TY - JOUR
T1 - Continuous Prediction of Lower-Limb Kinematics from Multi-Modal Biomedical Signals
AU - Yi, Chunzhi
AU - Jiang, Feng
AU - Zhang, Shengping
AU - Guo, Hao
AU - Yang, Chifu
AU - Ding, Zhen
AU - Wei, Baichun
AU - Lan, Xiangyuan
AU - Zhou, Huiyu
N1 - Publisher Copyright:
© 1991-2012 IEEE.
PY - 2022/5
Y1 - 2022/5
N2 - The fast-growing techniques of measuring and fusing multi-modal biomedical signals enable advanced motor intent decoding schemes of lower-limb 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),1 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. Our algorithm can predict knee angles with the averaged RMSE of 3.98 deg which is better than the 15.95-deg averaged RMSE of utilizing the traditional methods of ahead-of-time prediction. The best prediction time is in the interval of 27ms and 108ms. 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).
AB - The fast-growing techniques of measuring and fusing multi-modal biomedical signals enable advanced motor intent decoding schemes of lower-limb 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),1 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. Our algorithm can predict knee angles with the averaged RMSE of 3.98 deg which is better than the 15.95-deg averaged RMSE of utilizing the traditional methods of ahead-of-time prediction. The best prediction time is in the interval of 27ms and 108ms. 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).
KW - Electromechanical Delay
KW - Kinematics Prediction Electromyography
KW - Long Short-Term Memory
KW - Reponse Delay Compensation
UR - http://www.scopus.com/inward/record.url?scp=85103909421&partnerID=8YFLogxK
U2 - 10.1109/TCSVT.2021.3071461
DO - 10.1109/TCSVT.2021.3071461
M3 - Journal article
AN - SCOPUS:85103909421
SN - 1051-8215
VL - 32
SP - 2592
EP - 2602
JO - IEEE Transactions on Circuits and Systems for Video Technology
JF - IEEE Transactions on Circuits and Systems for Video Technology
IS - 5
ER -