Low-cost Motion Capture System Research Articles

Concurrent Validity of Measures of Upper Extremity Function Derived from Videogame-Based Motion Capture for Children with Hemiplegia.

Objective: Pediatric hemiplegia is associated with wide-ranging deficits in arm and hand motor function, neg-atively impacting participation in daily occupations and quality of life. This study investigated whether performance measures generated during therapy videogame play by children with hemiplegia can be valid indicators of upper extremity motor function. Materials and Methods: Ten children with hemiplegia used a custom therapy game system alternatively using their affected and non-affected hand to provide motion capture data that spans a wide range of motor function status. The children also completed a series of standardized outcome measure assessments with each hand, including the Quality of Upper Extremity Skills Test, the Jebsen Taylor Hand Function Test, and the Wolf Motor Function Test. Results: Statistical analysis using the nonparametric Spearman rank correlation revealed high and significant correlation between videogame-derived motion capture measures, characterizing the speed and smoothness of movements, and the standardized outcome measure assessments. Conclusion: The results suggest that a low-cost motion capture system can be used to monitor a child's motor function status and progress during a therapy program.

Validity and reliability of NOTCH® inertial sensors for measuring elbow joint angle during tennis forehand at different sampling frequencies

Portable and low-cost motion capture systems are gaining importance for biomechanical analysis. The aim was to determine the concurrent validity and reliability of the NOTCH® inertial sensors to measure the elbow angle during tennis forehand at different sampling frequencies (100, 250 and 500 Hz), using an optical capture system with sub-millimetre accuracy as a reference. 15 competitive players performed forehands wearing NOTCH and an upper body marker-set and the signals from both systems were adjusted and synchronized. The error magnitude was tolerable (5-10°) for all joint-axis and sampling frequencies, increasing significantly at 100 Hz for the flexion–extension and pronation-supination angles (p = 0.002 and 0.023; Cohen d > 0.8). Concordance correlation coefficient was very large (0.7–0.9) in all cases. The within-subject error variation between the test–retest did not show significant differences (p > 0.05). NOTCH® is a valid, reliable and portable alternative to measure elbow angles during tennis forehand.

Event-Based Motion Capture System for Online Multi-Quadrotor Localization and Tracking.

Motion capture systems are crucial in developing multi-quadrotor systems due to their ability to provide fast and accurate ground truth measurements for tracking and control. This paper presents the implementation details and experimental validation of a relatively low-cost motion-capture system for multi-quadrotor motion planning using an event camera. The real-time, multi-quadrotor detection and tracking tasks are performed using a deep learning network You-Only-Look-Once (YOLOv5) and a k-dimensional (k-d) tree, respectively. An optimization-based decentralized motion planning algorithm is implemented to demonstrate the effectiveness of this motion capture system. Extensive experimental evaluations were performed to (1) compare the performance of four deep-learning algorithms for high-speed multi-quadrotor detection on event-based data, (2) study precision, recall, and F1 scores as functions of lighting conditions and camera motion, and (3) investigate the scalability of this system as a function of the number of quadrotors flying in the arena. Comparative analysis of the deep learning algorithms on a consumer-grade GPU demonstrates a 4.8× to 12× sampling/inference rate advantage that YOLOv5 provides over representative one- and two-stage detectors and a 1.14× advantage over YOLOv4. In terms of precision and recall, YOLOv5 performed 15% to 18% and 27% to 41% better than representative state-of-the-art deep learning networks. Graceful detection and tracking performance degradation was observed in the face of progressively darker ambient light conditions. Despite severe camera motion, YOLOv5 precision and recall values of 94% and 98% were achieved, respectively. Finally, experiments involving up to six indoor quadrotors demonstrated the scalability of this approach. This paper also presents the first open-source event camera dataset in the literature, featuring over 10,000 fully annotated images of multiple quadrotors operating in indoor and outdoor environments.

Feasibility of a virtual reality-based exercise intervention and low-cost motion tracking method for estimation of motor proficiency in youth with autism spectrum disorder

