Motion capture sensing techniques used in human upper limb motion: a review

Purpose Motion capture system (MoCap) has been used in measuring the human body segments in several applications including film special effects, health care, outer-space and under-water navigation systems, sea-water exploration pursuits, human machine interaction and learning software to help teachers of sign language. The purpose of this paper is to help the researchers to select specific MoCap system for various applications and the development of new algorithms related to upper limb motion. Design/methodology/approach This paper provides an overview of different sensors used in MoCap and techniques used for estimating human upper limb motion. Findings The existing MoCaps suffer from several issues depending on the type of MoCap used. These issues include drifting and placement of Inertial sensors, occlusion and jitters in Kinect, noise in electromyography signals and the requirement of a well-structured, calibrated environment and time-consuming task of placing markers in multiple camera systems. Originality/value This paper outlines the issues and challenges in MoCaps for measuring human upper limb motion and provides an overview on the techniques to overcome these issues and challenges

Validation of joint angle measurements: comparison of a novel low cost marker-less system with an industry standard marker-based system

Human motion tracking is widely used for assessment of movement dysfunction in orthopaedic patients. Currently, most clinical motion analysis centres use marker based three-dimensional (3D) systems as they are deemed to be the most accurate method. However, due to space, costs and logistics they are not available in many clinical settings. This study compared joint angles measured in functional tests using the novel low-cost Microsoft Kinect Perfect Phorm system with the established marker based Nexus VICON system. When measuring right and left knee flexion, the average difference between the VICON and Kinect Perfect Phorm measurement was 13.2%, with a SD of 19.6. Both overestimation and underestimation of the joint angle was recorded in different participants. Although the average percentage difference during hip abduction tests was lower at -3.9%, the range of error was far greater (SD=75). From this, it can be concluded that the level of accuracy presented in the new low cost Kinect Perfect Phorm system is not yet suitable for clinical assessments. However, for general tests of performance, and for tracking cases where absolute accuracy is less critical, future versions of this software may have a place

A validation study of a Kinect based Body Imaging (KBI) device system based on ISO 20685:2010

To replace the traditional anthropometric data collection processes with the 3D acquiring system it is important that the validity of the data is not compromised. To do this, a validation study, based on the guideline of ISO 20685, can be performed. This paper presents the results of a comparison between traditional measurements and measurements taken with a 3D acquiring system using only four Kinect sensors. The results obtained were then compared with the maximum allowable error indicated in ISO 20685, concluding that this system cannot give sufficiently reliable data that can substitute the manual procedures.FEDER funds through the Competitive Factors Operational Program (COMPETE) and by national funds through FCT (Portuguese Foundation for Science and Technology) with the projects PEst- C/CTM/U10264 and ID/CEC/00319/201

Creating a Worker-Individual Physical Ability Profile Using a Low-Cost Depth Camera

Assembly workers suffer from long-term damage performing physically intensive tasks due to workstations that are not ergonomically designed for the individual’s needs. Current approaches towards ergonomic improvements of workstations only assess the workstations themselves without taking the individual worker and abilities into account. Therefore, physical limitations, such as age-related loss of range of motion, are not addressed. Work-induced long-term damages result in employee absences, especially of workers close to their pension. Regarding the demographic change, this issue will be even more prevalent in the future. The current approaches, like the functional capacity evaluation, allow movement analysis of individuals, but are too time-consuming to be performed on all workers of a production site. This paper presents a method to assess the individual ability of a worker using a low-cost depth camera with full body tracking to determine the angles between body segments. A set of ergonomic exercises is used to demonstrate relevant abilities for assembly and commissioning tasks. By capturing the motion sequence of these exercises, a physical ability profile can be created with little effort

Can shoulder range of movement be measured accurately using the Microsoft Kinect sensor plus Medical Interactive Recovery Assistant (MIRA) software?

