Session: 17-01-01: Research Posters

Paper Number: 150061

150061 - Estimation of Ground Reaction Forces Using Kinematic Data and Opensim. 

Human gait analysis determines the joint forces and torques used in designing suitable prosthetic devices to achieve an able-bodied gait for the user. Ground Reaction Forces (GRFs) are integral to this analysis, providing crucial insights into human locomotion, balance, and injury prevention. Traditional methods of measuring GRFs use force plates, which offer high accuracy but are often costly, labor-intensive, and require specialized equipment. As a result, there is a growing interest in developing cost-effective and accessible alternatives for GRF estimation.

This paper addresses the importance of GRF estimation and proposes an approach utilizing OpenSim, a software platform, and the center of mass (COM) dynamics to estimate the GRF. By leveraging kinematic data, this method offers a practical and efficient alternative to traditional GRF measurement techniques. This approach has significant implications for various fields, including biomechanics, sports science, rehabilitation, and ergonomics. The methodology begins with scaling subject-specific musculoskeletal models based on anthropometric data. These models are then used in Inverse Kinematics (IK) to calculate joint angles from motion capture data, representing the body's movement accurately. Following this, Body Kinematics (BK) is performed to estimate the kinematics of the COM. Finally, GRFs are estimated as the reaction force required to achieve the upward acceleration of the COM against gravity. This approach offers a significant advantage by using only kinematic data, thus reducing the need for expensive equipment and extensive data collection processes. The proposed method was validated using a publicly available dataset from Fukuchi et al. (2018), which includes kinematic and kinetic data from 42 healthy volunteers walking on a treadmill. The results showed a strong agreement between the estimated GRFs and those measured directly using force plates, particularly in the mid and late stages of the stance phase. The method demonstrated a reasonable accuracy, although it slightly underestimated GRFs during initial heel strikes and overestimated them during toe-offs. Despite these minor discrepancies, the overall reliability of the method was affirmed. Root mean square error (RMSE) analysis was performed to quantify the accuracy of the model, revealing an average RMSE of approximately 0.2% body weight (BW). For a 70kg individual, this corresponds to a maximum force error of around 14N, indicating that the kinematic data-based model provides a reasonably accurate estimation of vertical GRFs.

In conclusion, this study presents a validated, cost-effective method for estimating GRFs using kinematic data and OpenSim. This approach not only democratizes access to sophisticated gait analysis but also holds promise for significant advancements in biomechanics, prosthetic design, and rehabilitation. By reducing costs and accelerating the design improvement process, this method facilitates more widespread and effective analysis and development of assistive technologies.

Future research directions include further validation across a broader and more diverse population, exploring higher walking and running speeds, different gait patterns, and various surfaces and footwear. Additionally, integrating other biomechanical parameters such as muscle activity and joint forces could offer a more comprehensive understanding of load transfer during walking, leading to enhanced rehabilitation strategies and improved prosthetic device design.

Presenting Author: Ranjan Das University of California Merced

Presenting Author Biography: I am Ranjan Das, a dedicated graduate student in Mechanical Engineering at the University of California, Merced, currently focusing on human gait analysis. My research aims to develop advanced control strategies and mathematical models to detect early signs of Parkinson's disease through muscle force estimation and EMG data analysis.
My educational journey began with a Bachelor's in Technology in Mechanical Engineering from Tezpur University, India, followed by a Master's in Technology in Mechanical Engineering from IIT Bombay, India. I further enhanced my skills with a Master's Program Certification of Data Scientist from Simplilearn in December 2021.
I have over seven years of professional experience, where I performed solid, sheet metal, and plastic part modeling, process simulation, and both static and dynamic analysis of systems. I lead product development initiatives, conducting failure analysis, and designed successful oral dosage packaging machines and food processing machines.
Throughout my career, I have developed proficiency in programming languages such as MATLAB, Python, and am adept with FEA tools like SolidWorks and ANSYS. My academic projects include numerical modeling of friction in drilling, and finite element analysis of beams. Presently I’m researching on developing mathematical models for human gait analysis.
I have been recognized with scholarships for academic excellence, including the GATE qualification-based scholarship for my Master's program and the National Talent Search Examination Scholarship. My collaborative work with the National Aerospace Laboratory has further enriched my practical experience.

Authors:

Ranjan Das University of California Merced
Jacques-Ezechiel N'guessan University of California, Merced
Sachin Goyal University of California, Merced
Matthew Leineweber Biomotum Inc.