BackgroundMotor impairment is widely acknowledged as a core feature in children with autism spectrum disorder (ASD), which can affect adaptive behavior and increase severity of symptoms. Low-cost motion capture and virtual reality (VR) game technologies hold a great deal of promise for providing personalized approaches to motor intervention in ASD. The present study explored the feasibility, acceptability and potential efficacy of a custom-designed VR game-based intervention (GaitWayXR™) for improving gross motor skills in youth with ASD.MethodsTen children and adolescents (10–17 years) completed six, 20-min VR-based motor training sessions over 2 weeks while whole-body movement was tracked with a low-cost motion capture system. We developed a methodology for using motion tracking data to quantify whole-body movement in terms of efficiency, synchrony and symmetry. We then studied the relationships of the above quantities with standardized measures of motor skill and cognitive flexibility.ResultsOur results supported our presumption that the VR intervention is safe, with no adverse events and very few minor to moderate side-effects, while a large proportion of parents said they would use the VR game at home, the most prohibitive reasons for adopting the system for home therapy were cost and space. Although there was little evidence of any benefits of the GaitWayXR™ intervention in improving gross motor skills, we showed several positive correlations between the standardized measures of gross motor skills in ASD and our measures of efficiency, symmetry and synchrony from low-cost motion capture.ConclusionsThese findings, though preliminary and limited by small sample size, suggest that low-cost motion capture of children with ASD is feasible with movement exercises in a VR-based game environment. Based on these preliminary findings, we recommend conducting larger-scale studies with methods for improving adherence to VR gaming interventions over longer periods.

Distinct Feature Extraction for Video-Based Gait Phase Classification

Recent advances in image acquisition and analysis have resulted in disruptive innovation in physical rehabilitation systems facilitating cost-effective, portable, video-based gait assessment. While these inexpensive motion capture systems, suitable for home rehabilitation, do not generally provide accurate kinematics measurements on their own, image processing algorithms ensure gait analysis that is accurate enough for rehabilitation programs. This paper proposes high-accuracy classification of gait phases and muscle actions, using readings from low-cost motion capture systems. First, 12 gait parameters, drawn from the medical literature, are defined to characterize gait patterns. These proposed parameters are then used as input to our proposed multi-channel time-series classification and gait phase reconstruction methods. Proposed methods fully utilize temporal information of gait parameters, thus improving the final classification accuracy. The validation, conducted using 126 experiments, with 6 healthy volunteers and 9 stroke survivors with manually-labelled gait phases, achieves state-of-art classification accuracy of gait phase with lower computational complexity compared to previous solutions.1

A Low-Cost Motion Capture System for Small-Scale Wave Energy Device Tank Testing

The measurement of the motion of a small-scale wave energy device during wave tank tests is important for the evaluation of its response to waves and the assessment of power production. Usually, the motion of a small-scale wave energy converter (WEC) is measured using an optical motion tracking system with high precision and sampling rate. However, the cost for an optical motion tracking system can be considerably high and, therefore, the overall cost for tank testing is increased. This paper proposes a low-cost capture system composed of an inertial measurement unit and ultrasound sensors. The measurements from the ultrasound sensors are combined optimally with the measurements from the inertial measurement unit through an extended Kalman filter (EKF) in order to obtain an accurate estimation of the motion of a WEC.

Marker-based human pose tracking using adaptive annealed particle swarm optimization with search space partitioning

Pose estimation and tracking of an articulated structure based on data from multiple cameras has seen numerous applications in recent years. In this paper, a marker-based human pose tracking algorithm from multi view video sequences is proposed. The purpose of the proposed algorithm is to present a low cost motion capture system that can be used as an alternative to high cost available commercial human motion capture systems. The problem is defined as the optimization of 45 parameters which define body pose model and is solved using a modified version of particle swarm optimization (PSO) algorithm. The objective of this optimization is to maximize a fitness function which formulates how much the body model matches with 2D marker coordinates in video frames. A sampling covariance matrix is used in the first part of the velocity equation of PSO and is annealed with iterations. The sampling covariance matrix is computed adaptively, based on variance of parameters in the swarm. One of the concerns in this algorithm is the high number of parameters to define the model of body pose. To tackle this problem, we partition the optimization state space into six stages that exploit the hierarchical structure of the skeletal model. The first stage optimizes the six parameters that define the global orientation and position of the body. Other stages relate to optimization of right and left hand, right and left leg and head orientation. In the proposed partitioning method previously optimized parameters are allowed some variation in each step that is called soft partitioning. Experimental results on Pose Estimation and Action Recognition (PEAR) database indicate that the proposed algorithm achieves lower estimation error in tracking human motion compared with Annealed Particle Filter (APF) and Parametric Annealing (PA) methods.

Low-cost 3D motion capture system using passive optical markers and monocular vision

This paper presents a low-cost 3D motion capture system using a single camera and passive optical markers. 3D motion capture systems usually demand multiple cameras. Thus, those systems require high-cost hardware devices and large computing power. However, such high-cost systems prevent personal or mobile systems using 3D motion capturing. To overcome this problem, we propose a low-cost motion capture system. The structure of our system is based on a single camera and passive optical markers, which enables the system to be low-cost. As the components of the proposed systems, we introduce 2D optical marker recognition, marker size recognition, and mapping for 3D data extraction. To show the effectiveness and efficiency, we conduct preliminary experiments in terms of accuracy and time-complexity. Experiment results indicate that the proposed system provides 3D motion capturing using a single camera enough to utilize it in 3D interaction.

