Low-cost Motion Capture Research Articles

Motion and Audio Capture of Lectures for Use in Digital Game Based Learning Environments

This paper outlines the idea and methodology of using low cost motion and audio capture to present content by an educator during a theory based lecture with the intention of embedding and augmenting the recorded lecture session in a virtual space. Released in 2010, the Microsoft KinectTM removed the development barrier for software developers and educational professionals interested in developing natural user interface (NUI) and voice user interfaces (VUI) learning applications. Traditional online learning methods replicate current passive learning methods such as theory lectures which are recorded and offered online via streamed video through packages such as Moodle and Adobe ConnectTM. These more established distance teaching approaches do not cater for new methods for learning or a new game-orientated student cohort, despite the availability of technology which excels at interactivity and requires a user’s active attention for successful interactions. Initial demonstrations of the augmented lectures to students illustrated an acceptance of the technology for use in teaching and learning and the augmented lectures were received positively. These augmented lectures could foster a feeling of inclusiveness and social inclusion for the online learner which could be absent from video based alternatives.

HeartHealth: A Cardiovascular Disease Home-based Rehabilitation System

Abstract The increasing pressure on medical institutions around the world requires health care professionals to be prescribing home- based exercise rehabilitation treatments to empower patients to self-monitor their rehabilitation journey. Home-based exercise rehabilitation has shown to be highly effective in treating conditions such as Cardiovascular Disease (CVD). However, adherence to home-based exercise rehabilitation remains low. Possible causes for this are that patients are not monitored, they cannot be con- fident that they are performing the exercise correctly or accurately and they receive no feedback. This paper proposes HeartHealth, a novel patient-centric gamified exercise rehabilitation platform that can help address the issue of adherence to these programmes. The key functionality is the ability to record the patient movements and compare them against the exercises that have been pre- scribed in order to return feedback to the patient and to the health care professional, as well. In order to synthesize a compact fully operational system able to work in real life scenarios, tools and services from FI-PPP projects, FIWARE 1 and FI-STAR 2, were exploited and a new FI-STAR component, Motion Evaluation Specific Enabler (SE), was designed and developed. The HeartHealth system brings together real-time cloud-based motion evaluation coupled with accurate low-cost motion capture, a personalised ex- ercise rehabilitation programme and an intuitive and fun serious game interface, designed specifically with a Cardiac Rehabilitation population in mind.

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.

RGB cams vs RGB-D sensors: Low cost motion capture technologies performances and limitations

Motion capture of the human body has being performed for decades with a growing number of technologies, aims and application fields; but only recent optical markerless technologies based on silhouette recognition and depth sensors which have been developed for videogames control interface have brought motion capture to a broad diffusion. Actually, nowadays there are low cost hardware and software suitable for a wide range of applications that may vary from entertainment domain (e.g., videogames, virtual characters in movies) to the biomechanical and biomedical domain (e.g., gait analysis or orthopedic rehabilitation) and to a huge number of industrial sectors. In this quick evolving scenario it is hard to tell which technology is the most suitable for any desired goal. The aim of the paper is to answer to this issue by presenting a benchmark analysis that compares RGB and RGB-D technologies used to track performing people in a variety of conditions. In order to contrast the solutions, several different tasks have been selected, simultaneously captured and post-processed exactly in the same way. The test campaign has been designed to evaluate pros and cons according to the most important feature of a motion capture technology, such as volume of acquisition, accuracy of joint position and tracking of fast movements. Actors were asked to perform a number of tasks, among which free movements of arms, legs and full body, gait, and tasks performed interacting with a machine. The number of sensors around the scene and their disposition have been considered as well. We used Sony PS Eye cameras and Microsoft Kinect sensors as hardware solutions and iPisoft for data elaboration. The gathered results are organized, compared and discussed stressing performances and limitations of any combination and, at last, we proposed the best candidate technology for some key applications.

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.

Using real-time acceleration data for exercise movement training with a decision tree approach

In this paper, a movement-training system aiming to classify motions for physical education is proposed and analyzed. Traditional physical education requires an instructor teaching exercise movement individually. Teaching every student in a big class demands considerable time and efforts. Utilization of computer-assisted instruction (CAI) becomes pervasive in e-learning trend. However, CAI is often confined in literal form course such as mathematics and language courses. It is necessary to develop a motion-training system for physical education. In this paper, we develop a low-cost motion capture with Wii Remote Control (Wiimote) for training movement exercise, such as tennis and baseball. This system applies Wiimotes to capture acceleration of each part of limbs. Each Wiimote is attached to the limb which then sends back the acceleration information to the computer via Bluetooth wireless link. After gathering the acceleration data of multiple limbs’ parts, the computer recognizes the motion and classifies the motion to several correct and incorrect categories. As a result, it is able to provide the appropriate advice to the students. The system applies a modified ID3 inductive learning to generate a decision tree with continuous-valued attributes. We develop an easy-to-use GUI interface for coaches. The results show that the average accuracy of classification is 83%. The system reduces the workload of the coach and improves teaching and learning performance.

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.

HUMAN POSTURE RECONSTRUCTION AND ANIMATION FROM MONOCULAR IMAGES BASED ON GENETIC ALGORITHMS

The paper aims to propose a new approach towards human posture reconstruction and animation from monocular video sequences that contain any kind of human postures and movements. This is a way towards low cost motion capture and at the same time it avoids many limitations of those classical methods. A parameterized human skeleton model based on anatomy is adopted where the angular constraints are encoded in the joints. Criterion Function is defined to represent the residuals between feature points in the monocular image and the corresponding points resulted from projecting the human model to the projection plane. By transforming each segment of the human model to achieve the minimum value of the Criterion Function, the proper human posture that resembles the one represented by the monocular image can be generated. Different kinds of adjustments are utilized to adjust the body parts into the proper locations and orientations in 3D space without camera calibration. In order to find the optimal solution effectively in a high-dimensional parameter space by considering all the parameters simultaneously, the method of Genetic Algorithms is proposed. A procedure is developed to recover the whole body posture, and then a human animation system is developed to animate a series of human movements from monocular image sequences, during which information between consecutive frames is considered to improve the accuracy. Our technique makes it feasible to reconstruct any possible human postures, and experimental results from many monocular images and video sequences are encouraging.