Suncheol Kwon

Real-time pinch force estimation by surface electromyography using an artificial neural network

The palmar pinch force estimation is highly relevant not only in biomechanical studies, the analysis of sports activities, and ergonomic design analyses but also in clinical applications such as rehabilitation, in which information about muscle forces influences the physicians decisions on diagnosis and treatment. Force transducers have been used for such purposes, but they are restricted to grasping points and inevitably interfere with the human haptic sense because fingers cannot directly touch the environmental surface. We propose an estimation method of the palmar pinch force using surface electromyography (SEMG). Three myoelectric sites on the skin were selected on the basis of anatomical considerations and a Fisher discriminant analysis (FDA), and SEMG at these sites yields suitable information for pinch force estimation. An artificial neural network (ANN) was implemented to map the SEMG to the force, and its structure was optimized to avoid both under- and over-fitting problems. The resulting network was tested using SEMG signals recorded from the selected myoelectric sites of ten subjects in real time. The training time for each subject was short (approximately 96s), and the estimation results were promising, with a normalized root mean squared error (NRMSE) of 0.081+/-0.023 and a correlation (CORR) of 0.968+/-0.017.

Real-Time Upper Limb Motion Estimation From Surface Electromyography and Joint Angular Velocities Using an Artificial Neural Network for Human–Machine Cooperation

A current challenge with human-machine cooperation systems is to estimate human motions to facilitate natural cooperation and safety of the human. It is a logical approach to estimate the motions from their sources (skeletal muscles); thus, we employed surface electromyography (SEMG) to estimate body motions. In this paper, we investigated a cooperative manipulation control by an upper limb motion estimation method using SEMG and joint angular velocities. The SEMG signals from five upper limb muscles and angular velocities of the limb joints were used to approximate the flexion-extension of the limb in the 2-D sagittal plane. The experimental results showed that the proposed estimation method provides acceptable performance of the motion estimation [normalized root mean square error (NRMSE) <;0.15, correlation coefficient (CC) >;0.9] under the noncontact condition. From the analysis of the results, we found the necessity of the angular velocity input and estimation error feedback due to physical contact. Our results suggest that the estimation method can be useful for a natural human-machine cooperation control.

A Practical Strategy for sEMG-Based Knee Joint Moment Estimation During Gait and Its Validation in Individuals With Cerebral Palsy

Individuals with cerebral palsy have neurological deficits that may interfere with motor function and lead to abnormal walking patterns. It is important to know the joint moment generated by the patients muscles during walking in order to assist the suboptimal gait patterns. In this paper, we describe a practical strategy for estimating the internal moment of a knee joint from surface electromyography (sEMG) and knee joint angle measurements. This strategy requires only isokinetic knee flexion and extension tests to obtain a relationship between the sEMG and the knee internal moment, and it does not necessitate comprehensive laboratory calibration, which typically requires a 3-D motion capture system and ground reaction force plates. Four estimation models were considered based on different assumptions about the functions of the relevant muscles during the isokinetic tests and the stance phase of walking. The performance of the four models was evaluated by comparing the estimated moments with the gold standard internal moment calculated from inverse dynamics. The results indicate that an optimal estimation model can be chosen based on the degree of cocontraction. The estimation error of the chosen model is acceptable (normalized root-mean-squared error: 0.15-0.29, R: 0.71-0.93) compared to previous studies (Doorenbosch and Harlaar, 2003; Doorenbosch and Harlaar, 2004; Doorenbosch, Joosten, and Harlaar, 2005), and this strategy provides a simple and effective solution for estimating knee joint moment from sEMG.

Thumb-tip force estimation from sEMG and a musculoskeletal model for real-time finger prosthesis

Due to the difficulties in measurement of muscle activities and the complex musculoskeletal structure, estimations of the thumb-tip force in real time have been a challenge for controlling artificial prosthesis naturally. This study describes an isometric thumb-tip force estimation technique based on phenomenological muscle model named Hills model. The surface electromyogram (sEMG) signals of the muscles near surface were measured and converted to muscle activation information. The activations of deep muscles were inferred from the ratios of muscle activations from earlier study. The muscle length of each contributed muscle was obtained by using motion capture system and musculoskeletal modeling software packages. Once muscle forces were calculated, thumb-tip force was estimated based on mapping model from the muscle force to thumb-tip force. The proposed method was evaluated in comparisons with an artificial neural network (ANN) under four different thumb configurations to investigate the potential for estimations under conditions in which the thumb configuration changes. The results seem to be promising and the proposed method could be applied to predict finger-tip forces from non-invasive neurosignals with a real-time prosthesis control system.

