Mostafa Ghobadi

Three-Dimensional smooth trajectory planning using realistic simulation

This paper presents a method for planning three-dimensional walking patterns for biped robots in order to obtain stable smooth dynamic motion and also maximum velocity during walking. To determine the rotational trajectory for each actuator, there are some particular key points gained from natural human walking whose value is defined at the beginning, end and some intermediate or specific points of a motion cycle. The constraint equation of the motion between the key points will be then formulated in such a way to be compatible with geometrical constraints. This is first done in sagittal and then developed to lateral plane of motion. In order to reduce frequent switching due to discrete equations which is inevitable using coulomb dry friction law and also to have better similarity with the natural contact, a new contact model for dynamic simulation of foot ground interaction has been developed which makes the cyclic discrete equations continuous and can be better solved with ODE solvers. Finally, the advantages of the trajectory described are illustrated by simulation results.

Foot-mounted inertial measurement unit for activity classification.

This paper proposes a classification technique for daily base activity recognition for human monitoring during physical therapy in home. The proposed method estimates the foot motion using single inertial measurement unit, then segments the motion into steps classify them by template-matching as walking, stairs up or stairs down steps. The results show a high accuracy of activity recognition. Unlike previous works which are limited to activity recognition, the proposed approach is more qualitative by providing similarity index of any activity to its desired template which can be used to assess subjects improvement.

Three-Dimensional Smooth Trajectory Planning Using Realistic Simulation.

This paper presents a method for planning three-dimensional walking patterns for biped robots in order to obtain stable smooth dynamic motion and also maximum velocity during walking. To determine the rotational trajectory for each actuator, there are some particular key points gained from natural human walking whose value is defined at the beginning, end and some intermediate or specific points of a motion cycle. The constraint equation of the motion between the key points will be then formulated in such a way to be compatible with geometrical constraints. This is first done in sagittal and then developed to lateral plane of motion. In order to reduce frequent switching due to discrete equations which is inevitable using coulomb dry friction law and also to have better similarity with the natural contact, a new contact model for dynamic simulation of foot ground interaction has been developed which makes the cyclic discrete equations continuous and can be better solved with ODE solvers. Finally, the advantages of the trajectory described are illustrated by simulation results.

A ROBUST AUTOMATIC GAIT MONITORING APPROACH USING A SINGLE IMU FOR HOME-BASED APPLICATIONS

A new approach of human activity monitoring with a single Inertial Measurement Unit (IMU) capable of gait recognition and assessment is proposed for home-based applications. The method estimates the foot motion using a single IMU, then automatically segments the motion into steps, and extracts multiple kinematics templates. It classifies each segment by extracting Mahalanobis distance-based features from multiple sections of the motion templates and then training a Support Vector Machine. The proposed wearable system can distinguish between nine classes of activities with a classification accuracy of 99.6%. It can also discriminate between normal and abnormal gait patterns with an accuracy of 98.7%. In addition to a high recognition rate, the proposed approach provides a Gait Similarity Score (GSS) of the performed gait to its desired/normal pattern. The experimental results indicate the capability of GSS measure for assessing the quality of motion in “pre-”, “initial”, “mid” and “terminal” stages of swin...

Using mini minimum jerk model for human activity classification in home-based monitoring

This paper proposes a method for human activity classification in home based monitoring. The proposed approach is based on minimum jerk (MinJerk), a primary model for smooth path planning employed by human motor control in upper-extremity motion. Based on new evidences that show common control strategies in lower and upper extremity, MinJerk is adapted in our study to estimate the foot motion with fifth order polynomial functions. Experimental data are recorded during walking and going up and down the stairs using a single inertial measurement unit. Features of interest in this study are the optimized curve fitting coefficients. Using a structured support vector machine with radial basis function, an overall accuracy of 98.6% is achieved for activity classification. The proposed method is also capable of detecting the transitions between the movements with accuracy of 99.96%.

