Online calibration of the EMG to force relationship

BioMedical Engineering OnLine, 2013

Background: EMG-to-force estimation based on muscle models, for voluntary contraction has many applications in human motion analysis. The so-called Hill model is recognized as a standard model for this practical use. However, it is a phenomenological model whereby muscle activation, force-length and force-velocity properties are considered independently. Perreault reported Hill modeling errors were large for different firing frequencies, level of activation and speed of contraction. It may be due to the lack of coupling between activation and force-velocity properties. In this paper, we discuss EMG-force estimation with a multi-scale physiology based model, which has a link to underlying crossbridge dynamics. Differently from the Hill model, the proposed method provides dual dynamics of recruitment and calcium activation. Methods: The ankle torque was measured for the plantar flexion along with EMG measurements of the medial gastrocnemius (GAS) and soleus (SOL). In addition to Hill representation of the passive elements, three models of the contractile parts have been compared. Using common EMG signals during isometric contraction in four ablebodied subjects, torque was estimated by the linear Hill model, the nonlinear Hill model and the multi-scale physiological model that refers to Huxley theory. The comparison was made in normalized scale versus the case in maximum voluntary contraction. Results: The estimation results obtained with the multi-scale model showed the best performances both in fast-short and slow-long term contraction in randomized tests for all the four subjects. The RMS errors were improved with the nonlinear Hill model compared to linear Hill, however it showed limitations to account for the different speed of contractions. Average error was 16.9% with the linear Hill model, 9.3% with the modified Hill model. In contrast, the error in the multi-scale model was 6.1% while maintaining a uniform estimation performance in both fast and slow contractions schemes. Conclusions: We introduced a novel approach that allows EMG-force estimation based on a multi-scale physiology model integrating Hill approach for the passive elements and microscopic cross-bridge representations for the contractile element. The experimental evaluation highlights estimation improvements especially a larger range of contraction conditions with integration of the neural activation frequency property and force-velocity relationship through cross-bridge dynamics consideration.

2010 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES), 2010

This paper is motivated by works done in the area of robot-assisted stroke rehabilitation. The use of electromyographic (EM G) signal brings a new way of communication interface between user and robot. However, the EMG signal has to be transferred into useful information that serve as robot input. This paper presents a novel methodology for conversion of electromyographic (EMG) signal into estimated joint torque. Investigation of the proposed methodology covers human upper limb movement: shoulder flexion-extension, shoulder abduction-adduction, and elbow flexion-extension. Simulated annealing (SA) is implemented to obtain optimum model that maps EMG into estimated joint torque. General principle, design, and the implementation of SA for the problem are discussed in this paper. Experimentation was carried out to investigate the feasibility of the proposed algorithm. The results show that the algorithm is able to find optimum model that enables EMG to joint torque conversion.

International Journal of Engineering Research and Technology (IJERT), 2014

https://www.ijert.org/emg-signal-analysis-and-application-for-arm-exoskeleton-control https://www.ijert.org/research/emg-signal-analysis-and-application-for-arm-exoskeleton-control-IJERTV3IS080865.pdf Aim of this paper is to throw light on the concept of electromyography (EMG) signals and how they can be applied to real world applications through the employment of motion support exoskeleton. The scope of the present research is to design a low power, low cost EMG based exoskeleton system and its experimental implementation in an elbow joint, naturally controlled by the human. Preamplifier section is designed with operational amplifier OPA4227 through which raw EMG signal is extracted by passive electrodes. Amplified EMG signal is passed through filter section for restriction of frequency range. This restricted signal is rectified to acquire constant polarity for higher average output voltage. Acquisition and processing of EMG signal is done by ATMEGA 32U4 microcontroller. Data is transmitted to computer for visualization through Zigbee module. Surface EMG amplitude and the torque about elbow joint construct the system design.

Applied Bionics and Biomechanics, 2009

One of the approaches to study the human motor system, and specifically the motor strategies implied during postural tasks of the upper limbs, is to manipulate the mechanical conditions of each joint of the upper limbs independently. At the same time, it is essential to pick up biomechanical signals and bio-potentials generated while the human motor system adapts to the new condition. The aim of this paper is twofold: first, to describe the design, development and validation of an experimental platform designed to modify or perturb the mechanics of human movement, and simultaneously acquire, process, display and quantify bioelectric and biomechanical signals; second, to characterise the dynamics of the elbow joint during postural control. A main goal of the study was to determine the feasibility of estimating human elbow joint dynamics using EMG-data during maintained posture. In particular, the experimental robotic platform provides data to correlate electromyographic (EMG) activity, kinetics and kinematics information from the upper limb motion. The platform aims consists of an upper limb powered exoskeleton, an EMG acquisition module, a control unit and a software system. Important concerns of the platform such as dependability and safety were addressed in the development. The platform was evaluated with 4 subjects to identify, using system identification methods, the human joint dynamics, i.e. visco-elasticity. Results obtained in simulations and experimental phase are introduced.

method are presented in this chapter. Then, the upper-limb muscle activities during daily upper-limb motions have been studied to enable exoskeleton robots to estimate human upper-limb motions based on EMG signals of related muscles. The muscle combinations are identified to separate some motions of upper-limb. Minimum number of muscles to extract signals to control frequent daily upper-limb motions has been identified. In the next step, EMG signal of identified muscles are used to control two upper-limb exoskeleton robots. A three degree of freedom (DOF) exoskeleton robot (W-EXOS) for the forearm pronation/supination, wrist flexion/extension and ulnar/radial deviation are controlled by applying the surface EMG signals of six muscles. Surface EMG signals of upper-limb muscles are applied as input information to control a 6DOF exoskeleton robot (SUEFUL-6). In each case of applying EMG signals experiments have been carried out to evaluate the effectiveness of the EMG based control method. In the next section, the detection and processing of surface EMG signals are presented. The experimental study of upper-limb surface EMG is explained in section 3. Application of EMG signals to control the W-EXOS is described in section 4. Section 5 explains the EMG based control of the SUEFUL-6. The discussion in section 6 is followed by the conclusion in the section 7.