  The fast-growing techniques of measuring and fusing multi-modal biomedical
signals enable advanced motor intent decoding schemes of lowerlimb
exoskeletons, meeting the increasing demand for rehabilitative or assistive
applications of take-home healthcare. Challenges of exoskeletons motor intent
decoding schemes remain in making a continuous prediction to compensate for the
hysteretic response caused by mechanical transmission. In this paper, we solve
this problem by proposing an ahead of time continuous prediction of lower limb
kinematics, with the prediction of knee angles during level walking as a case
study. Firstly, an end-to-end kinematics prediction network(KinPreNet),
consisting of a feature extractor and an angle predictor, is proposed and
experimentally compared with features and methods traditionally used in
ahead-of-time prediction of gait phases. Secondly, inspired by the
electromechanical delay(EMD), we further explore our algorithm's capability of
compensating response delay of mechanical transmission by validating the
performance of the different sections of prediction time. And we experimentally
reveal the time boundary of compensating the hysteretic response. Thirdly, a
comparison of employing EMG signals or not is performed to reveal the EMG and
kinematic signals collaborated contributions to the continuous prediction.
During the experiments, EMG signals of nine muscles and knee angles calculated
from inertial measurement unit (IMU) signals are recorded from ten healthy
subjects. To the best of our knowledge, this is the first study of continuously
predicting lower-limb kinematics in an ahead-of-time manner based on the
electromechanical delay (EMD).