Adaptive Segmentation for Air Gestures Identification

Having a user-friendly Human-CAD interaction with high speed and accuracy plays a key role in development of future intelligent modeling environments. A major part of this puzzle is sketch identification using either 2D gestures — which is commonly recorded from mouse, light pen and touchpad — or air gestures captured from some newly emerged devices such as Leap Motion and Soft-Kinect. To this end, we present a leaning based technique for segmentation of air gestures. The proposed technique can detect the separation points of any single-stroke air gesture using specific motion features such as speed, curvature and center of curvature. Two types of separation points are considered: 1) rough separation points or simply corner points and 2) soft separation points such as inflection points. The segmentation is performed in two steps: Support Vector Machine (SVM) is used to adaptively differentiate the corner points from regular points. A soft segmentation method is then implemented to further break the rough segments into a set of smaller arcs and lines based on sudden change in the center of curvature. The experimental validation shows robust performance of the proposed method and low computation expenses.Copyright

Robust Attitude Estimation from Uncertain Observations of Inertial Sensors Using Covariance Inflated Multiplicative Extended Kalman Filter

This paper presents an attitude estimation method from uncertain observations of inertial sensors, which is highly robust against different uncertainties. The proposed method of covariance inflated multiplicative extended Kalman filter (CI-MEKF) takes the advantage of non-singularity of covariance in MEKF as well as a novel covariance inflation (CI) approach to fuse inconsistent information. The proposed CI approach compensates the undesired effect of magnetic distortion and body acceleration (as inherent biases of magnetometer and accelerometer sensors data, respectively) on the estimated attitude. Moreover, the CI-MEKF can accurately estimate the gyro bias. A number of simulation scenarios are designed to compare the performance of the proposed method with the state of the art in attitude estimation. The results show the proposed method outperforms the state of the art in terms of estimation accuracy and robustness. Moreover, the proposed CI-MEKF method is shown to be significantly robust against different uncertainties, such as large body acceleration, magnetic distortion, and errors, in the initial condition of the attitude.

Quantitative estimation of electro-osmosis force on charged particles inside a borosilicate resistive-pulse sensor

Nano and micron-scale pore sensors have been widely used for biomolecular sensing application due to its sensitive, label-free and potentially cost-effective criteria. Electrophoretic and electroosmosis are major forces which play significant roles on the sensors performance. In this work, we have developed a mathematical model based on experimental and simulation results of negatively charged particles passing through a 2μm diameter solid-state borosilicate pore under a constant applied electric field. The mathematical model has estimated the ratio of electroosmosis force to electrophoretic force on particles to be 77.5%.

Adaptation of Rehabilitation System Based on User’s Mental Engagement

Recognition of user’s mental engagement is imperative to the success of robotic rehabilitation. The paper explores the novel paradigm in robotic rehabilitation of using Passive BCI as opposed to the conventional Active ones. We have designed experiments to determine a user’s level of mental engagement. In our experimental study, we record the brain activity of 3 healthy subjects during multiple sessions where subjects need to navigate through a maze using a haptic system with variable resistance/assistance. Using the data obtained through the experiments we highlight the drawbacks of using conventional workload metrics as indicators of human engagement, thus asserting that Motor and Cognitive Workloads be differentiated.Additionally we propose a new set of features: differential PSD of Cz-Poz at alpha, Beta and Sigma band, (Mental engagement) and relative C3-C4 at beta (Motor Workload) to distinguish Normal Cases from those instances when haptic where applied with an accuracy of 92.93%. Mental engagement is calculated using the power spectral density of the Theta band (4–7 Hz) in the parietal-midline (Pz) with respect to the central midline (Cz). The above information can be used to adjust robotic rehabilitation parameters I accordance with the user’s needs. The adjustment may be in the force levels, difficulty level of the task or increasing the speed of the task.Copyright

Simultaneous Output-Only Identification of Physical Parameters and Unknown Inputs for Linear Mechanical Systems

This paper has studied the identification problem of linear mechanical systems where inputs are unknown and only displacement data are accessible for measurement. Eigensystem Realization Algorithm (ERA) has been used along with physical constraints considerations in time domain to simultaneously identify two separate models for the physical system and the unknown inputs. Inputs are assumed to be an arbitrary combination of harmonic signals with frequencies higher than natural frequencies of the physical system by which a linear mechanical system is meant in this paper. Adding physical constraints and utilizing canonical real Jordan form of the identified system leads to a unique analytical solution. To validate the theory part, a set of simulations has been run that demonstrates the physical parameters and input model can be estimated accurately.Copyright