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- human body motion tracking (1)
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Wearable systems have been applied in various studies as a convenient and efficient solution for
monitoring health and fitness. There is a large number of commercial products in the growing market
of wearable systems that can be worn as wristbands, clasps, or in the form of clothing. However,
these systems only provide general information about the intensity and possibly the type of
user activity, which is not sufficient for monitoring strength and conditioning exercises. To achieve
optimal muscular development and reduce the risk of exercise-related injury, a wearable system
should provide reliable biomechanical details of body movements as well as real-time feedback
during training. In addition, it should be an affordable, comfortable, and easy-to-use platform for
different types of users with different levels of movement intensity and work autonomously over
long periods of time. These requirements impose many challenges on the design of such systems.
This study presents most of these challenges and proposes solutions.
In this work, a low-cost and light-weight tracking suit is designed and developed, which integrates
multiple Inertial measurement units (IMUs). A novel data acquisition approach is proposed to
improve the energy efficiency of the system without the use of additional devices.
Given a valid calibration, IMUs, comprising inertial sensors and magnetometers, can provide accurate
orientation in three dimensions (3D). Unlike the inertial sensors, magnetometer measurements
are easily disturbed by ferromagnetic materials in the vicinity of the sensor, either inside the IMU
casing or in the final mounting position. Therefore, this work proposes a practical method for
in-field magnetometer calibration and alignment to the coordinate system of an IMU. This method
is verified experimentally in terms of magnitude deviation, heading error, plane projections, and
repeatability. The results show a higher accuracy compared to the related works.
Sensor to body calibration is a critical requirement for capturing accurate body movements.
Therefore, a theoretical analysis of an existing method is carried out, showing its limited applicability
for hip and knee joints. On this basis, by applying geometric constraints, a method is
proposed for estimating the positions of three IMUs (mounted on the pelvis, upper leg, and lower
leg) simultaneously. The result of experiments with different types of movements and arbitrary
intensity shows that the proposed method outperforms the previous method.
Moreover, two real-time tracking algorithms based on the extended Kalman filter (EKF) are proposed
for lower body motion estimation. The first approach provides an estimate of the pelvis
orientation. The second approach estimates the position of IMUs and the joint angles with respect
to the pelvis by incorporating the result of body-IMU calibration. The modeling of the biomechanical
constraint compensates for lack of a reliable horizontal reference, e.g. Earth’s magnetic field.
Experiments to track strength exercises such as squat and hip abduction/adduction show promising
results.
In order to finally provide a monitoring application in which users can follow the exercises according
to the instructions and taking into account their health status, this work proposes an approach
for the identification of exercises based on an online template matching algorithm, which detects
the correct performance using a previously recorded exercise in the presence of a supervisor.
Therefore, unlike most identification algorithms, no large datasets are required for training. The
algorithm is optimized here to reduce execution time while maintaining the accuracy. Experiments
show that for the specific application of this study, i.e. squat exercise monitoring, the proposed
method outperforms the related works in optimization of online template matching.