Automating Video-Based Two-Dimensional Motion Analysis in Sport? Implications for Gait Event Detection, Pose Estimation, and Performance Parameter Analysis.

Background: Two-dimensional (2D) video is a common tool used during sports training and competition to analyze movement. In these videos, biomechanists determine key events, annotate joint centers, and calculate spatial, temporal, and kinematic parameters to provide performance reports to coaches and athletes. Automatic tools relying on computer vision and artificial intelligence methods hold promise to reduce the need for time-consuming manual methods.

Bridging the lab-to-field gap using machine learning: a narrative review.

This paper summarises recent advancement in applications of machine learning in sports biomechanics to bridge the lab-to-field gap as presented in the Hans Gros Emerging Researcher Award lecture at the annual conference of the International Society of Biomechanics in Sports 2022. One major challenge in machine learning applications is the need for large, high-quality datasets. Currently, most datasets, which contain kinematic and kinetic information, were collected using traditional laboratory-based motion capture despite wearable inertial sensors or standard video cameras being the hardware capable of on-field analysis.

Estimating Ground Reaction Forces from Two-Dimensional Pose Data: A Biomechanics-Based Comparison of AlphaPose, BlazePose, and OpenPose.

The adoption of computer vision pose estimation approaches, used to identify keypoint locations which are intended to reflect the necessary anatomical landmarks relied upon by biomechanists for musculoskeletal modelling, has gained increasing traction in recent years. This uptake has been further accelerated by keypoint use as inputs into machine learning models used to estimate biomechanical parameters such as ground reaction forces (GRFs) in the absence of instrumentation required for direct measurement. This study first aimed to investigate the keypoint detection rate of three open-source pose estimation models (AlphaPose, BlazePose, and OpenPose) across varying movements, camera views, and trial lengths.

Synthesising 2D Video from 3D Motion Data for Machine Learning Applications.

To increase the utility of legacy, gold-standard, three-dimensional (3D) motion capture datasets for computer vision-based machine learning applications, this study proposed and validated a method to synthesise two-dimensional (2D) video image frames from historic 3D motion data. We applied the video-based human pose estimation model OpenPose to real (in situ) and synthesised 2D videos and compared anatomical landmark keypoint outputs, with trivial observed differences (2.11−3.

A Comparison of Three Neural Network Approaches for Estimating Joint Angles and Moments from Inertial Measurement Units.

The application of artificial intelligence techniques to wearable sensor data may facilitate accurate analysis outside of controlled laboratory settings-the holy grail for gait clinicians and sports scientists looking to bridge the lab to field divide. Using these techniques, parameters that are difficult to directly measure in-the-wild, may be predicted using surrogate lower resolution inputs. One example is the prediction of joint kinematics and kinetics based on inputs from inertial measurement unit (IMU) sensors.

Prediction of ground reaction force and joint moments based on optical motion capture data during gait.

The standard camera- and force plate-based set-up for motion analysis suffers from the disadvantage of being limited to laboratory settings. Since adaptive algorithms are able to learn the connection between known inputs and outputs and generalise this knowledge to unknown data, these algorithms can be used to leverage motion analysis outside the laboratory. In most biomechanical applications, feedforward neural networks are used, although these networks can only work on time normalised data, while recurrent neural networks can be used for real time applications.

Artificial Neural Networks in Motion Analysis-Applications of Unsupervised and Heuristic Feature Selection Techniques.

The use of machine learning to estimate joint angles from inertial sensors is a promising approach to in-field motion analysis. In this context, the simplification of the measurements by using a small number of sensors is of great interest. Neural networks have the opportunity to estimate joint angles from a sparse dataset, which enables the reduction of sensors necessary for the determination of all three-dimensional lower limb joint angles.

Estimation of Gait Mechanics Based on Simulated and Measured IMU Data Using an Artificial Neural Network.

Enhancement of activity is one major topic related to the aging society. Therefore, it is necessary to understand people's motion and identify possible risk factors during activity. Technology can be used to monitor motion patterns during daily life.

Prediction of lower limb joint angles and moments during gait using artificial neural networks.

In recent years, gait analysis outside the laboratory attracts more and more attention in clinical applications as well as in life sciences. Wearable sensors such as inertial sensors show high potential in these applications. Unfortunately, they can only measure kinematic motions patterns indirectly and the outcome is currently jeopardized by measurement discrepancies compared with the gold standard of optical motion tracking.

