Musculoskeletal model predictions sensitivity to upper body mass scaling during gait

• Published in: Computers in biology and medicine Vol. 186; p. 109739
• Main Authors: Hulleck, Abdul Aziz Vaqar, Abdullah, Muhammad, Alkhalaileh, AbdelSalam Tareq, Liu, Tao, Mohan, Dhanya Menoth, Katmah, Rateb, Khalaf, Kinda, El-Rich, Marwan
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.03.2025; Elsevier Limited
• Subjects: Adult; Algorithms; Ankle; Biomechanical Phenomena; Body Mass Index; Body size; Computation; Consent; Customization; Electromyography; Female; Gait; Gait - physiology; Gait biomechanics; Human motion; Humans; Inertial platforms; Internal Medicine; Inverse dynamics; Joints (anatomy); Kinematics; Kinetics; Low back pain; Male; Mass distribution; Mean square errors; Methods; Modelling; Models, Biological; Motion capture; Muscle function; Musculoskeletal modeling; Obesity; Obesity - physiopathology; Other; Parameter estimation; Parameter sensitivity; Rank tests; Root-mean-square errors; Scaling; Scoliosis; Sensitivity analysis; Sensors; Software; Statistical analysis; Statistical models; Subject-specific upper body mass distribution; Vertical distribution; Vertical forces; Young Adult; Subject-specific upper body mass distribution; Musculoskeletal modeling; Gait biomechanics
• ISSN: 0010-4825; 1879-0534; 1879-0534
• DOI: 10.1016/j.compbiomed.2025.109739

Musculoskeletal modeling based on inverse dynamics provides a cost-effective non-invasive means for calculating intersegmental joint reaction forces and moments, solely relying on kinematic data, easily obtained from smart wearables. On the other hand, the accuracy and precision of such models strongly hinge upon the selected scaling methodology tailored to subject-specific data. This study investigates the impact of upper body mass distribution on internal and external kinetics computed using a comprehensive musculoskeletal model during level walking in both normal weight and obese individuals. Human motion data was collected using seventeen body worn inertial measuring units for nineteen (19) healthy subjects. The results indicate that variations in segmental masses and centers of mass, resulting from diverse mass scaling techniques, significantly affect ground reaction force estimations in obese subjects, particularly in the vertical component, with a root mean square error (RMSE) of 54.7 ± 23.8 %BW; followed by 12.3 ± 8.0 %BW (medio-lateral); and 6.2 ± 3.2 %BW (antero-posterior). The vertical component of hip, knee, and ankle joint reaction forces also exhibit sensitivity to personalized mass distribution variations. Importantly, the degree of deviation in model predictions increases with body mass index. Statistical analysis using single sample Wilcoxon-Signed Rank test for non-normal data and t-test for normal data, revealed significant differences (p < 0.05) in the computed errors in kinetic parameters between the two scaling approaches. The body shape-based scaling approach significantly impacts musculoskeletal modeling in clinical applications where the upper body mass distribution is crucial, such as in spinal deformities, obesity, and low back pain. This approach accounts for the body shape inherent variability within the same BMI category and enhances the predicted joint kinetics. [Display omitted] •Impact of upper body mass distribution on musculoskeletal model predictions during gait.•Two mass scaling methods: Constant Percentage-Based (CPB) and Body Shape-Based (BSB).•Gait dynamics in both normal-weight and obese individuals using subject-specific models.•BSB scaling approach incorporated fifteen anthropometric measurements, providing a personalized model.•Predicted joint reaction forces and ground reaction forces are highly sensitive to mass scaling methods.•Obese individuals demonstrated increased sensitivity to variations in model scaling compared to normal-weight subjects.•Statistical analysis revealed significant differences in model predictions between CPB and BSB methods.•Results highlight the need for personalized scaling to improve the accuracy of musculoskeletal models.