Ehsan Tarkesh Esfahani

Multisensor Wireless System for Eccentricity and Bearing Fault Detection in Induction Motors

This paper presents a stand-alone multisensor wireless system for continuous condition monitoring of induction motors. The proposed wireless system provides a low-cost alternative to expensive condition monitoring technology available through dedicated current signature analysis or vibration monitoring equipment. The system employs multiple sensors (acoustic, vibration, and current) mounted on a common wireless platform. The faults of interest are static and dynamic air-gap eccentricity, bearing damage, and their combinations. The Hilbert-Huang transform of vibration data and power spectral density of current and acoustic signals are used as the features in a hierarchical classifier. The proposed wireless system can distinguish a faulty motor from a healthy motor with a probability of 99.9% of correct detection and less than 0.1% likelihood of false alarm. It can also discriminate between different fault categories and severity with an average accuracy of 95%.

Classification of primitive shapes using brain-computer interfaces

Brain-computer interfaces (BCIs) are recent developments in alternative technologies of user interaction. The purpose of this paper is to explore the potential of BCIs as user interfaces for CAD systems. The paper describes experiments and algorithms that use the BCI to distinguish between primitive shapes that are imagined by a user. Users wear an electroencephalogram (EEG) headset and imagine the shape of a cube, sphere, cylinder, pyramid or a cone. The EEG headset collects brain activity from 14 locations on the scalp. The data is analyzed with independent component analysis (ICA) and the Hilbert-Huang Transform (HHT). The features of interest are the marginal spectra of different frequency bands (theta, alpha, beta and gamma bands) calculated from the Hilbert spectrum of each independent component. The Mann-Whitney U-test is then applied to rank the EEG electrode channels by relevance in five pair-wise classifications. The features from the highest ranking independent components form the final feature vector which is then used to train a linear discriminant classifier. Results show that this classifier can discriminate between the five basic primitive objects with an average accuracy of about 44.6% (compared to naive classification rate of 20%) over ten subjects (accuracy range of 36%-54%). The accuracy classification changes to 39.9% when both visual and verbal cues are used. The repeatability of the feature extraction and classification was checked by conducting the experiment on 10 different days with the same participants. This shows that the BCI holds promise in creating geometric shapes in CAD systems and could be used as a novel means of user interaction.

Stable Walking Pattern for an SMA-Actuated Biped

In this paper, a walking pattern filter for shape-memory-alloy (SMA)-actuated biped robots is presented. SMAs are known for their high power-to-mass ratio as well as slow response. When used as actuators, the SMA speed limitation can potentially lead to stability problems for biped robots. The presented filter adapts the human motion such that an SMA biped robot maintains a stable walking pattern. The zero moment point (ZMP) is used as the main criterion of the filter to guarantee the stability of the motion. The SMA actuators are designed based on the dynamics and kinematics of the motion. The response time of each SMA actuator is modeled in order to estimate the behavior of the actuator in realizing the given trajectory. After applying the delay times to the motion, the new trajectories are generated and evaluated by the filter for the ZMP criterion. Using simulations, it is shown that the filter can generate smooth trajectories for the SMA-actuated biped robots. The filter furthermore guarantees the stability of a robot mimicking the human walking motion.

Cognitive skills assessment during robot-assisted surgery: separating the wheat from the chaff.

To investigate the utility of cognitive assessment during robot‐assisted surgery (RAS) to define skills in terms of cognitive engagement, mental workload, and mental state; while objectively differentiating between novice and expert surgeons.

Cognitive skills assessment during robot-assisted surgery: Separating the wheat from the chaff

To investigate the utility of cognitive assessment during robot‐assisted surgery (RAS) to define skills in terms of cognitive engagement, mental workload, and mental state; while objectively differentiating between novice and expert surgeons.