Walking with rollator: a systematic review of gait parameters in older persons.

Background: The aging population increasingly needs assistive technologies, such as rollators, to function and live less dependently. Rollators are designed to decrease the risk of falls by improving the gait mechanics of their users. However, data on the biomechanics of rollator assisted gait of older adults are limited, or mostly derived from experiments with younger adults.

Intelligent prediction of kinetic parameters during cutting manoeuvres.

Due to its capabilities in analysing injury risk, the ability to analyse an athlete's ground reaction force and joint moments is of high interest in sports biomechanics. However, using force plates for the kinetic measurements influences the athlete's performance. Therefore, this study aims to use a feed-forward neural network to predict hip, knee and ankle joint moments as well as the ground reaction force from kinematic data during the execution and depart contact of a maximum effort 90° cutting manoeuvre.

Assessment of the measurement accuracy of inertial sensors during different tasks of daily living.

The low cost and ease of use of inertial measurement units (IMUs) make them an attractive option for motion analysis tasks that cannot be easily measured in a laboratory. To date, only a limited amount of research has been conducted comparing commercial IMU systems to optoelectronic systems, the gold standard, for everyday tasks like stair climbing and inclined walking. In this paper, the 3D joint angles of the lower limbs are determined using both an IMU system and an optoelectronic system for twelve participants during stair ascent and descent, and inclined, declined and level walking.

Is bone-cement augmentation of screw-anchor fixation systems superior in unstable femoral neck fractures? A biomechanical cadaveric study.

Objectives: Improved fixation techniques with optional use of bone cements for implant augmentation have been developed to enhance stability and reduce complication rates after osteosynthesis of femoral neck fractures. This biomechanical study aimed to evaluate the effect of cement augmentation on implant anchorage and overall performance of screw-anchor fixation systems in unstable femoral neck fractures.

Methods: Ten pairs of human cadaveric femora were used to create standardized femoral neck fractures (Pauwels type 3 fractures; AO/OTA 31-B2) with comminution and were fixed by means of a rotationally stable screw-anchor (RoSA) system.

Understanding cutting maneuvers - The mechanical consequence of preparatory strategies and foot strike pattern.

Objectives: This study investigated the relation of different previously reported preparatory strategies and musculo-skeletal loading during fast preplanned 90° cutting maneuvers (CM). The aim was to increase the understanding of the connection between whole body orientation, preparatory actions and the solution strategy to fulfil the requirements of a CM.

Methods: Three consecutive steps of anticipated 90° CMs were investigated in a 3D movement analysis setup.

A systematic review of gait analysis methods based on inertial sensors and adaptive algorithms.

The conventional methods to assess human gait are either expensive or complex to be applied regularly in clinical practice. To reduce the cost and simplify the evaluation, inertial sensors and adaptive algorithms have been utilized, respectively. This paper aims to summarize studies that applied adaptive also called artificial intelligence (AI) algorithms to gait analysis based on inertial sensor data, verifying if they can support the clinical evaluation.

A new in vitro spine test rig to track multiple vertebral motions under physiological conditions.

In vitro pure moment spine tests are commonly used to analyse surgical implants in cadaveric models. Most of the tests are performed at room temperature. However, some new dynamic instrumentation devices and soft tissues show temperature-dependent material properties.

Biomechanical testing of a PEEK-based dynamic instrumentation device in a lumbar spine model.

Background: The purpose of this study was to investigate the range-of-motion after posterior polyetheretherketone-based rod stabilisation combined with a dynamic silicone hinge in order to compare it with titanium rigid stabilisation.

Methods: Five human cadaveric lumbar spines with four vertebra each (L2 to L5) were tested in a temperature adjustable spine-testing set-up in four trials: (1) native measurement; (2) kinematics after rigid monosegmental titanium rod instrumentation with anterior intervertebral bracing of the segment L4/5; (3) kinematics after hybrid posterior polyetheretherketone rod instrumentation combined with a silicone hinge within the adjacent level (L3/4) and (4) kinematics after additional decompression with laminectomy of L4 and bilateral resection of the inferior articular processes (L3). During all steps, the specimens were loaded quasi-statically with 1°/s with pure moment up to 7.