Gait Cycle-Inspired Learning Strategy for Continuous Prediction of Knee Joint Trajectory from sEMG

Predicting lower limb motion intent is vital for controlling exoskeleton robots and prosthetic limbs. Surface electromyography (sEMG) attracts increasing attention in recent years as it enables ahead-of-time prediction of motion intentions before actual movement. However, the estimation performance of human joint trajectory remains a challenging problem due to the inter- and intra-subject variations. The former is related to physiological differences (such as height and weight) and preferred walking patterns of individuals, while the latter is mainly caused by irregular and gait-irrelevant muscle activity. This paper proposes a model integrating two gait cycle-inspired learning strategies to mitigate the challenge for predicting human knee joint trajectory. The first strategy is to decouple knee joint angles into motion patterns and amplitudes former exhibit low variability while latter show high variability among individuals. By learning through separate network entities, the model manages to capture both the common and personalized gait features. In the second, muscle principal activation masks are extracted from gait cycles in a prolonged walk. These masks are used to filter out components unrelated to walking from raw sEMG and provide auxiliary guidance to capture more gait-related features. Experimental results indicate that our model could predict knee angles with the average root mean square error (RMSE) of 3.03(0.49) degrees and 50ms ahead of time. To our knowledge this is the best performance in relevant literatures that has been reported, with reduced RMSE by at least 9.5%

A review on locomotion mode recognition and prediction when using active orthoses and exoskeletons

Understanding how to seamlessly adapt the assistance of lower-limb wearable assistive devices (active orthosis (AOs) and exoskeletons) to human locomotion modes (LMs) is challenging. Several algorithms and sensors have been explored to recognize and predict the users’ LMs. Nevertheless, it is not yet clear which are the most used and effective sensor and classifier configurations in AOs/exoskeletons and how these devices’ control is adapted according to the decoded LMs. To explore these aspects, we performed a systematic review by electronic search in Scopus and Web of Science databases, including published studies from 1 January 2010 to 31 August 2022. Sixteen studies were included and scored with 84.7 ± 8.7% quality. Decoding focused on level-ground walking along with ascent/descent stairs tasks performed by healthy subjects. Time-domain raw data from inertial measurement unit sensors were the most used data. Different classifiers were employed considering the LMs to decode (accuracy above 90% for all tasks). Five studies have adapted the assistance of AOs/exoskeletons attending to the decoded LM, in which only one study predicted the new LM before its occurrence. Future research is encouraged to develop decoding tools considering data from people with lower-limb impairments walking at self-selected speeds while performing daily LMs with AOs/exoskeletons.This work was funded in part by the Fundação para a Ciência e Tecnologia (FCT) with the Reference Scholarship under grant 2020.05711.BD, under the Stimulus of Scientific Employment with the grant 2020.03393.CEECIND, and in part by the FEDER Funds through the COMPETE 2020— Programa Operacional Competitividade e Internacionalização (POCI) and P2020 with the Reference Project SmartOs Grant POCI-01-0247-FEDER-039868, and by FCT national funds, under the national support to R&D units grant, through the reference project UIDB/04436/2020 and UIDP/04436/2020

Decoding kinematic variables from electroencephalographic signals during lower limb mobility protocols

Due to the prevalence of disabilities that affect the lower limbs in the growing population, it seems necessary to provide assistance to those that lost their ability to walk and grant means to those that lack such function. A brain-computer interface (BCI) is a useful technology that includes systems or devices that sense and respond to neural processes, allowing a disabled user to interact with any device by interpreting neurophysiological signals. BCI systems have been based on electroencephalography (EEG) which consists of sensing electrical signals from the brain using noninvasive sensors on the surface of the scalp. BCIs appear to be under two categories: the discrete classification of human tasks and the continuous trajectory reconstruction of kinematics. This research consists on proving that it is possible to make a continuous trajectory reconstruction, also called decodification, from slow cortical potentials, i.e., low frequencies of the EEG signals. In this study, two types of lower limb mobility protocols are proposed: synchronous movements consisting in raising and lowering the foot or the knee within fixed time periods, and asynchronous movements consisting of self-paced continuous flexions and extensions of the knee in a given set of time. The first approach presents evidence of the nonlinear characteristics of the EEG signals during synchronous lower limb mobility protocols. Whereas in the literature, it has only been reported the characterization of these signals between different mental states. To characterize the behavior of the EEG signal, the randomness, complexity, nonstationarity, and nonlinearity of the EEG were studied. Firstly, randomness is analyzed by the Hurst exponent, which also is used to characterize the nonstationary behavior of the EEG signals. In this thesis, the Hurst exponent values of the brain signal show a nonrandom persistent time series, when considering small time windows. The correlation dimension is used as a measure of the complexity of the system related to the number of degrees of freedom, and it is also used to distinguish between random, periodic, or chaotic behavior. The correlation dimension has shown that the underlying system of the brain can range in a relatively low number of dimensions. Finally, the largest Lyapunov exponent is used to confirm the presence of chaos in the underlying dynamics of the time series. In this thesis, the largest Lyapunov exponent values seem to be strictly positive, which is often considered as a definition of deterministic chaos. Implying that the underlying dynamics is indeed nonlinear. With these insights, we could define a nonarbitrary selection of a candidate model (e.g., computational model or neural network) to classify motion tasks and/or to resolve the continuous trajectory reconstruction of lower limb kinematics. This selection could provide reliable and affined methods for EEG-based BCI systems to manipulate assistive devices useful in neuromuscular rehabilitation. The second approach presents additional evidence of decodification using slow cortical potentials. Different electrode arrays and time ranges were tested to compare performances of the reconstruction, proving certain electrodes contribute in greater amount than others to the decodification. The decodification of segmented signals for different types of tasks gave a better performance compared to using a single decoder for the entire signals. Finally, the usage of transformation functions to the EEG signals in order to later be used by the decoder proved there exists combinations of equations that give better results than using the EEG signal directly. In summary, the approach to characterize the EEG signals gives information that can be useful for further studies regarding the mathematical modeling of neural activity during motor tasks. Whereas the second approach shows evidence of improvement for decodification of the kinematics from neural signals. Both results could be starting points to further improve the understanding of neuro-motor tasks and their application of artificial reproduction of movements from EEG signals through a BCI

