Farid Mobasser

Estimation of Elbow-Induced Wrist Force With EMG Signals Using Fast Orthogonal Search

In many studies and applications that include direct human involvement-such as human-robot interaction, control of prosthetic arms, and human factor studies-hand force is needed for monitoring or control purposes. The use of inexpensive and easily portable active electromyogram (EMG) electrodes and position sensors would be advantageous in these applications compared to the use of force sensors, which are often very expensive and require bulky frames. Multilayer perceptron artificial neural networks (MLPANN) have been used commonly in the literature to model the relationship between surface EMG signals and muscle or limb forces for different anatomies. This paper investigates the use of fast orthogonal search (FOS), a time-domain method for rapid nonlinear system identification, for elbow-induced wrist force estimation. It further compares the forces estimated using FOS with the forces estimated by MLPANN for the same human anatomy under an ensemble of operational conditions. In this paper, the EMG signal readings from upper arm muscles involved in elbow joint movement and sensed elbow angular position and velocity are utilized as inputs. A single degree-of-freedom robotic experimental testbed has been constructed and used for data collection, training and validation

Dynamic parameter identification and analysis of a PHANToM haptic device

In this paper, the dynamics of a SensAble Technologies PHANToM Premium 1.5 haptic device is experimentally identified and analyzed. Towards this purpose, the dynamic model derived in the work of M. C. Cavusoglu and D. Feygin (2001) is augmented with a friction model and is linearly parameterized. The identified model predicts joint torques with over 95% accuracy and produces an inertia matrix that is confirmed to be positive-definite within the device workspace. In addition, user hand force estimates with and without including the identified dynamics are compared with the measured values. The experiments are also conducted for other typical installation conditions of the device, such as with force sensor mounted at the end-effector, using gimbal and counterbalance weight, and upside-down installation of the device. The identified dynamic model can be used for hand force estimation, accurate gravity counterbalancing for different installation conditions, and model-based control systems design for haptic simulation and tele-operation applications

Neural-Network-Based Contact Force Observers for Haptic Applications

In the majority of robotic and haptic applications, including manipulation and human-robot interaction, contact force needs to be monitored and controlled. Transparent implementation of bilateral teleoperation or haptic controllers necessitates the exchange of operator and environment contact forces. This requires the use of expensive commercially available force/torque sensors, which are rather bulky, are vulnerable to impact forces, and increase system inertia and compliance. An alternative solution is the use of dynamic force observers, which estimate external forces using system dynamic model. However, due to the uncertainties in system dynamic structure and parameters, these model-based observers do not produce accurate force estimates, and often create a dynamic lag that may cause bandwidth limitation and instability. This paper proposes two neural-network-based force/torque observers that do not require a system dynamic model. The observers can estimate human hand force and environment contact force with up to 98.3% accuracy in the sense of mean-square error, and with negligible dynamic lag. The performance of the proposed observers are extensively analyzed in separate human-robot and robot-environment experimental settings, and in a two-channel bilateral teleoperation control loop with multiple runs with two Planar Twin-Pantograph haptic devices

A Method for Online Estimation of Human Arm Dynamics

Human arm dynamics can be used for human body performance analysis or for control of human-machine interfaces. In this paper, a novel method for online estimation of human forearm dynamics using a second-order quasi-linear model is presented. The proposed method uses Moving Window Least Squares to locally identify dynamic parameters for a limited number of operating points in a variable space defined by elbow joint angle and velocity, and the electromyogram signals collected from upper-arm muscles. The dynamic parameters for these limited points are then employed to train a Radial Basis Function Artificial Neural Network to interpolate/extrapolate for online estimates of arm dynamic parameters for other operating points in the variable space. The proposed estimation method is evaluated on a single degree-of-freedom robotic arm

Rowing stroke force estimation with EMG signals using artificial neural networks

Performance analysis in sports activities such as rowing requires measurement of athlete hand force. The use of inexpensive and easily portable active electromyogram (EMG) electrodes and position sensors would be advantageous compared to the use of heavy duty expensive force sensors that require bulky frames and are vulnerable to overload. In this study, artificial neural networks (ANN) are employed for hand force estimation using EMG signals collected from upper arm muscles involved in elbow joint movement and sensed elbow angular position and velocity. In particular, the performance of multilayer perceptron (MLPANN) and radial basis function ANN (RBFANN) for hand force estimation under emulated rowing condition are compared experimentally

