Jimson Ngeo | Nara Institute of Science and Technology (NAIST) (original) (raw)
Address: Nara, Nara, Japan
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Patients suffering from loss of hand functions caused by stroke and other spinal cord injuries ha... more Patients suffering from loss of hand functions caused by stroke and other spinal cord injuries have driven a surge in the development of wearable assistive devices in recent years. In this paper, we present a system made up of a low-profile, optimally designed finger exoskeleton continuously controlled by a user's surface electromyographic (sEMG) signals. The mechanical design is based on an optimal four-bar linkage that can model the finger's irregular trajectory due to the finger's varying lengths and changing instantaneous center. The desired joint angle positions are given by the predictive output of an artificial neural network with an EMG-to-Muscle Activation model that parameterizes electromechanical delay (EMD). After confirming good prediction accuracy of multiple finger joint angles we evaluated an index finger exoskeleton by obtaining a subject's EMG signals from the left forearm and using the signal to actuate a finger on the right hand with the exoskeleton. Our results show that our sEMG-based control strategy worked well in controlling the exoskeleton, obtaining the intended positions of the device, and that the subject felt the appropriate motion support from the device.
Surface electromyography (sEMG) signals are often used in many robot and rehabilitation applicati... more Surface electromyography (sEMG) signals are often used in many robot and rehabilitation applications because these reflect the motor intention of users. However, inherent problems such as electromechanical delay are present in such applications. Here, we present a method to estimate finger joint angles using a neural network with inputs obtained from an EMG-to-Activation model which parameterizes this delay. Our results show overall root-mean-square errors of 5-12% between the predicted and actual joint angles. We also show results when the proposed muscle activation input is used compared to using features used by other related studies. Finally, we compare the use of a neural network to a Gaussian Process, which is a popular nonparametric Bayesian regressor that could efficiently give better prediction in this setting.
Prediction of dynamic hand finger movements has many clinical and engineering applications in the... more Prediction of dynamic hand finger movements has many clinical and engineering applications in the control of human interface devices such as those used in virtual reality control, robot prosthesis and rehabilitation aids. Surface electromyography (sEMG) signals have often been used in the mentioned applications because these reflect the motor intention of users very well. In this study, we present a method to estimate the finger joint angles of a hand from sEMG signals that considers electromechanical delay (EMD), which is inherent when EMG signals are captured alongside motion data. We use the muscle activation obtained from the sEMG signals as input to a neural network. In this muscle activation model, the EMD is parameterized and automatically obtained through optimization. With this method, we can predict the finger joint angles with sEMG signals in both periodic and nonperiodic free movements of the flexion and extension movement of the fingers. Our results show correlation as high as 0.92 between the actual and predicted metacarpophalangeal (MCP) joint angles for periodic finger flexion movements, and as high as 0.85 for nonperiodic movements, which are more dynamic and natural.
Patients suffering from loss of hand functions caused by stroke and other spinal cord injuries ha... more Patients suffering from loss of hand functions caused by stroke and other spinal cord injuries have driven a surge in the development of wearable assistive devices in recent years. In this paper, we present a system made up of a low-profile, optimally designed finger exoskeleton continuously controlled by a user's surface electromyographic (sEMG) signals. The mechanical design is based on an optimal four-bar linkage that can model the finger's irregular trajectory due to the finger's varying lengths and changing instantaneous center. The desired joint angle positions are given by the predictive output of an artificial neural network with an EMG-to-Muscle Activation model that parameterizes electromechanical delay (EMD). After confirming good prediction accuracy of multiple finger joint angles we evaluated an index finger exoskeleton by obtaining a subject's EMG signals from the left forearm and using the signal to actuate a finger on the right hand with the exoskeleton. Our results show that our sEMG-based control strategy worked well in controlling the exoskeleton, obtaining the intended positions of the device, and that the subject felt the appropriate motion support from the device.
Surface electromyography (sEMG) signals are often used in many robot and rehabilitation applicati... more Surface electromyography (sEMG) signals are often used in many robot and rehabilitation applications because these reflect the motor intention of users. However, inherent problems such as electromechanical delay are present in such applications. Here, we present a method to estimate finger joint angles using a neural network with inputs obtained from an EMG-to-Activation model which parameterizes this delay. Our results show overall root-mean-square errors of 5-12% between the predicted and actual joint angles. We also show results when the proposed muscle activation input is used compared to using features used by other related studies. Finally, we compare the use of a neural network to a Gaussian Process, which is a popular nonparametric Bayesian regressor that could efficiently give better prediction in this setting.
Prediction of dynamic hand finger movements has many clinical and engineering applications in the... more Prediction of dynamic hand finger movements has many clinical and engineering applications in the control of human interface devices such as those used in virtual reality control, robot prosthesis and rehabilitation aids. Surface electromyography (sEMG) signals have often been used in the mentioned applications because these reflect the motor intention of users very well. In this study, we present a method to estimate the finger joint angles of a hand from sEMG signals that considers electromechanical delay (EMD), which is inherent when EMG signals are captured alongside motion data. We use the muscle activation obtained from the sEMG signals as input to a neural network. In this muscle activation model, the EMD is parameterized and automatically obtained through optimization. With this method, we can predict the finger joint angles with sEMG signals in both periodic and nonperiodic free movements of the flexion and extension movement of the fingers. Our results show correlation as high as 0.92 between the actual and predicted metacarpophalangeal (MCP) joint angles for periodic finger flexion movements, and as high as 0.85 for nonperiodic movements, which are more dynamic and natural.