BackgroundThis study compared the accuracy of measuring shoulder range of movement (ROM) with a simple laptop-sensor combination vs. trained observers (shoulder physiotherapists and shoulder surgeons) using motion capture (MoCap) laboratory equipment as the gold standard. MethodsThe Microsoft Kinect sensor (Microsoft Corp., Redmond, WA, USA) tracks 3-dimensional human motion. Ordinarily used with an Xbox (Microsoft Corp.) video game console, Medical Interactive Recovery Assistant (MIRA) software (MIRA Rehab Ltd., London, UK) allows this small sensor to measure shoulder movement with a standard computer. Shoulder movements of 49 healthy volunteers were simultaneously measured by trained observers, MoCap, and the MIRA device. Internal rotation was assessed with the shoulder abducted 90° and external rotation with the shoulder adducted. Visual estimation and MIRA measurements were compared with gold standard MoCap measurements for agreement using Bland-Altman methods. Results There were 1670 measurements analyzed. The MIRA evaluations of all 4 cardinal shoulder movements were significantly more precise, with narrower limits of agreement, than the measurements of trained observers. MIRA achieved ±11° (95% confidence interval [CI], 8.7°-12.6°) for forward flexion vs. ±16° (95% CI, 14.6°-17.6°) by trained observers. For abduction, MIRA showed ±11° (95% CI, 8.7°-12.8°) against ±15° (95% CI, 13.4°-16.2°) for trained observers. MIRA attained ±10° (95% CI, 8.1°-11.9°) during external rotation measurement, whereas trained observers only reached ±21° (95% CI, 18.7°-22.6°). For internal rotation, MIRA achieved ±9° (95% CI, 7.2°-10.4°), which was again better than TOs at ±18° (95% CI, 16.0°-19.3°). ConclusionsA laptop combined with a Microsoft Kinect sensor and the MIRA software can measure shoulder movements with acceptable levels of accuracy. This technology, which can be easily set up, may also allow precise shoulder ROM measurement outside the clinic setting

Measurement Straight Leg Raise for Low Back Pain Based Grayscale Depth

Spinal disorders are the most frequent cause of pain and lower part of the spine, which is often called Low Back Pain.Straight Leg Raise Test  can provide important information to detect the cause of LBP. Straight Leg Raise test conducted by physican with a goniometer required accurately reading angle when your feet up. But this can be overcome with Kinect can detect motion and displays images and depth data. Methodological includes image acquisition method using RGB and Grayscale depth, skeleton tracking, feature extraction detection Straight Leg Raise . The proposed algorithm describes a method for estimating the data triangulation angle Straight Leg Raise by Kinect. Results measurement if   the positive Low Back Pain below 60 degrees there is a tendency to suffer from one of the causes of Low Back Pain. The results can be stored in the database as medical history and used to monitor the progress of therap

Markerless measurement techniques for motion analysis in sports science

Markerless motion capture system and X-ray fluoroscopy as two markerless measurement systems were introduced to the application method in sports biomechanical areas. An overview of the technological process, data accuracy, suggested movements, and recommended body parts were explained. The markerless motion capture system consists of four parts: camera, body model, image feature, and algorithms. Even though the markerless motion capture system seems promising, it is not yet known whether these systems can be used to achieve the required accuracy and whether they can be appropriately used in sports biomechanics and clinical research. The biplane fluoroscopy technique analyzes motion data by collecting, image calibrating, and processing, which is effective for determining small joint kinematic changes and calculating joint angles. The method was used to measure walking and jumping movements primarily because of the experimental conditions and mainly to detect the data of lower limb joints

Correction of joint angles from kinect for balance exercising and assessment

[EN] The new generation of videogame interfaces such as Microsoft's Kinect opens the possibility of implementing exercise programs for physical training, and of evaluating and reducing the risks of elderly people falling. However, applications such as these might require measurements of joint kinematics that are more robust and accurate than the standard output given by the available middleware. This article presents a method based on particle filters for calculating joint angles from the positions of the anatomical points detected by PrimeSense's NITE software. The application of this method to the measurement of lower limb kinematics reduced the error by one order of magnitude, to less than 10 degrees, except for hip axial rotation, and it was advantageous over inverse kinematic analysis, in ensuring a robust and smooth solution without singularities, when the limbs are out-stretched and anatomical landmarks are aligned.This work has been undertaken within the framework of the iStoppFalls project, which has received funding from the European Community (grant agreement FP7-ICT-2011-7-287361) and the Australian Government.De Rosario Martínez, H.; Belda Lois, JM.; Fos Ros, F.; Medina Ripoll, E.; Poveda Puente, R.; Kroll, M. (2014). Correction of joint angles from kinect for balance exercising and assessment. Journal of Applied Biomechanics. 30(2):294-299. https://doi.org/10.1123/jab.2013-0062S29429930