Real-time estimation of thumb-tip forces using surface electromyogram for a novel human-machine interface

Due to difficulties in measurement of muscle activities and understanding a users intention under different configurations, controlling machine forces using surface electromyogram (SEMG) is difficult in a human-machine interface (HMI). This study describes a novel HMI using Hill-based muscle model to control the isometric force of a robotic thumb that considers the importance of the thumb in hand function. In order to estimate force intension, SEMG from the skin surface was measured and converted to muscle activation information. The activations of deep muscles were inferred from the ratios of muscle activations from earlier studies. The muscle length of each contributed muscle was obtained by using a motion capture system and musculoskeletal modeling software packages. Once muscle forces were calculated, thumb-tip force was estimated based on a mapping model from the muscle force to thumb-tip force. The proposed method was evaluated in comparisons with a linear regression and artificial neural network (ANN) under four different thumb configurations to investigate the potential for estimations under conditions in which the thumb configuration changes.

Real-time upper limb motion prediction from noninvasive biosignals for physical human-machine interactions

Human motion and its intention sensing from noninvasive biosignals is one of the significant issues in the field of physical human-machine interactions (pHMI). This paper presents a real-time upper limb motion prediction method using surface electromyography (sEMG) signals for pHMI. The sEMG signals from 5 channels were collected and used to predict the motion by an artificial neural network (ANN) algorithm. We designed a human-machine interaction system to verify the proposed method. Interaction experiments were performed with or without physical contact, and the effects of instances of contact were investigated. The experimental results were compared with controlled experiments using a customized goniometer, which is able to measure upper limb flexion-extension. The results showed that the proposed method was not superior to the use of direct angle measurements; however, it provides sufficient accuracy and a fast response speed for interactions. SEMG-based interactions will become more natural with further studies of human-machine combination models.

Movement Stability Analysis of Surface Electromyography-Based Elbow Power Assistance

The use of power assistive devices that use surface electromyography (SEMG) signals may be limited by the noisy nature of SEMG signals. The aim of this study was to investigate the variation in human movement stability while the amount of SEMG-based assistive power was changed. A robotic device provided a torque that was proportional to the torque estimated by SEMG for assisting human movements, and 12 volunteers participated in the elbow flexion experiments. The maximum finite-time Lyapunov exponent (MFTLE), the average logarithmic rate of the divergence of neighboring trajectories, and the variability of the kinematic data were used to quantify the stability of the assisted elbow movements. The stability provided by the MFTLE decreased as the amount of assistive torque increased with respect to the amount of human torque. The kinematic variability increased with the increase in assistive torque. Therefore, by ensuring that the amount of SEMG-based assistive torque is less than the amount of human torque, the assistance may provide relatively natural movements. This study is the first to quantify movement stability as SEMG-based assistive power is applied. This study can provide a foundation for determining the appropriate amount of SEMG-based assistive power.

Understanding of hands and task characteristics for development of biomimetic robot hands

This paper describes a study to understand human hand dexterity, which is the ultimate goal of biomimetic robot hands. The hand is one of the most complicated organs in the human body and this complicated anatomical structure is a source of a dexterity and force capability that man-made hands have rarely achieved. The anatomical and neuromuscular aspects of the human hand are surveyed to provide technical specifications. In addition, the roles of visual and tactile sensory capabilities during object manipulation are discussed. Finally, the paper presents an instrumentation system for measuring surface electromyogram (sEMG) and exerted finger forces concurrently. This system is able to characterize hand tasks and provide technical specifications for development of biomimetic hands with dexterity and force capabilities that are comparable to those of human hands.

Variation of Dynamic Muscle Model during Fatigue-Inducing Voluntary Contraction

There has been a paucity of studies to identify the variation of muscle properties due to muscle fatigue, although feature changes of surface electromyographic signals due to fatigue have been reported on. In this paper, we investigated the variation of muscle properties in pre-fatigue condition and post-fatigue condition by using the SEMG and the dynamic muscle model. Five subjects performed index finger isometric abduction contraction by using the first dorsal interosseous (FDI) muscle. After fatigue-inducing contraction, the total twitch duration increased by 30.10%, the contraction time and half relaxation time (RT 1/2) were raised to 7.45% and 13.11%, respectively. These results indicate that the response of the twitch force was slowed and prolonged due to the fatigue-inducing contraction. Our results can be used to monitor and identify the muscle properties of patients in rehabilitation programs.

Bimanual shoulder flexion system with surface electromyography for hemiplegic patients after stroke: A preliminary study

A robot-assisted bimanual shoulder flexion rehabilitation system with surface electromyography (sEMG) for hemiplegic patients after stroke is presented as a preliminary study before clinical test. The assistive system driven by combination of bimanual mirror imaging motion and sEMG in order to induce continuous voluntary stimulation to muscle and nerve of the patients. In this paper, hardware design, controller with impedance compensation of actuator using disturbance observer (DOB) for back-drivable operation, and sEMG signal processing to obtain desired assistive torque are also reported. The performance of impedance compensation and assistive operation of the system with sEMG were verified by experiments with a healthy participant. This system is expected to help to recover functionality of neural/musculoskeletal system to hemiplegic patients.