Transparent Rate Mode Bilateral Teleoperation Control

Transparent teleoperation under rate mode has proven to be difficult in terms of stability, performance and implementation. This is mainly due to the need for an exchange of derivative and integral of measured positions and forces which make transparent rate mode controllers prone to noise and abrupt contact force changes. Moreover, the performance of controllers declines in the presence of communication delays. This paper proposes two control architectures based on the use of local force feedback (LFF) and environment impedance reflection (EIR). The LFF controllers eliminate a force channel while preserving transparency under ideal conditions. In the EIR controller, the identified impedance of the environment is employed in the master controller to predict the slave position and contact force derivatives. The stability robustness and performance of these controllers are evaluated and compared to those of a benchmark controller under different operational conditions, such as noise and delay, using analytical methods and experimental results.

Hand Force Estimation using Electromyography Signals

In many studies and applications that include direct human involvement such as human-robot interaction, control of prosthetic arms, and human factor studies, hand force is needed for monitoring or control purposes. The use of inexpensive and easily portable active electromyogram (EMG) electrodes and position sensors would be advantageous in these applications compared to the use of force sensors which are often very expensive and require bulky frames. Among non model-based estimation methods, “Multilayer Perceptron” Artificial Neural Networks (MLPANN) have widely been used to estimate muscle force or joint torque of different anatomy of humans or animals. This paper investigates the use of Radial Basis Function (RBF) ANN and MLPANN for force estimation and compares the performance of the two methodologies for the same human anatomy, i.e. hand force estimation, under an ensemble of operational conditions. In this unified study, the EMG signal readings from upper arm muscles involved in elbow joint movement and sensed elbow angular position and velocity are utilized as inputs to ANNs. Moreover, the use of elbow angular acceleration signal as input for ANN is investigated. Towards this end, a single degree-of-freedom robotic experimental testbed has been constructed, which allows for data collection, training and validation.

A model-independent force observer for teleoperation systems

Transparent implementation of bilateral teleoperation or haptic controllers necessitates the measurement of operator hand force. This requires the use of expensive commercially available 6 degree-of-freedom (DOF) force/torque sensors. Since the bandwidth of human operator force output is reasonably low, observers can be used for hand force estimation. The drawback of conventional force observers is their dynamic lag and the need for exact knowledge of the dynamic model of the haptic device. As an alternative, a novel model-independent force observer (MIFO) is proposed in this paper, in which a multilayer perceptron neural network (MLPANN) is utilized and trained for force estimation. The performance of the proposed observer is verified on a 1-DOF experimental setup.

Implementation of a rate mode impedance reflecting teleoperation controller on a haptic simulation system

Transparent teleoperation under rate mode has proven to be difficult in terms of stability, performance and implementation. This is mainly due to the need for exchange of derivatives and integrals of measured positions and forces. This paper proposes and implements a new control architecture designed based on the environment impedance reflecting controller previously developed. The performance of this new controller, implemented on a haptic simulation test-bed, is compared to that of a conventional controller under different operational conditions.

Predictive Teleoperation using Laser Rangefinder

Presence of delay in communication channels degrades performance and stability in bilateral teleoperation controllers. One method to remedy this problem is the use of predictive controllers in which the slave and environment dynamics are modelled and rendered locally at master to reproduce the contact information and bypass the delayed feedback loop. A major challenge in these controllers is the agility of local model adaptation to the changes in environment dynamics and its synchronization with slave contact events. In this paper, a novel collision prediction method using laser rangefinder is introduced. The proposed method is employed to synchronize the master local model and slave control events in a force position teleoperation architecture. Moreover, this method speeds up environment identification algorithm. The proposed predictive controller is evaluated experimentally on a one degree of freedom (DOF) master-slave teleoperation setup and its performance is compared to that of a conventional force-position controller