Evaluation of methods for extraction of the volitional EMG in dynamic hybrid muscle activation

Machine-learning-based coordination of powered ankle-foot orthosis and functional electrical stimulation for gait control

This study proposes a novel gait rehabilitation method that uses a hybrid system comprising a powered ankle-foot orthosis (PAFO) and FES, and presents its coordination control. The developed system provides assistance to the ankle joint in accordance with the degree of volitional participation of patients with post-stroke hemiplegia. The PAFO adopts the desired joint angle and impedance profile obtained from biomechanical simulation. The FES patterns of the tibialis anterior and soleus muscles are derived from predetermined electromyogram patterns of healthy individuals during gait and personalized stimulation parameters. The CNN-based estimation model predicts the volitional joint torque from the electromyogram of the patient, which is used to coordinate the contributions of the PAFO and FES. The effectiveness of the developed hybrid system was tested on healthy individuals during treadmill walking with and without considering the volitional muscle activity of the individual. The results showed that consideration of the volitional muscle activity significantly lowers the energy consumption by the PAFO and FES while providing adaptively assisted ankle motion depending on the volitional muscle activities of the individual. The proposed system has potential use as an assist-as-needed rehabilitation system, where it can improve the outcome of gait rehabilitation by inducing active patient participation depending on the stage of rehabilitation.

An Adaptive Spatial Filtering Method for Multi-Channel EMG Artifact Removal During Functional Electrical Stimulation With Time-Variant Parameters

Removing the stimulation artifacts evoked by the functional electrical stimulation (FES) in electromyogram (EMG) signals is a challenge. Previous researches on stimulation artifact removal have focused on FES modulation with time-constant parameters, which has limitations when there are time-variant parameters. Therefore, considering the synchronism of muscle activation induced by FES and the asynchronism of muscle activation induced by proprioceptive nerves, we proposed a novel adaptive spatial filtering method called G-S-G. It entails fusing the Gram-Schmidt orthogonalization (G-S) and Grubbs criterion (G) algorithms to remove the FES-evoked stimulation artifacts in multi-channel EMG signals. To verify this method, we constructed a series of simulation data by fusing the FES signal with time-variant parameters and the voluntary EMG (vEMG) signal, and applied the G-S-G method to remove any FES artifacts from the simulation data. After that, we calculated the root mean square (RMS) value for both preprocessed simulation data and the vEMG data, and then compared them. The simulation results showed that the G-S-G method was robust and effective at removing FES artifacts in simulated EMG signals, and the correlation coefficient between the preprocessed EMG data and the recorded vEMG data yielded a good performance, up to 0.87. Furthermore, we applied the proposed method to the experimental EMG data with FES-evoked stimulation artifact, and also achieved good performance with both the time-constant and time-variant parameters. This study provides a new and accessible approach to resolving the problem of removing FES-evoked stimulation artifacts.

Development of a Novel Robotic Rehabilitation System With Muscle-to-Muscle Interface

In this study, we developed a novel robotic system with a muscle-to-muscle interface to enhance rehabilitation of post-stroke patients. The developed robotic rehabilitation system was designed to provide patients with stage appropriate physical rehabilitation exercise and muscular stimulation. Unlike the position-based control of conventional bimanual robotic therapies, the developed system stimulates the activities of the target muscles, as well as the joint movements of the paretic limb. The robot-assisted motion and the electrical stimulation on the muscles of the paretic side are controlled by on-line comparison of the motion and the muscle activities between the paretic and unaffected sides. With the developed system, the rehabilitation exercise can be customized and modulated depending on the patient’s stage of motor recovery after stroke. The system can be operated in three different modes allowing both passive and active exercises. The effectiveness of the developed system was verified with healthy human subjects, where the subjects were paired to serve as the unaffected side and the paretic side of a hemiplegic patient.

Hardware System for Real-Time EMG Signal Acquisition and Separation Processing during Electrical Stimulation

The study aimed to develop a real-time electromyography (EMG) signal acquiring and processing device that can acquire signal during electrical stimulation. Since electrical stimulation output can affect EMG signal acquisition, to integrate the two elements into one system, EMG signal transmitting and processing method has to be modified. The whole system was designed in a user-friendly and flexible manner. For EMG signal processing, the system applied Altera Field Programmable Gate Array (FPGA) as the core to instantly process real-time hybrid EMG signal and output the isolated signal in a highly efficient way. The system used the power spectral density to evaluate the accuracy of signal processing, and the cross correlation showed that the delay of real-time processing was only 250 μs.

Methods for Dynamic Characterization of the Major Muscles Activating the Lower Limb Joints in Cycling Motion

The functional activation, through electrical stimulation, of the lower limb consisting of several deficient muscles requires well-patterned and coordinated activation of these muscles. This study presents a method for characterizing the parameters of the major muscle groups controlling the ankle and knee joints in cycling motion, the latter having particular significance in the rehabilitation of locomotion. To lower mechanical indeterminacy in the joints the system is reduced by grouping the muscles acting in synergism. The joint torques were calculated by inverse dynamics methods from cycling motion data, including kinematics and foot/pedal reaction loads (forces, moments). The mechanical indeterminacy was resolved by applying optimization criteria and the individual muscle torques were parceled-out from the joint torques. System identification of the individual muscles, part of which being bi-articular, in this non-isometric condition was performed from the relationship between the evaluated force and the measured EMG of each the muscles, using both first and second order linear transfer functions. Feasibility of the presented method was demonstrated through the computation of the coefficients of the muscles involved and validating the results on the experimental data obtained from one subject.

Foot center of pressure manipulation and gait therapy influence lower limb muscle activation in patients with osteoarthritis of the knee

BACKGROUND

Foot center of pressure (COP) manipulation has been associated with improved gait patterns. The purpose of this study was to determine lower limb muscle activation changes in knee osteoarthritis patients, both immediately after COP manipulation and when COP manipulation was combined with continuous gait therapy (AposTherapy).

METHODS

Fourteen females with medial compartment knee osteoarthritis underwent EMG analyzes of key muscles of the leg. In the initial stage, trials were carried out at four COP positions. Following this, gait therapy was initiated for 3 months. The barefoot EMG was compared before and after therapy.

RESULTS

The average EMG varied significantly with COP in at least one phase of stance in all examined muscles of the less symptomatic leg and in three muscles of the more symptomatic leg. After training, a significant increase in average EMG was observed in most muscles. Most muscles of the less symptomatic leg showed significantly increased peak EMG. Activity duration was shorter for all muscles of the less symptomatic leg (significant in the lateral gastrocnemius) and three muscles of the more symptomatic leg (significant in the biceps femoris). These results were associated with reduced pain, increased function and improved spatiotemporal parameters.

CONCLUSIONS

COP manipulation influences the muscle activation patterns of the leg in patients with knee osteoarthritis. When combined with a therapy program, muscle activity increases and activity duration decreases.

Reciprocal EMG controlled FES for pathological tremor suppression of forearm

A novel assistive system is designed to suppress pathological tremor of forearm via functional electrical stimulation (FES). It aims to attenuate the tremor with the minimum effect on the voluntary movement. Surface electromyograph (EMG) is adopted as the sensing feedback information to regulate FES. A two-stage filter is proposed to process the raw EMG signal. The first stage removes the artifacts in the raw EMG signal contaminated by FES. The second stage filter separates the high frequency tremulous EMG from the low frequency voluntary components. The extracted tremor EMG of biceps and triceps will then be used as control input in the FES controller to stimulate the two muscles reciprocally. This paper presents the design and implementation of the first stage of the two-stage filter. Experiments on healthy subjects have shown promising results.