Hand Grasp Motion Intention Recognition Based on High-Density Electromyography in Chronic Stroke Patients.

Stroke is a debilitating condition that leads to a loss of motor function, inability to perform daily life activities, and ultimately worsening quality of life. Robot-based rehabilitation is a more effective method than conventional rehabilitation but needs to accurately recognize the patient's intention so that the robot can assist the patient's voluntary motion. This study focuses on recognizing hand grasp motion intention using high-density electromyography (HD-EMG) in patients with chronic stroke. The study was conducted with three chronic stroke patients and involved recording HD-EMG signals from the muscles involved in hand grasp motions. The adaptive onset detection algorithm was used to accurately identify the start of hand grasp motions accurately, and a convolutional neural network (CNN) was trained to classify the HD-EMG signals into one of four grasping motions. The average true positive and false positive rates of the grasp onset detection on three subjects were 91.6% and 9.8%, respectively, and the trained CNN classified the grasping motion with an average accuracy of 76.3%. The results showed that using HD-EMG can provide accurate hand grasp motion intention recognition in chronic stroke patients, highlighting the potential for effective robot-based rehabilitation.

Development of a High-SNR Stochastic sEMG Processor in a Multiple Muscle Elbow Joint.

In the robotics and rehabilitation engineering fields, surface electromyography (sEMG) signals have been widely studied to estimate muscle activation and utilized as control inputs for robotic devices because of their advantageous noninvasiveness. However, the stochastic property of sEMG results in a low signal-to-noise ratio (SNR) and impedes sEMG from being used as a stable and continuous control input for robotic devices. As a traditional method, time-average filters (e.g., low-pass filters) can improve the SNR of sEMG, but time-average filters suffer from latency problems, making real-time robot control difficult. In this study, we propose a stochastic myoprocessor using a rescaling method extended from a whitening method used in previous studies to enhance the SNR of sEMG without the latency problem that affects traditional time average filter-based myoprocessors. The developed stochastic myoprocessor uses 16 channel electrodes to use the ensemble average, 8 of which are used to measure and decompose deep muscle activation. To validate the performance of the developed myoprocessor, the elbow joint is selected, and the flexion torque is estimated. The experimental results indicate that the estimation results of the developed myoprocessor show an RMS error of 6.17[%], which is an improvement with respect to previous methods. Thus, the rescaling method with multichannel electrodes proposed in this study is promising and can be applied in robotic rehabilitation engineering to generate rapid and accurate control input for robotic devices.

Enhanced torque estimation method from multi-channel surface electromyography compensating electrode location variation.

Recognizing human intentions from the human counterpart is very important in human-robot interaction applications. Surface electromyography(sEMG) has been considered as a potential source for motion intention because the signal represents the on-set timing and amplitude of muscle activation. It is also reported that sEMG has the advantage of knowing body movements ahead of actual movement. However, sEMG based applications suffer from electrode location variation because sEMG shows different characteristics whenever the skin condition is different. They need to recreate the estimation model if electrodes are attached to different locations or conditions. In this paper, we developed a sEMG torque estimation model for electrode location variation. A decomposition model of sEMG signals was developed to discriminate the muscle source signals for electrode location variation, and we verified this model without making a new torque estimation model. Torque estimation accuracy using the proposed method was increased by 24.8% and torque prediction accuracy was increased by 47.7% for the electrode location variation in comparison with the method without decomposition. Therefore, the proposed sEMG decomposition method showed an enhancement in torque estimation for electrode location variation.

Predicting Walking Intentions using sEMG and Mechanical sensors for various environment.

Predicting the motion intentions of a user is very challenging when controlling an exoskeleton robot. When only a mechanical sensor is used, a change in the motion is detected during the user's movement. An electromyographic (EMG) signal, which is a biological signal, is detected by the activation of the muscles before the actual movement of a person. Using the EMG signal, the motion intention can be identified before the actual movement, and the delay in time in controlling the exoskeletal robot can be shortened to reduce the resistance felt by the user. In this paper, the surface electromyographic (sEMG) signal is used together with a mechanical sensor to identify the walking environment according to the walking gait cycle. In the classification, the combination of sensors was varied, and information from one leg and two legs was analyzed by the different gait periods before and after heel contact and toe off. As a result of the classification into three sensor combinations, sEMG, kinetic, and kinematic sensors, at the pre heel contact time before walking, a 96.8% and 98.6% accuracy was obtained for information from one and two legs, respectively. In the same gait environment, it was shown that the gait prediction can be performed based on the time unit by dividing the time interval before starting the gait. An average accuracy of 84.4% was obtained when the time was divided by the environment in intervals of 100ms before heel contact, and the average was 90.9% when it was divided by an interval of 200ms before heel contact.