Abstract

Clinical Gait Analysis (CGA) is a pivotal technique for evaluating pathological conditions, particularly musculoskeletal disorders. However, its efficacy is often hindered by the fact that normative gait data is almost always used worldwide as a basis for CGA, regardless of differences in critical parameters such as BMI, age, gender, and walking speeds. To address this, we developed multiple regression models for predicting lower limb sagittal kinematic waveforms. We recorded anthropometric, demographic, spatiotemporal, and kinematic data from 30 healthy individuals. Leveraging the gait cycle time and joint angles as dependent variables, and BMI, age, gender, and walking speeds as predictors, we developed 46 regression equations. We employed PCHIP utilizing 80% of the kinematic data to reconstruct the waveforms and validated via leave-one-out cross validation. Our models successfully reconstructed hip, knee, and ankle kinematic waveforms, achieving R2 ≥ 0.9 and RMSE ≤ 6° from the validation study. P-values < 0.05 as well as the clinical relevance of the predictors were considered during the regression analysis. These outcomes underscore the potential for our approach to be used as the basis to enhance the precision of region-specific gait data predictions, thus facilitating more accurate CGA.

Competing Interest Statement

The authors have declared no competing interest.

Funding Statement

This study did not receive any funding

Author Declarations

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

Yes

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

The University of Ghana local Ethics Committee for Basic and Applied Sciences (ECBAS) had already approved a data collection protocol our predecessors used. Since our study was an extension of the previous works done, the protocol for the current study was simply waived. The data recording process was briefly explained to the participants and they were made to fill a consent form which was approved by the University of Ghana local Ethics Committee of Basic and Applied Sciences prior to their inclusion. The protocol was conformed to the Declaration of Helsinki for human experiments (World Medical Association, 1974).

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

Yes

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

Yes

I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

Yes

Data Availability

All data produced in the present study are available upon reasonable request to the corresponding author.

https://github.com/PhilipKone/Predictive-Gait-Analysis-

AbbreviationsCGAClinical gait analysisLMLinear ModelSLMStepwise Linear ModelRLMRobust Linear ModelC1self-selected normal walking speedC2slow walking speedC3fast walking speedBMIBody Mass IndexPCHIPPiecewise Cubic Hermite Interpolating PolynomialR2coefficient of determinationRMSERoot Mean Squared ErrorWSdimensionless walking speedLEDLight Emitting Diode