Body Motion Capture and Applications (original) (raw)

Inertial Motion Capture Costume Design Study

Sensors, 2017

The paper describes a scalable, wearable multi-sensor system for motion capture based on inertial measurement units (IMUs). Such a unit is composed of accelerometer, gyroscope and magnetometer. The final quality of an obtained motion arises from all the individual parts of the described system. The proposed system is a sequence of the following stages: sensor data acquisition, sensor orientation estimation, system calibration, pose estimation and data visualisation. The construction of the system's architecture with the dataflow programming paradigm makes it easy to add, remove and replace the data processing steps. The modular architecture of the system allows an effortless introduction of a new sensor orientation estimation algorithms. The original contribution of the paper is the design study of the individual components used in the motion capture system. The two key steps of the system design are explored in this paper: the evaluation of sensors and algorithms for the orientation estimation. The three chosen algorithms have been implemented and investigated as part of the experiment. Due to the fact that the selection of the sensor has a significant impact on the final result, the sensor evaluation process is also explained and tested. The experimental results confirmed that the choice of sensor and orientation estimation algorithm affect the quality of the final results.

Development of an Inertial Motion Capture System for Clinical Application

I-com, 2017

In this research, a fully automatic inertial motion capture system for the determination and analysis of kinematic motion parameters in ski jumping was developed. Two databases were created for the implementation of the measurement system: one basic database acquired in a laboratory setting and one database acquired during a summer ski jump season on an actual ski jumping slope. First, the former database was used to set up the fundamental data processing method. Next, this method was extended to derive jump kinematics for motion analysis in the larger summer jumping data set. Data analysis showed that the determined body kinematics varied largely in heading angle due to variances in the magnetic field near the top of the ski jumping hill. Therefore, a novel method for the additional compensation of magnetic disturbances was added to the processing framework. The resulting system output data indicated that the final body orientations, joint positions and joint angles were of good and meaningful accuracy. The enhanced inertial capture system consequently constitutes a reliable and very accurate tool to evaluate ski jumps from inertial sensor data under high data comparability and repeatability within different athletes and capture sessions.

Survey of Motion Tracking Methods Based on Inertial Sensors: A Focus on Upper Limb Human Motion

Sensors (Basel, Switzerland), 2017

Motion tracking based on commercial inertial measurements units (IMUs) has been widely studied in the latter years as it is a cost-effective enabling technology for those applications in which motion tracking based on optical technologies is unsuitable. This measurement method has a high impact in human performance assessment and human-robot interaction. IMU motion tracking systems are indeed self-contained and wearable, allowing for long-lasting tracking of the user motion in situated environments. After a survey on IMU-based human tracking, five techniques for motion reconstruction were selected and compared to reconstruct a human arm motion. IMU based estimation was matched against motion tracking based on the Vicon marker-based motion tracking system considered as ground truth. Results show that all but one of the selected models perform similarly (about 35 mm average position estimation error).

Human motion capture sensors and analysis in robotics

2009 IEEE International Conference on Control and Automation, 2009

Abstract This survey reviews motion capture technologies and the current challenges associated with their application in robotic systems. Various sensor systems used in current literature are introduced and evaluated based on the relative strengths and weaknesses. Some research problems pursued with these sensors in robotics are reviewed and application areas are discussed. Significant methodologies in analysing the sensor data are discussed and evaluated based on the perceived benefits and limitations. Finally, results ...

Development and testing of a device for human kinematics measurement

WSEAS TRANSACTIONS on …, 2009

This paper presents a simple, inexpensive, and fast procedure for motion kinematics measurement and analysis . System developed in our laboratory is based on a high speed industrial camera, active LED markers and a PC for handling cameras video stream and data analysis. Active markers used in this work were assembled using small, lightweight and easily available white LEDs. Smaller LEDs allow larger density of markers to be placed on a subject in motion, tracking position and orientation of all segments relevant for motion kinematic analysis. Computer vision algorithm for marker detection and tracking was developed in-house, followed by an algorithm for computing and analyzing kinematics data of human locomotion . Procedures for camera calibration and sub pixel accuracy were also developed and integrated with the system. The accuracy and properties of our system were tested, and results were compared with the existing referent systems presently used in the field. Results of testing marker -camera properties suggest that the system could support work in larger volumes (distances from camera) and almost perpendicular rotations of marker against camera. This property allows building of a 3D kinematics tracking system with two or more cameras placed at different angels against the subject in setup. Proposed system has a few disadvantages; measurements and results that are representative in only one plane and use of battery powered active markers that could disturb subject during normal gait trial. The major advantage of our system is that it offers acceptable accuracy, high speed (up to 320Hz) and easy upgradeability at much lower price when compared with the other commercially available systems . Further development of our system will include additional cameras for 3D marker tracking and integration with an inertial sensor for full kinematics and kinetic measurement of human movement.

Low-cost real-time motion capturing system using inertial measurement units

ACTA IMEKO

Human movement modeling - also referred to as motion-capture - is a rapidly expanding field of interest for medical rehabilitation, sports training, and entertainment. Motion capture devices are used to provide a virtual 3-dimensional reconstruction of human physical activities - employing either optical or inertial sensors. Utilizing inertial measurement units and digital signal processing techniques offers a better alternative in terms of portability and immunity to visual perturbations when compared to conventional optical solutions.In this paper, a cable-free, low-cost motion-capture solution based on inertial measurement units is proposed. The goal of the proposed solution is to apply motion capture to the fields that, because of cost problems, did not take enough benefit of such technology. An example, for instance, could be a gym. According to this goal, the necessary requirement for the proposed system is to be low-cost. Therefore, all the considerations and all the solution...

Inertial sensors for motion detection of human upper limbs

2007

Purpose -This paper seeks to present an inertial motion tracking system for monitoring movements of human upper limbs in order to support a homebased rehabilitation scheme in which the recovery of stroke patients' motor function through repetitive exercises needs to be continuously monitored and appropriately evaluated. Design/methodology/approach -Two inertial sensors are placed on the upper and lower arms in order to obtain acceleration and turning rates. Then the position of the upper limbs can be deduced by using the kinematical model of the upper limbs that was designed in the previous paper. The tracking system starts from inertial data acquisition and pre-filtering, followed by a number of processes such as transformation of coordinate systems of sensor data, and kinematical modelling and optimization of position estimation. Findings -The motion detector using the proposed kinematic model only has drifts in the measurements. Fusion of acceleration and orientation data can effectively solve the drift problem without the involvement of a Kalman filter.