeleton model in OpenSim while also studying the full-body, animated 3D model from different angles. It has the potential to provide more detailed and in-depth motion analysis for areas such as healthcare, sports science, and biomechanics.

In this paper, a nonlinear optimization solver is proposed to establish the human upper body motion capture system, including the orientation of each bone joint and the global position of the hip bone. 5 IMUs and one Kinect are used, the rotation data and acceleration data of IMUs, Kinect skeleton position data and human pose prior are fused in a non-linear optimization way. The method proposed in this paper combines the characteristics of Kinect data with high accuracy and IMU data with stability, which can make up for the accuracy of human pose capture in dynamic, magnetically disturbed and occlusion conditions. In order to verify the practicability of the human motion capture system proposed in this paper, the system is firstly applied to real-time human motion capture, then a simple human-robot collaboration (HRC) case is implemented, which is very common in modern collaborative medical field. The experimental results show that the proposed human motion capture system can achieve stable human motion measurement, which can meet the needs of HRC scenarios.

The resource-intensive nature of traditional marker-based motion capture systems limits opportunities for quantitative motion analysis. However, the advancement of markerless motion capture technology yields tremendous promise for accessible kinematic analyses beyond conventional research settings. This work compared the lower limb kinematics measured by a neural network-driven markerless motion capture system to those from a standard marker-based motion capture system during squat, hip hinge, and reverse lunge tasks. Fourteen adults performed three repetitions of each movement while being recorded simultaneously by two iPads and 17 infrared optical motion capture cameras. The mean peak cross-correlation values indicated high agreement for knee and hip flexion (>0.95) and poor agreement for hip adduction, knee varus, and internal rotation (<0.49) for all tasks; agreement for hip internal rotation and ankle flexion was task dependent (0.27–0.97). The average root mean square error indicated joint-specific offsets between systems, as values ranged from 6.43° to 12.32° for the knee, 11.25° to 17.35° for the hip, and 21.51° to 25.67° for the ankle. These findings suggest that, while the markerless system demonstrates the ability to capture gross motor patterns in the sagittal plane, further refinement of the underlying models is needed to improve the validity of the system’s measurements.

Markerless motion capture (MMC) shows promise for examining human movement across many domains because of its nonintrusive nature and negligible per-subject setup time. However, published MMC systems typically require specific hardware. This validation study compared lower-body joint kinematics from Ergomechanic, a hardware-agnostic pose model-based MMC system, to an established marker-based motion capture (MBMC) system. Static trial data from eighteen people were used to register MMC keypoints within a widely used musculoskeletal model. The registered model was used to calculate joint kinematics for static pose, squatting, and walking trials. A novel perturbation analysis estimated the contributions to differences in MBMC and MMC approaches to measurement disparities. Very good (0.87-1.0) correlations between the systems were calculated for ankle, knee, and hip flexion-extension angles. Good (0.70-0.86) correlations were found for hip external-internal and abduction-adduction. Pelvis and lumbar spine angles had a wider range of correlation results (-0.06 to 0.95), likely due to the few MMC keypoints in these body regions. Relative contributions from the perturbation analysis were (i) 75% from variations in MMC data relative to MBMC; (ii) 8% because MMC keypoints (26) < MBMC markers (67); and (iii) 3% from differences in musculoskeletal model scaling. These results validate Ergomechanic for leg kinematics during standing, walking, and squatting. Further, they suggest system improvements for pelvis and torso kinematics and provide new insights into the sources of known differences between MMC and MBMC measurements.

Motion capture systems have become a common utility in a wide variety of fields and rely on various underlying principles to estimate the pose of one or multiple mobile objects. The most popular applications for pose estimation systems can be found in the entertainment sector where it is used for computer animation purposes or in sports for performance measurements. These systems present millimeter accuracy but also come with a high cost. We wish to reduce the cost of such a motion capture system while retaining the high accuracy by using an embedded ultrasonic measurement system. The proposed system comprises of one or multiple mobile nodes containing a small microphone array (at least 3 microphones) which receives multiple simultaneously emitted coded and broadband ultrasonic transmissions from at least three fixed transmitters. Using this technique, the complete pose (six degrees-of-freedom) of every mobile sensor can be estimated while retaining the low-cost aspect of the system. Motion capture systems which combine low cost with high accuracy open many new fields of application such as physiotherapy practices.

This paper describes a novel optical motion capture technique. Motion capture is a technique for producing a 3-D model of a human motion or action. Unlike the existent methods of motion capture, the presented technique neither performs camera calibration nor employs markers. Instead it makes use of a motion database, and, by retrieving the database, it recognizes the unknown motion of an observed human and it reproduces the motion by use of a digital human model. This new motion capture system may provide a simple and easy-to-use motion capture system. Performance of the proposed technique is shown experimentally.

Web services are designed to provide interoperability among diverse applications. Nowadays, a growing number of commercial enterprises are redefining their business processes under this technology. The platform and language independence of Web services programming interfaces enables the seamless integration of heterogeneous Web based-systems. A business processes integration and monitoring system (BPIMS) has been developed to automate, integrate and monitor many of the enterprise business processes described as Web services (WS). Our main contribution consists in a brokering architecture to automate many of the enterprise business processes without resorting to large investments in software development and deployment. Our brokering architecture is based on a four-layer design that meets crucial design aspects like abstraction, scalability, interoperability and greater dynamism.

In this chapter, we have presented an overview of the functions and specifications for cognitive radio RF platforms. Typically, these combine analog and digital components and signalprocessing systems and offer the user a wide range of options trading off cost, performance, the development effort required, and required computational support. RFICs are attractive for simple applications typically requiring channelized communications with FSK-based waveforms, while SDRs offer power and flexible performance in return for commensurate investment in software development.

We introduce the Theoretical Astrophysical Observatory (TAO), an online virtual laboratory that houses mock observations of galaxy survey data. Such mocks have become an integral part of the modern analysis pipeline. However, building them requires expert knowledge of galaxy modeling and simulation techniques, significant investment in software development, and access to high performance computing. These requirements make it difficult for a small research team or individual to quickly build a mock catalog suited to their needs. To address this TAO offers access to multiple cosmological simulations and semi-analytic galaxy formation models from an intuitive and clean web interface. Results can be funnelled through science modules and sent to a dedicated supercomputer for further processing and manipulation. These modules include the ability to (1) construct custom observer light cones from the simulation data cubes; (2) generate the stellar emission from star formation histories, apply dust extinction, and compute absolute and/or apparent magnitudes; and (3) produce mock images of the sky. All of TAO’s features can be accessed without any programming requirements. The modular nature of TAO opens it up for further expansion in the future.