Measuring Motion Capture Data Quality for Data Driven Human Motion Synthesis

In automotive industry, market demands shorter life cycles and individualized products. For manual assembly, this trend leads to more frequent planning of an ever increasing number of process variants. In order to ensure planning quality, virtual verification of manual production is crucial for efficient process optimization. However, virtual verification is not established in practice because available simulation tools require prohibitive manual modeling effort for human motions of acceptable quality. For automating the modeling process, data driven motion synthesis approaches are promising candidates that –however– require high quality input data for acceptable synthesis results. Therefore, objective motion capture data quality measures for data driven human motion synthesis are sought.This work proposes and tests a principal component analysis (PCA) and a Shannon entropy based quality measure. Both measures evaluate post-processed data and thus consider motion capture hardware in combination with a post-processing tool chain.The measures are tested for selectivity and validity using two low cost and two high cost motion capture systems. They differ in selectivity for high and low cost motion capture systems. Both measures correctly predict motion synthesis quality in tests with treadmill walking. Therefore, they can be employed for testing if a motion capture system is suitable for data driven motion synthesis that relies on PCA for input dimension reduction. Further research on robustness of the measures against motion variation is proposed.

Enhanced Hand Function Assessment Using Pressure

Hand injuries affect our ability to manipulate objects, and existing tools are useful in determining if patients have difficulty performing tasks. The Jebsen–Taylor Hand Function Test (JTT), is a widely used test for functional motor assessment that reflects activities of daily living. But current tools, including the JTT, do not measure compensatory behavior. This study aimed to investigate the use of a low cost motion capture system to evaluate compensatory behavior of Rheumatoid Arthritis (RA) patents during a JTT assessment. Youth and Older control groups were used as a basis for comparison of RA patient performance. Results of the JTT itself illustrated how RA patient can perform some tasks more quickly than control groups. However, the JTT only evaluates completion times of each task, not how the participant performed the activity. Including Range of Motion measurements during this JTT assessment indicated that there are significant differences in the Range of Motion of RA patients in comparison to both young and older control groups. For example, hand displacement results for the RA participants were significantly greater than those of the older control. This demonstrates that the integration of motion capture analysis with the JTT gives a more comprehensive assessment of hand function.

Samba: a real-time motion capture system using wireless camera sensor networks.

There is a growing interest in 3D content following the recent developments in 3D movies, 3D TVs and 3D smartphones. However, 3D content creation is still dominated by professionals, due to the high cost of 3D motion capture instruments. The availability of a low-cost motion capture system will promote 3D content generation by general users and accelerate the growth of the 3D market. In this paper, we describe the design and implementation of a real-time motion capture system based on a portable low-cost wireless camera sensor network. The proposed system performs motion capture based on the data-driven 3D human pose reconstruction method to reduce the computation time and to improve the 3D reconstruction accuracy. The system can reconstruct accurate 3D full-body poses at 16 frames per second using only eight markers on the subject's body. The performance of the motion capture system is evaluated extensively in experiments.

Low-cost motion capture systems in practice

Optical motion capture technology is a very useful tool for collecting, analysing and quantifying human movements. Over the last decades, a variety of new systems have been developed with an enormo...

Next-Generation Low-Cost Motion Capture Systems Can Provide Comparable Spatial Accuracy to High-End Systems

The objective quantification of three-dimensional kinematics during different functional and occupational tasks is now more in demand than ever. The introduction of new generation of low-cost passive motion capture systems from a number of manufacturers has made this technology accessible for teaching, clinical practice and in small/medium industry. Despite the attractive nature of these systems, their accuracy remains unproved in independent tests. We assessed static linear accuracy, dynamic linear accuracy and compared gait kinematics from a Vicon MX20 system to a Natural Point OptiTrack system. In all experiments data were sampled simultaneously. We identified both systems perform excellently in linear accuracy tests with absolute errors not exceeding 1%. In gait data there was again strong agreement between the two systems in sagittal and coronal plane kinematics. Transverse plane kinematics differed by up to 3° at the knee and hip, which we attributed to the impact of soft tissue artifact accelerations on the data. We suggest that low-cost systems are comparably accurate to their high-end competitors and offer a platform with accuracy acceptable in research for laboratories with a limited budget.