Using brain-computer interfaces to detect human satisfaction in human-robot interaction

This article discusses the use of a brain–computer interface (BCI) to obtain emotional feedback from a human in response to the motion of humanoid robots in collaborative environments. The purpose of this study is to detect the human satisfaction level and use it as a feedback for correcting and improving the behavior of the robot to maximize human satisfaction. This article describes experiments and algorithms that use human brains activity collected through BCI in order to estimate the level of satisfaction. Users wear an electroencephalogram (EEG) headset and control the movement of the robot by mental imagination. The robots responds to the mental imagination may not be the same as human mental command and this will affect the emotional satisfaction level. The headset records brain activity from 14 locations on the scalp. Power spectral density of each EEG frequency band and four largest Lyapunov exponents of each EEG signal form the feature vector. The Mann–Whitney–Wilcoxon test is then used to rank all the features. The highest rank features are then selected to train a linear discriminant classifier (LDC) to determine the satisfaction level. Our experimental results show an accuracy of 79.2% in detecting the human satisfaction level.

Developing an Adaptive Controller for a Shape Memory Alloy Walking Assistive Device

The Shape Memory Alloy is a lightweight, compact and biocompatible actuation mechanism which is considered here to replace the current actuation technologies in assistive locomotion devices. This paper is aimed toward the development of an adaptive robust controller to deal with control problems in the actuation of shape memory alloys (SMA). In this research the ankle joint is considered to be actuated by SMA but it can be extended to the other joints as well. To check the performance of the controller, the dynamics of the ankle joint during walking is studied and an SMA manipulator with a similar behavior is used for the experiment. Nonlinear behavior of SMA wires requires nonlinear control techniques for tracking the desired ankle angle. Since the device is subjected to several uncertainties and unmodeled parameters, it is also necessary for the implemented control technique to be robust and adaptive. To this end, the proposed control technique consists of two parts. The first part is an adaptive PID controller which is motivated from a sliding mode control. The control gains are adjusted based on an adaptation mechanism to minimize the sliding condition. The second part of the controller is a supervisory control that guarantees the stability of the system.

Technical mentorship during robot-assisted surgery: a cognitive analysis

To investigate cognitive and mental workload assessments, which may play a critical role in defining successful mentorship.

Understanding Cognitive Performance During Robot-Assisted Surgery

OBJECTIVE To understand cognitive function of an expert surgeon in various surgical scenarios while performing robot-assisted surgery. MATERIALS AND METHODS In an Internal Review Board approved study, National Aeronautics and Space Administration-Task Load Index (NASA-TLX) questionnaire with surgical field notes were simultaneously completed. A wireless electroencephalography (EEG) headset was used to monitor brain activity during all procedures. Three key portions were evaluated: lysis of adhesions, extended lymph node dissection, and urethro-vesical anastomosis (UVA). Cognitive metrics extracted were distraction, mental workload, and mental state. RESULTS In evaluating lysis of adhesions, mental state (EEG) was associated with better performance (NASA-TLX). Utilizing more mental resources resulted in better performance as self-reported. Outcomes of lysis were highly dependent on cognitive function and decision-making skills. In evaluating extended lymph node dissection, there was a negative correlation between distraction level (EEG) and mental demand, physical demand and effort (NASA-TLX). Similar to lysis of adhesion, utilizing more mental resources resulted in better performance (NASA-TLX). Lastly, with UVA, workload (EEG) negatively correlated with mental and temporal demand and was associated with better performance (NASA-TLX). The EEG recorded workload as seen here was a combination of both cognitive performance (finding solution) and motor workload (execution). Majority of workload was contributed by motor workload of an expert surgeon. During UVA, muscle memory and motor skills of expert are keys to completing the UVA. CONCLUSION Cognitive analysis shows that expert surgeons utilized different mental resources based on their need.

Using Brain Computer Interfaces for Geometry Selection in CAD Systems: P300 Detection Approach

The purpose of this paper is to explore the potential of brain-computer interfaces as user interfaces for CAD systems. The paper describes experiments and algorithms that use the BCI for selecting different surface of geometrical objects in the CAD systems using the P300 wave. The P300 (P3) wave is an event related potential (ERP) elicited by infrequent, stimuli (target faces flashing). Users wear an electroencephalogram (EEG) headset and try to select a target face of an object. Different faces of the object randomly flash which make the flashing of target face, an infrequent event. The EEG headset collects brain activity from 14 locations on the scalp. The data is analyzed with independent component analysis (ICA) and the discrete wavelet transforms (DWT) to detect the P300 component in the signal. The flashing face which causes the P300 component in the EEG signal is classified as the target face. Using a linear discriminant analysis, the target face is classified correctly with an average accuracy of 73.9%.Copyright