TO CAPTURE human body motion in an ambulatory situation without the need for external emitters or cameras,
several systems are available. Mechanical trackers utilize rigid
or flexible goniometers which are worn by the user. These
angle measuring devices provide joint angle data to kinematic
algorithms which are used to determine body posture. Attach-
ment of the body-based linkages as well as the positioning
of the goniometers present several problems. The soft tissue
of the body allows the position of the linkages relative to the
body to change as motion occurs. Even without these changes,
alignment of the goniometer with body joints is difficult,
especially for multiple degree of freedom joints.
The use of inertial sensors has become a common practice
in ambulatory motion analysis [1, 2]. For accurate and drift
free orientation estimation several methods have been reported
combining the signals from 3D gyroscopes, accelerometers
and magnetometers [3, 4]. Accelerometers are used to deter-
mine the direction of the local vertical by sensing acceleration
due to gravity. Magnetic sensors provide stability in the
horizontal plane by sensing the direction of the earth magnetic
field like a compass. Data from these complementary sensors
can be used to eliminate drift by continuous correction of the
orientation obtained by integrating rate sensor data.
By using the calculated orientations of individual body seg-
ments and the knowledge about the segment lengths, rotations
between segments can be estimated and a position of the
segments can be derived under strict assumptions of a linked
kinematic chain [4–6]. This method assumes an articulated
rigid body in which the joints only have rotational degrees
of freedom. However, a human body and its joints cannot be
modeled as a pure kinematic chain with well-defined joints
such as hinge-joints and ball-and-socket-joints. Each human
joint allows some laxity in all directions (both position and orientation) other than its main direction of movement [7].
Moreover, to be able to track complex human joints and non-
rigid body parts such as the back and shoulder accurately,
more than three degrees of freedom, as given by an orientation
measurement, are required. Furthermore, importantly, with
only orientation driven motion capture, it is not possible
to analyze the clearance of both feet, which occurs during
running or jumping. Using this approach, it is also not possible
to accurately determine the displacement of the body with
respect to a coordinate system not fixed to the body.
To provide full six-degree-of-freedom tracking of body
segments with connected inertial sensor modules, each body
segment’s orientation and position can be estimated by, respec-
tively, integrating the gyroscope data and double integrating
the accelerometer data in time. However, due to the inherent
integration drift, these uncorrected estimates are only accurate
within a few seconds [8]. By combining the inertial estimates
with other body worn aiding systems, such as a acoustic [9]
or a magnetic tracker [10], unbound integration drift can be
prevented.
TO CAPTURE human body motion in an ambulatory situation without the need for external emitters or cameras,several systems are available. Mechanical trackers utilize rigidor flexible goniometers which are worn by the user. Theseangle measuring devices provide joint angle data to kinematicalgorithms which are used to determine body posture. Attach-ment of the body-based linkages as well as the positioningof the goniometers present several problems. The soft tissueof the body allows the position of the linkages relative to thebody to change as motion occurs. Even without these changes,alignment of the goniometer with body joints is difficult,especially for multiple degree of freedom joints.The use of inertial sensors has become a common practicein ambulatory motion analysis [1, 2]. For accurate and driftfree orientation estimation several methods have been reportedcombining the signals from 3D gyroscopes, accelerometersand magnetometers [3, 4]. Accelerometers are used to deter-mine the direction of the local vertical by sensing accelerationdue to gravity. Magnetic sensors provide stability in thehorizontal plane by sensing the direction of the earth magneticfield like a compass. Data from these complementary sensorscan be used to eliminate drift by continuous correction of theorientation obtained by integrating rate sensor data.By using the calculated orientations of individual body seg-ments and the knowledge about the segment lengths, rotationsbetween segments can be estimated and a position of thesegments can be derived under strict assumptions of a linkedkinematic chain [4–6]. This method assumes an articulatedrigid body in which the joints only have rotational degreesof freedom. However, a human body and its joints cannot bemodeled as a pure kinematic chain with well-defined jointssuch as hinge-joints and ball-and-socket-joints. Each humanjoint allows some laxity in all directions (both position and orientation) other than its main direction of movement [7].Moreover, to be able to track complex human joints and non-rigid body parts such as the back and shoulder accurately,more than three degrees of freedom, as given by an orientationmeasurement, are required. Furthermore, importantly, withonly orientation driven motion capture, it is not possibleto analyze the clearance of both feet, which occurs duringrunning or jumping. Using this approach, it is also not possibleto accurately determine the displacement of the body withrespect to a coordinate system not fixed to the body.To provide full six-degree-of-freedom tracking of bodysegments with connected inertial sensor modules, each bodysegment’s orientation and position can be estimated by, respec-tively, integrating the gyroscope data and double integratingthe accelerometer data in time. However, due to the inherent
integration drift, these uncorrected estimates are only accurate
within a few seconds [8]. By combining the inertial estimates
with other body worn aiding systems, such as a acoustic [9]
or a magnetic tracker [10], unbound integration drift can be
prevented.
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