Systematic Review of Intelligent Algorithms in Gait Analysis and Prediction for Lower Limb Robotic Systems

The rate of development of robotic technologies has been meteoric, as a result of compounded advancements in hardware and software. Amongst these robotic technologies are active exoskeletons and orthoses, used in the assistive and rehabilitative fields. Artificial intelligence techniques are increasingly being utilised in gait analysis and prediction. This review paper systematically explores the current use of intelligent algorithms in gait analysis for robotic control, specifically the control of active lower limb exoskeletons and orthoses. Two databases, IEEE and Scopus, were screened for papers published between 1989 to May 2020. 41 papers met the eligibility criteria and were included in this review. 66.7% of the identified studies used classification models for the classification of gait phases and locomotion modes. Meanwhile, 33.3% implemented regression models for the estimation/prediction of kinematic parameters such as joint angles and trajectories, and kinetic parameters such as moments and torques. Deep learning algorithms have been deployed in ∼15% of the machine learning implementations. Other methodological parameters were reviewed, such as the sensor selection and the sample sizes used for training the models

ヒト二足歩行の神経制御機序 : 大脳皮質と脊髄神経回路の役割

学位の種別: 課程博士審査委員会委員 : （主査）東京大学教授 中澤 公孝, 東京大学准教授 柳原 大, 東京大学准教授 工藤 和俊, 東京大学准教授 吉岡 伸輔, 上武大学教授 関口 浩文University of Tokyo(東京大学

Biomechatronics: Harmonizing Mechatronic Systems with Human Beings

This eBook provides a comprehensive treatise on modern biomechatronic systems centred around human applications. A particular emphasis is given to exoskeleton designs for assistance and training with advanced interfaces in human-machine interaction. Some of these designs are validated with experimental results which the reader will find very informative as building-blocks for designing such systems. This eBook will be ideally suited to those researching in biomechatronic area with bio-feedback applications or those who are involved in high-end research on manmachine interfaces. This may also serve as a textbook for biomechatronic design at post-graduate level

Extracting Kinematic Parameters for Monkey Bipedal Walking from Cortical Neuronal Ensemble Activity

The ability to walk may be critically impacted as the result of neurological injury or disease. While recent advances in brain–machine interfaces (BMIs) have demonstrated the feasibility of upper-limb neuroprostheses, BMIs have not been evaluated as a means to restore walking. Here, we demonstrate that chronic recordings from ensembles of cortical neurons can be used to predict the kinematics of bipedal walking in rhesus macaques – both offline and in real time. Linear decoders extracted 3D coordinates of leg joints and leg muscle electromyograms from the activity of hundreds of cortical neurons. As more complex patterns of walking were produced by varying the gait speed and direction, larger neuronal populations were needed to accurately extract walking patterns. Extraction was further improved using a switching decoder which designated a submodel for each walking paradigm. We propose that BMIs may one day allow severely paralyzed patients to walk again

Down-Conditioning of Soleus Reflex Activity using Mechanical Stimuli and EMG Biofeedback

Spasticity is a common syndrome caused by various brain and neural injuries, which can severely impair walking ability and functional independence. To improve functional independence, conditioning protocols are available aimed at reducing spasticity by facilitating spinal neuroplasticity. This down-conditioning can be performed using different types of stimuli, electrical or mechanical, and reflex activity measures, EMG or impedance, used as biofeedback variable. Still, current results on effectiveness of these conditioning protocols are incomplete, making comparisons difficult. We aimed to show the within-session task- dependent and across-session long-term adaptation of a conditioning protocol based on mechanical stimuli and EMG biofeedback. However, in contrast to literature, preliminary results show that subjects were unable to successfully obtain task-dependent modulation of their soleus short-latency stretch reflex magnitude