Estimation of the Knee Adduction Moment and Joint Contact Force during Daily Living Activities Using Inertial Motion Capture

Abstract

Knee osteoarthritis is a major cause of pain and disability in the elderly population with many daily living activities being difficult to perform as a result of this disease. The present study aimed to estimate the knee adduction moment and tibiofemoral joint contact force during daily living activities using a musculoskeletal model with inertial motion capture derived kinematics in an elderly population. Eight elderly participants were instrumented with 17 inertial measurement units, as well as 53 opto-reflective markers affixed to anatomical landmarks. Participants performed stair ascent, stair descent, and sit-to-stand movements while both motion capture methods were synchronously recorded. A musculoskeletal model containing 39 degrees-of-freedom was used to estimate the knee adduction moment and tibiofemoral joint contact force. Strong to excellent Pearson correlation coefficients were found for the IMC-derived kinematics across the daily living tasks with root mean square errors (RMSE) between 3° and 7°. Furthermore, moderate to strong Pearson correlation coefficients were found in the knee adduction moment and tibiofemoral joint contact forces with RMSE between 0.006–0.014 body weight × body height and 0.4 to 1 body weights, respectively. These findings demonstrate that inertial motion capture may be used to estimate knee adduction moments and tibiofemoral contact forces with comparable accuracy to optical motion capture.

A Machine Learning and Wearable Sensor Based Approach to Estimate External Knee Flexion and Adduction Moments During Various Locomotion Tasks

“…Their results showed for the comparison with an optical motion capture system higher r values (range 0.82-0.99 and 0.76-0.99 for the inertial and optical motion capture systems, respectively) and lower rRMSE values (range from 5 to 15% for both systems) compared to the KFM and KAM estimations present in this study. More recent studies from Dorschky et al (2019) and Konrath et al (2019) used inertial motion capturing and musculoskeletal modeling to estimate biomechanical variables, such as joint kinematics and kinetics without GRF data. Dorschky et al (2019) presented high correlations for sagittal plain kinematics (r > 0.93) and kinetics (r > 0.90) in gait and running.…”

“…Knee joint kinetics were not analyzed in this study. A further approach of a mobile assessment of knee joint biomechanics in natural environment was recently provided by Konrath et al (2019). The authors estimated the KAM and the tibio-femoral joint contact force during activities of daily living by means of combining musculoskeletal modeling with inertial motion capture (17 IMUs).…”

Wearable Monitoring and Predicting System for Knee Joint Fatigue Based on Curvature and Pressure Sensing

“…In evaluation, the prototype is able to acquire reliable data in dynamic knee movements, but it has not provided an economical embedded solution of portable device for daily application and is without the testing dataset on real sports of human. Another motion capture system based on 17 inertial units and 53 opto-reflective markers is proposed to measure the knee adduction and joint contact force during daily living activities of elderly people with knee osteoarthritis [8]. This research proves the feasibility of the inertial motion detecting system but lacks the data analysis for actual daily applications.…”

“…According to Equation (7), Equation (6) can be further converted to the incremental form as Equation (8). That formula can simplify the calculation and save storage space.…”

Measuring biomechanical loads in team sports – from lab to field

“…For example, full-body wireless inertial sensor suits have been shown to be a reliable and valid method to simultaneously measure kinematic information of all body segments outside the laboratory (e.g. Xsens MVN [75]), and can already provide GRF and joint moment estimates during stereotypical activities such as walking [76,77]. To overcome discomfort and movement restriction issues associated with the use of multiple body-worn devices, markerless motion capture techniques are a non-invasive method for measuring different biomechanical variables in various sport environments [78,79,80,81,82,83].…”