The study of human movement within sports biomechanics and rehabilitation settings has made considerable progress over recent decades. However, developing a motion analysis system that collects accurate kinematic data in a timely, unobtrusive and externally valid manner remains an open challenge. This narrative review considers the evolution of methods for extracting kinematic information from images, observing how technology has progressed from laborious manual approaches to optoelectronic marker-based systems. The motion analysis systems which are currently most widely used in sports biomechanics and rehabilitation do not allow kinematic data to be collected automatically without the attachment of markers, controlled conditions and/or extensive processing times. These limitations can obstruct the routine use of motion capture in normal training or rehabilitation environments, and there is a clear desire for the development of automatic markerless systems. Such technology is emerging, often driven by the needs of the entertainment industry, and utilising many of the latest trends in computer vision and machine learning. However, the accuracy and practicality of these systems has yet to be fully scrutinised, meaning such markerless systems are not currently in widespread use within biomechanics. This review aims to introduce the key state-of-the-art in markerless motion capture research from computer vision that is likely to have a future impact in biomechanics, while considering the challenges with accuracy and robustness that are yet to be addressed.

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A Review of the Evolution of Vision-Based Motion Analysis and the Integration of Advanced Computer Vision Methods Towards Developing a Markerless System

Background and Purpose . Reports have described the contribution of motor imagery (MI) practice for improving upper-extremity functions in patients with hemiparesis following stroke. The purpose of this case report is to describe the use of MI practice to attempt to improve walking in an individual with hemiparesis. Case Description. A 69-year-old man with left hemiparesis received MI gait practice for 6 weeks. Intervention focused on task-oriented gait and on impairments of the affected lower limb. Preintervention, midterm, postintervention, and follow-up measurements of temporal-distance stride parameters and sagittal kinematics of the knee joint were taken. Main Outcomes . At 6 weeks postintervention, the patient had a 23% increase in gait speed and a 13% reduction in double-support time. An increase in range of motion of the knees also was observed. No changes in gait symmetry were noted. Discussion . The outcomes suggest that MI may be useful for the enhancement of walking ability in patients following stroke. Because improvement was mainly in temporal-distance gait variables and knee movement, imagery practice probably should focus on its specific impairments during gait in order to affect the performance of the paretic lower extremity.

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Motor Imagery for Gait Rehabilitation in Post-Stroke Hemiparesis

https://www.archives-pmr.org/article/S0003-9993(08)00324-9/pdf

Home-based motor imagery training for gait rehabilitation of people with chronic poststroke hemiparesis.

The use of contemporary technology is widely recognised as a key tool for enhancing competitive performance in swimming. Video analysis is traditionally used by coaches to acquire reliable biomecha...

Wearable inertial sensors in swimming motion analysis: a systematic review

The biomechanical role of the subscapularis in latissimus dorsi transfer for the treatment of irreparable rotator cuff tears.

Background and Purpose. Computerized 3-dimensional (3-D) motion measurement systems are used by those interested in human motion. The purposes of this study were (1) to determine the limits of accuracy in determining intersegmental angles during pendular motion at varying speeds and (2) to determine changes in accuracy introduced by autodigitization and digitization by experienced manual raters. Methods. Angular speed of a T-shaped pendulum was systematically increased by releasing the pendulum from 4 angles (0° [no movement], 45°, 90°, and 120°). Twelve reference angles calculated from markers placed on the pendulum were estimated over 20 frames for 10 trials at each release position. Results. Mean errors across trials and frames for intersegmental angles reconstructed by a 3-D motion measurement system were within ±1 degree across all release positions. An analysis of variance and a post hoc Tukey test revealed that the mean error for the autodigitized trials was larger than that for the manually digitized trials. For the autodigitized trials, the static trials (release position=0°) produced less mean error than the trials with movement produced. The ICCs showed a high degree of consistency among all raters, ranging from .707 to .999. Conclusion and Discussion. Our findings support the conclusion that under carefully controlled conditions, a 3-D motion measurement system can produce clinically acceptable measurements of accuracy across a range of angular speeds. Furthermore, acceptable accuracy is possible regardless of the digitization method.

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Accuracy of digitization using automated and manual methods

The objective of this study was to determine the limits of accuracy of angular estimates produced by a computerized three-dimensional (3-D) motion measurement system using auto-digitization and manual digitization. We hypothesized that auto-digitization would produce more accurate and reliable estimates of angular data than manual digitization.
Twelve reference angles were estimated from markers placed on a T-shaped pendulum. The pendulum’s angular velocity was systematically increased by changing the release position from four angles (0 -static, 45 , 90 , and 120 ).
Angular estimates were compared over 20 frames for 10 trials at each release position using analysis of variance (ANOVA), and intra-class correlation coefficients (ICC’s) were used to estimate the variability among release positions.
Two S-VHS camcorders were placed at 45 angles relative to the activity plane for filming, forming an inter-camera angle of 90 . The cameras operated at a film speed of 60 Hz, with a shutter speed of 1/500 second. A single 300-W flood light was positioned beside each camera to illuminate 1.90 cm diameter retro reflective markers. A 3-D calibration structure defined a calibration area 182.88 cm wide, 152.40 cm high, and 60.96 cm deep.
Eighty independent film clips (4 angular velocities X 10 trials X 2 views) were manually digitized across 20 frames by five experienced digitizers. Data was transformed to 3-D coordinates using a DLT, and smoothed with a Butterworth second-order, low-pass recursive filter with a fc of 6 Hz.
Mean errors of the angular estimates across trials and frames were within ±1.0 for each of the four release positions. ANOVA and a post-hoc Tukey test revealed the mean error of the auto-digitized trials was statistically larger (p<0.0001) than the manually digitized trials. For the auto-digitized trials, the static (release position 0 ) produced statistically less (p<0.05) mean error than the dynamic trials. The ICC’s showed a high degree of consistency among all digitizers, with the range from 0.707 to 0.999.
The results of this study support the conclusion than clinically acceptable accuracy can be obtained regardless of digitization method across a range of angular velocities. Surprisingly, it revealed higher accuracy for manually digitized angular estimates than for auto-digitized estimates.

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J. Kyle Gibson

Papers

Accuracy of digitization using automated and manual methods

Accuracy of digitization using automated and manual methods