Estimating Lower Limb Kinematics Using a Reduced Wearable Sensor Count

Goal:
This paper presents an algorithm for accurately estimating pelvis, thigh, and shank kinematics during walking using only three wearable inertial sensors.
Methods:
The algorithm makes novel use of a constrained Kalman filter (CKF). The algorithm iterates through the prediction (kinematic equation), measurement (pelvis position pseudo-measurements, zero velocity update, flat-floor assumption, and covariance limiter), and constraint update (formulation of hinged knee joints and ball-and-socket hip joints).
Results:
Evaluation of the algorithm using an optical motion capture-based sensor-to-segment calibration on nine participants (7 men and 2 women, weight
$\text{63.0} \pm \text{6.8}$
kg, height
$\text{1.70} \pm \text{0.06}$
m, age
$\text{24.6} \pm \text{3.9}$
years old), with no known gait or lower body biomechanical abnormalities, who walked within a
$\text{4} \times \text{4}$
m
$^2$
capture area shows that it can track motion relative to the mid-pelvis origin with mean position and orientation (no bias) root-mean-square error (RMSE) of
$\text{5.21} \pm \text{1.3}$
cm and
$\text{16.1} \pm \text{3.2}^\circ$
, respectively. The sagittal knee and hip joint angle RMSEs (no bias) were
$\text{10.0} \pm \text{2.9}^\circ$
and
$\text{9.9} \pm \text{3.2}^\circ$
, respectively, while the corresponding correlation coefficient (CC) values were
$\text{0.87} \pm \text{0.08}$
and
$\text{0.74} \pm \text{0.12}$
.
Conclusion:
The CKF-based algorithm was able to track the 3D pose of the pelvis, thigh, and shanks using only three inertial sensors worn on the pelvis and shanks.
Significance:
Due to the Kalman-filter-based algorithm's low computation cost and the relative convenience of using only three wearable sensors, gait parameters can be computed in real-time and remotely for long-term gait monitoring. Furthermore, the system can be used to inform real-time gait assistive devices.