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Biomechanics And Motor Control Of Human Movement

Brain-machine interfaces (BMIs) can be used to decode brain activity into commands to control external devices. This paper presents the decoding of intuitive upper extremity imagery for multi-directional arm reaching tasks in three-dimensional (3D) environments. We designed and implemented an experimental environment in which electroencephalogram (EEG) signals can be acquired for movement execution and imagery. Fifteen subjects participated in our experiments. We proposed a multi-directional convolution neural network-bidirectional long short-term memory network (MDCBN)-based deep learning framework. The decoding performances for six directions in 3D space were measured by the correlation coefficient (CC) and the normalized root mean square error (NRMSE) between predicted and baseline velocity profiles. The grand-averaged CCs of multi-direction were 0.47 and 0.45 for the execution and imagery sessions, respectively, across all subjects. The NRMSE values were below 0.2 for both sessions. Furthermore, in this study, the proposed MDCBN was evaluated by two online experiments for real-time robotic arm control, and the grand-averaged success rates were approximately 0.60 (±0.14) and 0.43 (±0.09), respectively. Hence, we demonstrate the feasibility of intuitive robotic arm control based on EEG signals for real-world environments.

Brain-Controlled Robotic Arm System Based on Multi-Directional CNN-BiLSTM Network Using EEG Signals

Background Many lower-limb exoskeletons have been developed to assist gait, exhibiting a large range of control methods. The goal of this paper is to review and classify these control strategies, that determine how these devices interact with the user. Methods In addition to covering the recent publications on the control of lower-limb exoskeletons for gait assistance, an effort has been made to review the controllers independently of the hardware and implementation aspects. The common 3-level structure (high, middle, and low levels) is first used to separate the continuous behavior (mid-level) from the implementation of position/torque control (low-level) and the detection of the terrain or user's intention (high-level). Within these levels, different approaches (functional units) have been identified and combined to describe each considered controller. Results 291 references have been considered and sorted by the proposed classification. The methods identified in the high-level are manual user input, brain interfaces, or automatic mode detection based on the terrain or user's movements. In the mid-level, the synchronization is most often based on manual triggers by the user, discrete events (followed by state machines or time-based progression), or continuous estimations using state variables. The desired action is determined based on position/torque profiles, model-based calculations, or other custom functions of the sensory signals. In the low-level, position or torque controllers are used to carry out the desired actions. In addition to a more detailed description of these methods, the variants of implementation within each one are also compared and discussed in the paper. Conclusions By listing and comparing the features of the reviewed controllers, this work can help in understanding the numerous techniques found in the literature. The main identified trends are the use of pre-defined trajectories for full-mobilization and event-triggered (or adaptive-frequency-oscillator-synchronized) torque profiles for partial assistance. More recently, advanced methods to adapt the position/torque profiles online and automatically detect terrains or locomotion modes have become more common, but these are largely still limited to laboratory settings. An analysis of the possible underlying reasons of the identified trends is also carried out and opportunities for further studies are discussed.

/pdf/review-of-control-strategies-for-lower-limb-exoskeletons-to-2rtwoa8l16.pdf

Review of control strategies for lower-limb exoskeletons to assist gait

Urban intelligence is an emerging concept which guides a series of infrastructure developments in modern smart cities. Human-computer interaction (HCI) is the interface between residents and the smart cities, it plays a key role in bridging the gap in applicating information technologies in modern cities. Hand gestures have been widely acknowledged as a promising HCI method, recognition human hand gestures using surface electromyogram (sEMG) is an important research topic in the application of sEMG. However, state-of-the-art signal processing technologies are not robust in feature extraction and pattern recognition with sEMG signals, several technical problems are still yet to be solved. For example, how to maintain the availability of myoelectric control in intermittent use, since pattern recognition qualities are greatly affected by time variability, but it is unavoidable during daily use. How to ensure the reliability and effectiveness of myoelectric control system also important in developing a good human-machine interface. In this paper, linear discriminant analysis (LDA) and extreme learning machine (ELM) are implemented in hand gesture recognition system, which is able to reduce the redundant information in sEMG signals and improve recognition efficiency and accuracy. The characteristic map slope (CMS) is extracted by using the feature re-extraction method because CMS can strengthen the relationship of features cross time domain and enhance the feasibility of cross-time identification. This study is focusing on optimizing the time differences in sEMG pattern recognition, the experimental results are beneficial to reducing the time differences in gesture recognition based on sEMG. The recognition framework proposed in this paper can enhance the generalization ability of HCI in the long term use and it also simplifies the data collection stage before training the device ready for daily use, which is of great significance to improve the time generalization performance of an HCI system.

