Marker-based Motion Tracking System Research Articles

Kinematic synergies show good consistency when extracted with a low-cost markerless device and a marker-based motion tracking system

Recently, markerless tracking systems, such as RGB-Depth cameras, have spread to overcome some of the limitations of the gold standard marker-based tracking systems. Although these systems are valuable substitutes for human motion analysis, as they guarantee higher flexibility, faster setup time and lower costs, their tracking accuracy is lower with respect to marker-based systems. Many studies quantified the error made by markerless systems in terms of body segment length estimation, articular angles, and biomechanics, concluding that they are appropriate for many clinical applications related to motion analysis. We propose an innovative approach to compare a markerless tracking system (Kinect V2) with a gold standard marker-based system (Vicon), based on motor control assessment. We quantified kinematic synergies from the tracking data of fifteen participants performing multi-directional upper limb movements. Kinematic synergy analysis decomposes the kinematic data into a reduced set of motor primitives that describe how the central nervous system coordinates motion at spatial and temporal level. Synergies were extracted with the recently released mixed-matrix factorization algorithm. Four synergies were extracted from both marker-based and markerless datasets and synergies were grouped in 6 clusters for each dataset. Cosine similarity in each cluster was ⩾0.60 in both systems, showing good consistency of synergies. Good matching was found between synergies extracted from markerless and from marker-based data, with a cosine similarity between matched synergies ⩾0.60 in 5 out 6 synergies. These results showed that the markerless sensor can be feasible for kinematic synergy analysis for gross movements, as it correctly estimates the number of synergies and in most cases also their spatial and temporal organization.

Optimizing a Feature-Based Motion Tracking System for Prospective Head Motion Estimation in MRI and PET/MRI

Patient head motion during magnetic resonance imaging (MRI) of the brain causes artifacts that limit diagnostic value. Prospective motion correction can, in principle, allow artifact-free imaging for any MRI sequence. Optical motion tracking systems are effective at measuring head motion for prospective motion correction but normally rely on attached markers, which are cumbersome and can move relative to the head. In this study, we optimized and benchmarked an optical motion tracking system that uses in-bore stereo-optical cameras to track feature patches on the forehead rather than attached markers. Feature patches were applied to the forehead in the form of an easily removable ink stamp or temporary tattoo. This approach has flexibility in camera positioning and allows tracking of a variety of surfaces on the human face, even those lacking prominent contours. This is advantageous given the limited space and line-of-sight with typical head coils. We optimized the accuracy and efficiency of two key components of the method: 1) the geometry of the stereo camera setup and 2) the feature detection and matching algorithm used for pose estimation. The absolute accuracy of the optimized feature-based method was compared with a commercial marker-based motion tracking system. Estimates of head motion of 15 volunteers simultaneously tracked by the two systems resulted in average positional discrepancies of less than 3 mm using a stamp and submillimeter average discrepancy using a feature-dense tattoo. These results suggest the feasibility of obtaining accurate head motion data for prospective motion compensated MRI using optimized feature-based motion tracking.

Marker-Based Movement Analysis of Human Body Parts in Therapeutic Procedure.

Movement analysis of human body parts is momentous in several applications including clinical diagnosis and rehabilitation programs. The objective of this research is to present a low-cost 3D visual tracking system to analyze the movement of various body parts during therapeutic procedures. Specifically, a marker based motion tracking system is proposed in this paper to capture the movement information in home-based rehabilitation. Different color markers are attached to the desired joints’ locations and they are detected and tracked in the video to encode their motion information. The availability of this motion information of different body parts during the therapy can be exploited to achieve more accurate results with better clinical insight, which in turn can help improve the therapeutic decision making. The proposed framework is an automated and inexpensive motion tracking system with execution speed close to real time. The performance of the proposed method is evaluated on a dataset of 10 patients using two challenging matrices that measure the average accuracy by estimating the joints’ locations and rotations. The experimental evaluation and its comparison with the existing state-of-the-art techniques reveals the efficiency of the proposed method.

Validating ambulatory gait assessment technique for hazard sensing in construction environments

Abstract Wearable sensing technology offers the opportunity to collect an individual's physical response data in occupational settings; recent studies highlight the usefulness of such data for safety management on a construction site. The authors previously studied a worker's gait movements (measured by Wearable Inertial Measurement Units, WIMUs), providing evidence for the pertinence of collecting workers' gait information to identify fall hazards in a steelwork environment. However, gait parameter measurement accuracy has not been examined in settings with different walk-path conditions and hazards, like those in construction environments. To address this knowledge gap, this study evaluates the accuracy of a WIMU-based gait parameter measurement and examines each gait parameter's ability to distinguish hazardous conditions. Data collection was performed in a laboratory environment with both a WIMU-based system and a marker-based motion tracking system (i.e., the reference system). The presented WIMU-based approach resulted in a 6.48 mean absolute percentage error (MAPE) in a non-hazard condition and manifested comparable performance for distinguishing hazardous environments compared to the reference motion tracking system. The results of this study indicate the validity of an ambulatory gait parameter measurement and demonstrate an opportunity to measure changes in gait parameter values caused by hazards.

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

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).

Sound recording using optical marker-based motion tracking system: Relation between various constraints

Optical marker-based motion tracking system is the device that can record the motion of moving object using multiple high-speed infrared (IR) cameras. Recent development of the motion capture device enables capturing the detailed motions with high spatial precision of sub-millimeter and high sampling rate up to 10 kHz. Currently, the motion tracking cameras can record the local vibrating movement of an acoustic instrument, which makes it possible to retrieve sound from the visual domain. In this study, two constraints in using marker-based motion tracking cameras for the sound recording are discussed. One is the temporal constraint where high sampling rates cause the detectability problem of the retro-reflective marker. When the cameras are operated with high speed, the cameras cannot radiate sufficient IR light and makes it hard to detect. The other is the amplitude constraint where the low camera calibration accuracy increases the signal-to-noise ratio. The effect of these constraints is observed using professional motion tracking cameras (Qualisys) and their relations over various physical conditions will be given.

Performing gait analysis within the timed up & go assessment test: comparison of aTUG to a marker-based tracking system

Results from a technical validation of the aTUG (ambient Timed Up & Go) system are presented. The approach’s gait analysis capabilities were compared to a gold standard: SIMI Motion, a marker-based motion tracking system. Seven people participated and computation of step length and step duration happened with a median error of 3 cm (IQR 3 cm) respectively 0.08 s (IQR 0.07 s). These results show that aTUG has a measurement precision which is sufficient for use in clinical gait analysis and enables the use of the device without a gold standard, i.e. in hospitals outside laboratories or in the homes of patients. aTUG is an approach and system that utilizes only ambient sensor technologies to support the execution of geriatric mobility assessment tests and to perform a gait analysis simultaneously. Such capabilities are strongly demanded in order to support physicians in executing the geriatric assessment tests frequently and objectively in professional and domestic environments. The latter may enable more early prevention and more sustainable rehabilitation.