Automated event detection algorithm for two squatting protocols

A comparison between manual and automated event detection for a drop vertical jump task using motion capture

BACKGROUND

The use of movement screens as a clinical tool for injury risk assessment requires variables to be extracted across specific phases of interest. While manually selecting task events is the traditional method, automated event detection is an effective technique that maintains consistency across a cohort. This study aimed to examine variations in event identification, comparing manual detection and the application of an automated algorithm, with a specific focus on a drop vertical jump task.

METHODS

Thirty participants cleared to return-to-play after anterior cruciate ligament reconstruction and thirty controls were tested. For the automated event detection, normalized vertical ground reaction force and the velocity of the sacrum marker were used to identify five events during the drop vertical jump: initial contact, end of loading, end of propulsion, second contact, and end of second loading. Two raters manually selected events and were compared to the event times of the automated algorithm.

FINDINGS

Manual event detection exhibited excellent reliability Significant differences between manual and automated detection were observed, particularly at events indicating the lowest squat position (Event2 and Event5). Participants who had undergone anterior cruciate ligament reconstruction demonstrated larger differences than controls at Event5, correlating with significant squat depth disparities.

INTERPRETATION

While manual event detection demonstrated reliability, automated algorithms revealed differences, specifically in events of the drop vertical jump involving the lowest squat position. The automated algorithm presents potential benefits in reducing processing time and enhancing accuracy for event identification, offering valuable insights for motion capture applications in clinical settings.

IMU positioning affects range of motion measurement during squat motion analysis

Inertial Measurement Units (IMUs) provides embedded and accessible (financially and technically speaking) motion analysis for sports or clinical applications (rehabilitation, therapy…). Despite being advertised for it ease of use, the very nature of IMU sensor makes it prone to errors which are usually corrected through calibration processes thus adding extra complexity for the users. The main goal of this study is to estimate the effect of sensor positioning on the thigh for a simple assessment of squat motion range of motion (ROM) as could be done in a pragmatic clinical approach (i.e., without prior calibration). Kinematics, squat counts and timing of three IMU sensors along the thigh were recorded during squat motion and compared to an optoelectronic reference system. Results showed concordance coefficients of the IMU system over 0.944 without the need for calibration with a preference for placement on the distal part of the segment regarding kinematics data.

Advancing biomechanics laboratories capabilities: A proposed framework for in-house technology development

BACKGROUND

Technological advancements have generated more opportunities to develop/distribute custom data analysis codes (e.g., automated events, biomechanical models, etc.). Industry standards for the code development process is regularly modeled to ensure product quality and usability. Procedural project management improves efficiency of the code development process by monitoring project planning, duration, analysis, success, and maintenance. The purpose of this study was to outline in the form of guidance to research labs, a framework that standardizes the development, management, testing, and documentation of various types of data analysis codes, utilized in the motion analysis laboratory setting.

METHODS

This brief report outlines the workflow, briefly highlights its success a year after implementation, and provides a framework that can be adopted across laboratories of different sizes and those involved in multi-center collaborative studies. Specifically, the workflow outlined is initiated when a requestor has identified the need for a custom data analysis code. The workflow is complete and the code is released once the results of testing performed by a non-affiliated user, verifies that the code project workflow was followed appropriately, confirms a standard operating procedure has been finalized, and ensures the requestor and additional end-users are satisfied with the final product.

FINDINGS

Guidance documents and optimization of workflows are imperative for motion analysis laboratories managing numerous coding projects.

INTERPRETATION

Implementation of the proposed framework is an effective approach to reduce workload, by minimizing redundancies, maximizing on the research team's expertise and promotes collaborative input which in turn allows for feedback along the process.

Electromyography measurements of the deep hip muscles do not improve estimates of hip contact force

The deep hip muscles are often omitted in studies investigating hip contact forces using neuromusculoskeletal modelling methods. However, recent evidence indicates the deep hip muscles have potential to change the direction of hip contact force and could have relevance for hip contact loading estimates. Further, it is not known whether deep hip muscle excitation patterns can be accurately estimated using neuromusculoskeletal modelling or require measurement (through invasive and time-consuming methods) to inform models used to estimate hip contact forces. We calculated hip contact forces during walking, squatting, and squat-jumping for 17 participants using electromyography (EMG)-informed neuromusculoskeletal modelling with (informed) and without (synthesized) intramuscular EMG for the deep hip muscles (piriformis, obturator internus, quadratus femoris). Hip contact force magnitude and direction, calculated as the angle between hip contact force and vector from femoral head to acetabular center, were compared between configurations using a paired t-test deployed through statistical parametric mapping (P < 0.05). Additionally, root mean square error, correlation coefficients (R²), and timing accuracy between measured and modelled deep hip muscle excitation patterns were computed. No significant between-configuration differences in hip contact force magnitude or direction were found for any task. However, the synthesized method poorly predicted (R²-range 0.02-0.3) deep hip muscle excitation patterns for all tasks. Consequently, intramuscular EMG of the deep hip muscles may be unnecessary when estimating hip contact force magnitude or direction using EMG-informed neuromusculoskeletal modelling, though is likely essential for investigations of deep hip muscle function.