/pdf/intelligent-human-computer-interaction-based-on-surface-emg-2ranap9enp.pdf

Intelligent Human-Computer Interaction Based on Surface EMG Gesture Recognition

A review of the key technologies for sEMG-based human-robot interaction systems

To improve the efficacy of robotic exoskeleton-based rehabilitation training, active joint torque of subjects should be detected. This paper presents a practical and adaptive method to estimate active joint torque using electromyography (EMG) signals for a custom lower limb robotic exoskeleton with two degrees of freedom (DOFs). This estimator, constructed of radial basis function neural networks (RBFNNs), was used to form an extended Slotine-Li controller. This extended controller eliminated the need for the calibration for EMG-torque model. The adaptive control of exoskeleton and adaptive estimation of active joint torque were performed within the same framework. By introducing a two-step learning strategy into the controller, the estimator can continuously adapt to changes in the EMG-torque model, and overcome the problems due to the time-varying property of EMG signals. Simulation and experimental results show that the presented estimator can predict the active joint torque of subjects in a practical and adaptive manner. Additionally, the accurate movement control of exoskeleton is also guaranteed. At present, the experiments are conducted only for the swing phase due to the lack of the force plate sensors.

A Practical and Adaptive Method to Achieve EMG-Based Torque Estimation for a Robotic Exoskeleton

Shared control of a robotic arm using non-invasive brain–computer interface and computer vision guidance

Robotic exoskeletons for physical rehabilitation have been utilized for retraining patients suffering from paraplegia and enhancing motor recovery in recent years However, users are not voluntarily involved in most systems This paper aims to develop a locomotion trainer with multiple gait patterns, which can be controlled by the active motion intention of users A multimodal human–robot interaction (HRI) system is established to enhance subject’s active participation during gait rehabilitation, which includes cognitive HRI (cHRI) and physical HRI (pHRI) The cHRI adopts brain–computer interface based on steady-state visual evoked potential The pHRI is realized via admittance control based on electromyography A central pattern generator is utilized to produce rhythmic and continuous lower joint trajectories, and its state variables are regulated by cHRI and pHRI A custom-made leg exoskeleton prototype with the proposed multimodal HRI is tested on healthy subjects and stroke patients The results show that voluntary and active participation can be effectively involved to achieve various assistive gait patterns

Toward Multimodal Human–Robot Interaction to Enhance Active Participation of Users in Gait Rehabilitation

It is evident that the dominant therapy of functional electrical stimulation (FES) for stroke rehabilitation suffers from heavy dependency on therapists experience and lack of feedback from patients’ status, which decrease the patients’ voluntary participation, reducing the rehabilitation efficacy. This paper proposes a closed loop FES system using surface electromyography (sEMG) bias feedback from bilateral arms for enhancing upper-limb stroke rehabilitation. This wireless portable system consists of sEMG data acquisition and FES modules, the former is used to measure and analyze the subject’s bilateral arm motion intention and neuromuscular states in terms of their sEMG, the latter of multi-channel FES output is controlled via the sEMG bias of the bilateral arms. The system has been evaluated with experiments proving that the system can achieve 39.9 dB signal-to-noise ratio in the lab environment, outperforming existing similar systems. The results also show that voluntary and active participation can be effectively employed to achieve different FES intensity for FES-assisted hand motions, demonstrating the potential for active stroke rehabilitation.

sEMG Bias-Driven Functional Electrical Stimulation System for Upper-Limb Stroke Rehabilitation

This paper introduces a progressive assist-as-needed (pAAN) controller into our custom-made lower limb exoskeleton system. This control strategy can enhance the active participation of subjects. The controller can dynamically estimate a subject’s input (voluntary joint torque) based on electromyography (EMG) without calibrations. The EMG-torque relationship learning is unsupervised. The zero-error estimation of the subject’s input is guaranteed by a progressive learning strategy. The adaptive controller adjusts the control inputs of motors to complete predefined trajectories. Online torque estimation and adaptive motion control are both realised in the pAAN controller. Additionally, some practical problems of EMG application, caused by time-varying property of EMG signals and electrode displacement, would be avoided. From the simulation and experimental studies, our pAAN controller can predict the subject’s input well, and the exoskeleton helps subjects move precisely. Active participation of subjects is achieved during training.

Electromyography-Driven Progressive Assist-as-Needed Control for Lower Limb Exoskeleton

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