Human motion capture systems, constructed from Inertial
Measurement Units (IMUs), have been the subject of recent development and
validation. Inertial kinetics and kinematics have substantial influences on
human biomechanical function. Real-time computation of kinematics is essential
for delivering prompt results or diagnoses, as well as for applications that
necessitate providing feedback to the user, such as correcting a rehabilitation
exercise or enhancing an athletic maneuver. A new algorithm for Inertial
Measurement Unit (IMU)-based motion tracking is presented in this work. The
primary aims of this paper are to combine recent developments in improved
biosensor technology with mainstream motion-tracking hardware to measure the
overall performance of human movement based on joint axis-angle representations
of limb rotation.
This work describes an alternative approach to representing
three-dimensional rotations using a normalized vector around which an
identified joint angle defines the overall rotation, rather than a traditional
Euler angle approach. Furthermore, IMUs allow for the direct measurement of
joint angular velocities, offering the opportunity to increase the accuracy of
instantaneous axis of rotation estimations. Although the axis-angle representation
requires vector quotient algebra (quaternions) to define rotation, this
approach may be preferred for many graphics, vision, and virtual reality
software applications. The analytical method was validated with laboratory data
gathered from an infant dummy leg's flexion and extension knee movements.
The results showed that the novel approach could reasonably
handle a simple case and provide a detailed analysis of axis-angle migration.
The invariant combination of the axis-angle representation could open a new era
of quantifying biomechanical perception-action systems as interactions with the
natural or built environment. The described algorithm could play a notable role
in the biomechanical analysis of human joints and offers a harbinger of IMU-based
biosensors which may detect pathological patterns of joint disease and injury.
Author(s)details:-
Wangdo Kim
Ingeniería Mecánica, Universidad de Ingenieria y Tecnologia – UTEC, Lima,
Perú and Research Center in Bioengineering, Ingeniería Mecánica, Universidad de
Ingenieria y Tecnología-UTEC,Lima 15049, Peru.
Emir A Vela
Ingeniería Mecánica, Universidad de Ingenieria y Tecnologia – UTEC, Lima,
Perú and Research Center in Bioengineering, Ingeniería Mecánica, Universidad de
Ingenieria y Tecnología-UTEC,Lima 15049, Peru.
Please See the book
here :- https://doi.org/10.9734/bpi/strufp/v3/3398G
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