Citation: Muller, A.; Mecheri, H.;
Corbeil, P.; Plamondon, A.;
Robert-Lachaine, X. Inertial Motion
Capture-Based Estimation of L5/S1
Moments during Manual Materials
Handling. Sensors 2022, 22, 6454.
https://doi.org/10.3390/s22176454
Academic Editors: Pietro Picerno,
Andrea Mannini and Clive D’Souza
Received: 22 July 2022
Accepted: 24 August 2022
Published: 26 August 2022
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Article
Inertial Motion Capture-Based Estimation of L5/S1 Moments
during Manual Materials Handling
Antoine Muller
1,
* , Hakim Mecheri
2
, Philippe Corbeil
3,4
, André Plamondon
2
and Xavier Robert-Lachaine
2
1
Univ Lyon, Univ Gustave Eiffel, Univ Claude Bernard Lyon 1, LBMC UMR_T 9406, F-69622 Lyon, France
2
Institut de Recherche Robert-Sauvé en Santé et en Sécurité du Travail (IRSST), Montreal, QC H3A 3C2, Canada
3
Department of Kinesiology, Université Laval, Québec, QC G1V 0A6, Canada
4
Centre Interdisciplinaire de Recherche en Réadaptation et Intégration Sociale du Centre Intégré Universitaire
de Santé et de Services Sociaux de la Capitale-Nationale (CIRRIS/CIUSSS-CN), Québec, QC G1C 3S2, Canada
* Correspondence: antoine.muller@univ-lyon1.fr
Abstract:
Inertial motion capture (IMC) has gained popularity in conducting ergonomic studies
in the workplace. Because of the need to measure contact forces, most of these in situ studies are
limited to a kinematic analysis, such as posture or working technique analysis. This paper aims
to develop and evaluate an IMC-based approach to estimate back loading during manual material
handling (MMH) tasks. During various representative workplace MMH tasks performed by nine
participants, this approach was evaluated by comparing the results with the ones computed from
optical motion capture and a large force platform. Root mean square errors of 21 Nm and 15 Nm were
obtained for flexion and asymmetric L5/S1 moments, respectively. Excellent correlations were found
between both computations on indicators based on L5/S1 peak and cumulative flexion moments,
while lower correlations were found on indicators based on asymmetric moments. Since no force
measurement or load kinematics measurement is needed, this study shows the potential of using
only the handler’s kinematics measured by IMC to estimate kinetics variables. The assessment of
workplace physical exposure, including L5/S1 moments, will allow more complete ergonomics
evaluation and will improve the ecological validity compared to laboratory studies, where the
situations are often simplified and standardized.
Keywords:
inertial measurement units (IMU); wearable systems; workplace ergonomics; in situ
analysis; kinetics; ground reaction forces
1. Introduction
The use of inertial motion capture (IMC) systems for measuring human movement
is constantly increasing. In the context of ergonomics evaluation, IMC is used in the
field for ambulatory assessment of physical workloads by analyzing human postures and
movement [
1
]. In comparison to studies performed in the laboratory, numerous studies
described the important benefits of workplace analyses [2–4].
Kinematic data obtained from IMC systems have been validated in several studies in
the laboratory by comparison to data obtained from optoelectronic motion capture (OMC)
systems, considered as a reference system. These validation studies have been performed
for various applications [
5
,
6
], including work tasks with complete body movements and
acquisition duration of over 30 min (Robert-Lachaine et al., 2020 [
7
]; Robert-Lachaine et al.,
2017 [
8
]). The estimation of kinetic variables such as back loading from IMC systems has
also been validated in the laboratory [
9
,
10
]. In this case, the use of an additional force
platform was required to measure the ground reaction forces.
Because of the need to measure the contact forces between the subject and his envi-
ronment (in most cases, the ground reaction forces), most in situ ergonomics studies are
limited to a kinematic analysis [
2
–
4
]. However, the addition of physical exposure indicators
based on kinetic data would allow a more complete ergonomic evaluation. Back loading is
Sensors 2022, 22, 6454. https://doi.org/10.3390/s22176454 https://www.mdpi.com/journal/sensors
