IEEE Robotics & Automation Magazine - September 2022 - 41

platform can also be used as the interface for remote maintenance
and fault diagnosis.
Additionally, the platform stores healthy subjects' data,
that is, the body parameters (e.g., height, age, wrist width,
and so on) and gait trajectories under different walking
speeds. By referring to the large data set, a neural network
(NN) is constructed in BEAR-H for the generation of individualized
gaits, under the structure shown in Figure 5.
The overall structure consists of four modules: input, feature
extraction, mapping, and output, and the workflow
can be summarized as follows:
●
The NN is constructed to realize a probabilistic mapping
from the body parameters of patients to the features
of gait trajectory. The features are designed as a
series of coefficients of Fourier transform of the gait trajectory,
which can then be reconstructed by computing
the Fourier inversion with the reference to the predicted
features (i.e., outputs of NNs).
●
The training data set is collected from healthy subjects.
Specifically, the gait trajectory of the subject is recorded
using anthropometry methods and set as the ground
truth. The multiscale body parameters (e.g., height,
weight, leg length, and so forth) are measured and then
fed into the NN. The NN outputs the predicted gait features,
which are compared with the ground truth to
update the weights and train the NN.
●
The well-trained NN receives body parameters of the
patient and then generates an individualized gait trajectory
(i.e., the representation with gait features) for a specific
subject corresponding to the trajectory of a healthy
subject with similar body parameters. As the body
parameters vary for different subjects, the individualized
gait is also varied and automatically adjusted by the
trained NN.
Motion Capturing
Note that the backbone network is based on a loglinearized
Gaussian mixture network, and the training and
implementation phases are illustrated in Figure 6. Therefore,
the individualized gait is actually generated by estimating
the posterior probability of the gait in a data-driven way,
given the specific body parameters of a patient. Such a formulation
explores the underlying relationship between the
body parameters and gait; hence, it can better suit the patient
to guarantee his/her natural motion after rehabilitation.
Remark 1
Note that the subject-specific gait is related with EMG signals
and the strength of a human limb. However, a stroke
patient has significantly different EMG signals and strength
data compared with the situation when he/she was in
healthy condition. Therefore, that information is not used
as the input to retrieve the original healthy gait pattern.
Clinical Trials
Clinical trials are carried out to study the performance of
BEAR-H, where the patient wears the robot and walks in a
straight line in a hospital environment (see Figure 7). Before
clinical trials, the patients are informed about the purposes
of the study and sign informed consent. Then, some
preliminary training is performed to help patients get
used to wearing the robot, which ensures that the patient
has the ability to participate in the subsequent formal trials;
the preliminary training lasts for three to five days.
Next, the formal trial is performed for five days each
week, and the patient walks by wearing the robot twice
per day, with each walking trial lasting for approximately
30 min. An experimental protocol was approved on
23 May 2019 by the Ethics Committee of The Second
Affiliated Hospital, Shenzhen University. A clinical process
Joint
Input
Anthropometry
Data
Measurement
Healthy
Subjects
Body Parameters
(Training Input)
Body
Parameters
Patients
Probabilistic Mapping
Figure 5. The overall structure of gait generation consists of four modules: input, feature extraction, mapping, and output.
SEPTEMBER 2022 * IEEE ROBOTICS & AUTOMATION MAGAZINE *
41
Log-Linearized
Gaussian Mixture
Network
Angles
Feature Extraction
Ground
Truth
Output
Gait
Features
Predicted
Output
Predicted
Features
Inversion
Fourier
Transform
Coefficients
Individualized
Gait

IEEE Robotics & Automation Magazine - September 2022

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