Estimating 3D L5/S1 moments and ground reaction forces during trunk bending using a full-body ambulatory inertial motion capture system

Abstract

a b s t r a c tInertial motion capture (IMC) systems have become increasingly popular for ambulatory movement analysis. However, few studies have attempted to use these measurement techniques to estimate kinetic variables, such as joint moments and ground reaction forces (GRFs).Therefore, we investigated the performance of a full-body ambulatory IMC system in estimating 3D L5/S1 moments and GRFs during symmetric, asymmetric and fast trunk bending, performed by nine male participants. Using an ambulatory IMC system (Xsens/MVN), L5/S1 moments were estimated based on the upper-body segment kinematics using a top-down inverse dynamics analysis, and GRFs were estimated based on full-body segment accelerations.As a reference, a laboratory measurement system was utilized: GRFs were measured with Kistler force plates (FPs), and L5/S1 moments were calculated using a bottom-up inverse dynamics model based on FP data and lower-body kinematics measured with an optical motion capture system (OMC). Correspondence between the OMCþ FP and IMC systems was quantified by calculating root-mean-square errors (RMSerrors) of moment/force time series and the interclass correlation (ICC) of the absolute peak moments/forces.Averaged over subjects, L5/S1 moment RMSerrors remained below 10 Nm (about 5% of the peak extension moment) and 3D GRF RMSerrors remained below 20 N (about 2% of the peak vertical force). ICCs were high for the peak L5/S1 extension moment (0.971) and vertical GRF (0.998). Due to lower amplitudes, smaller ICCs were found for the peak asymmetric L5/S1 moments (0.690-0.781) and horizontal GRFs (0.559-0.948).In conclusion, close correspondence was found between the ambulatory IMC-based and laboratory-based estimates of back load.

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

“…The majority of applied methods require modeling of the musculoskeletal system to a certain degree, with mandatory embedded subject-specific anthropometric data (e.g., mass, dimensions, and center of mass of the body segments). However, such modeling processes inevitably introduce inaccuracies (van den Noort et al, 2013;Faber et al, 2016;Ancillao et al, 2018). In contrast, machine learning-based approaches do not need an a priori knowledge of the model as they build up their model by using training data (Sivakumar et al, 2016;Ancillao et al, 2018;Halilaj et al, 2018).…”

Continuous ambulatory hand force monitoring during manual materials handling using instrumented force shoes and an inertial motion capture suit

“…It is important to realize that the HF errors will not only vary with the type of measurement system used, but also with specific instrumentation within each type. For instance, errors in the laboratory system were 3-4N smaller than in a previous study, which used another type of FP and another version of the Optotrak system (Faber et al, 2015).…”

“…For application of this method in the actual workplace, GRFs and segment accelerations should be measured using ambulatory measurement tools. In previous studies, we have examined the applicability of measuring GRF using instrumented force shoes (FS) (Faber et al, 2009b) and segment accelerations using a fullbody inertial motion capture (IMC) system consisting of inertial measurement units (IMUs) (Faber et al, 2015). In the present study, we evaluated the performance of these ambulatory measurement tools for the estimation of 3D hand forces.…”

Measuring Biomechanical Risk in Lifting Load Tasks Through Wearable System and Machine-Learning Approach

“…These two devices are known for their accuracy and reliability on measurements of kinematic variables, but they are difficult to introduce in a workplace setting [11,22,[33][34][35]. Continuous monitoring of a working site could be obtained through the adoption of wearable motion capture systems that are suitable for outdoor motion analysis [2,4,9,10,17,[36][37][38][39]. Wearable devices, like inertial measurement units (IMUs), represent the most suitable technologic solution for gathering reliable measurements of kinematic parameters and performing motion analysis in a real manufacturing scenario.…”

“…Estimating biomechanical risks in MMH tasks through IMUs [4,37,39] allows for the design of setups that are not bulky and that suitable for each working activity. In 2016, Gholipour and colleagues assessed spinal posture during lifting tasks through three inertial sensors placed on pelvis and trunk segments [38].…”