Wii remote–based low-cost motion capture for automated assembly simulation

This paper describes the development of a Wii remote (Wiimote)---based low-cost motion capture system and demonstrates its application for automated assembly simulation. Multiple Wiimotes are used to form a vision system to perform motion capture in 3D space. A hybrid algorithm for calibrating a multi-camera stereo vision system has been developed based on Zhang's and Svoboda's calibration algorithms. This hybrid algorithm has been evaluated and shown accuracy improvement over Svoboda's algorithm for motion capture with multiple cameras. The captured motion data are used to automatically generate an assembly simulation of objects represented by CAD models in real time. The Wiimote-based motion capture system is practically attractive because it is inexpensive, wireless, and easily portable. Application examples have been developed for a single vision system with two Wiimotes to track the assembly of a microsatellite prototype frame and for an integrated vision system with four Wiimotes to track the assembly of a bookshelf.

Real-Time Automated Simulation Generation Based on CAD Modeling and Motion Capture

This paper describes the design, implementation and evaluation of low-cost motion capture systems with the support of hardware-software interfaces for real-time automated simulation generation using CAD models. To capture the motions of 3D objects in a physical environment, stereo vision systems are developed using two kinds of inexpensive, off-the-shelf cameras: 1) Wii Remote (Wiimote) and 2) Firefly MV. In this paper, we present the system’s architectural design, the software developed to implement and integrate several existing algorithms, and effective approaches to addressing practical issues such as camera calibration, object identification, and registration between the real-world and virtual-world coordinate frames. Application examples have been developed to demonstrate real-time automated generation of simulations of assembly operations using Siemens NX software for CAD modeling.

A low-cost system for capturing and analyzing the motion of aquatic organisms

Capture and quantitative analysis of the motion of organisms is a powerful tool that can be used in diverse biological fields including physiology, behavior, kinematics, and ecology. A number of high-end commercial motion capture and analysis systems that offer a wide array of features and image capture and analysis capabilities are available. However, few, if any, such systems are low cost and could be used for projects with small budgets or by researchers interested in collecting pilot data before upgrading to an expensive motion capture and analysis system. Our paper describes a low-cost (<$US1000, not including the cost of the computer) motion capture and analysis system that simultaneously captures live video from 5 digital video cameras (Unibrain Fire-i FireWire [IEEE 1394]) using standard security camera software (SecuritySpy 1.3.1). The video analyses are carried out using a combination of standard spreadsheet software (Microsoft Excel) and a freeware image-analysis program (NIH Image J), with one of the available particle-tracking plugins. Tests using New Zealand mudsnails (Potamopyrgus antipodarum) demonstrated that the system could record and accurately track the movement of small (4 mm) targets. Recommendations for designing similar project-specific systems are provided.

MOCA: A Low-Power, Low-Cost Motion Capture System Based on Integrated Accelerometers

Human-computer interaction (HCI) and virtual reality applications pose the challenge of enabling real-time interfaces for natural interaction. Gesture recognition based on body-mounted accelerometers has been proposed as a viable solution to translate patterns of movements that are associated with user commands, thus substituting point-and-click methods or other cumbersome input devices. On the other hand, cost and power constraints make the implementation of a natural and efficient interface suitable for consumer applications a critical task. Even though several gesture recognition solutions exist, their use in HCI context has been poorly characterized. For this reason, in this paper, we consider a low-cost/low-power wearable motion tracking system based on integrated accelerometers called motion capture with accelerometers (MOCA) that we evaluated for navigation in virtual spaces. Recognition is based on a geometric algorithm that enables efficient and robust detection of rotational movements. Our objective is to demonstrate that such a low-cost and a low-power implementation is suitable for HCI applications. To this purpose, we characterized the system from both a quantitative point of view and a qualitative point of view. First, we performed static and dynamic assessment of movement recognition accuracy. Second, we evaluated the effectiveness of user experience using a 3D game application as a test bed.

Design and evaluation of a new three-dimensional motion capture system based on video

This technical note describes the development and evaluation of a low-cost motion capture and analysis system (SOMCAM3D). From diverse video sequences obtained by commercial cameras, synchronized by a certain event, and calibrated from a calibration process, the system is able to reconstruct three-dimensional positions in a reference system defined by the calibration process. From these positions, a wide variety of kinematic variables could be calculated. This system aims to analyze movement in applications of gait analysis, sports, animals, ergonomics, robotics, etc. It is designed to work with low-cost equipment (commercial cameras, computers, etc.). Also it allows us to work in 2D, outdoors, and without marks. Finally, this technical note includes a study of how precise and accurate this system is.