Abstract

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A haptic display system is presented for manipulating virtual mechanisms derived from a mechanical CAD design. Links are designed and assembled into mechanisms using Utah's Alpha.1 CAD system, and are then manipulated with a Sarcos Dextrous Arm Master. Based on the mechanism's kinematics and the virtual grasp, the motion of the master is divided into motion of the mechanism and constraint violation.The operator expen"ences the dynamic forces from the mechanism plus constraint forces.

This paper presents a novel feature selection method to design a non-invasive driver drowsiness detection system based on steering wheel data. The proposed feature selector can select the most related features to the drowsiness level to improve the classification accuracy. This method is based on the combination of the filter and wrapper feature selection algorithms using adaptive neuro-fuzzy inference system (ANFIS). In this method firstly, four different filter indexes are applied on extracted features from steering wheel data. After that, output values of each filter index are imported as inputs to a fuzzy inference system to determine the importance degree of each feature and select the most important features. Then, the selected features are imported to a support vector machine (SVM) for binary classification to classify the driving conditions in two classes of drowsy and awake. Finally, the classifier accuracy is exploited to adjust parameters of an adaptive fuzzy system using a particle swarm optimization (PSO) algorithm. The experimental data were collected from about 20.5 h of driving in the simulator. The results show that the drowsiness detection system is working with a high accuracy and also confirm that this method is more accurate than the recent available algorithms.

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This study proposes a drowsiness detection approach based on the combination of several different detection methods, with robustness to the input signal loss. Hence, if one of the methods fails for any reason, the whole system continues to work properly. To choose correct combination of the available methods and to utilize the benefits of methods of different categories, an image processing-based technique as well as a method based on driver-vehicle interaction is used. In order to avoid driving distraction, any use of an intrusive method is prevented. A driving simulator is used to gather real data and then artificial neural networks are used in the structure of the designed system. Several tests were conducted on twelve volunteers while their sleeping situations during one day prior to the tests, were fully under control. Although the impact of the proposed system on the improvement of the detection accuracy is not remarkable, the results indicate the main advantages of the system are the reliability of the detections and robustness to the loss of the input signals. The high reliability of the drowsiness detection systems plays an important role to reduce drowsiness related road accidents and their associated costs.

We present a physics-based training simulator for bone machining. Based on experimental studies, the energy required to remove a unit volume of bone is a constant for every particular bone material. We use this physical principle to obtain the forces required to remove bone material with a milling tool rotating at high speed. The rotating blades of the tool are modeled as a set of small cutting elements. The force of interaction between a cutting element and bone is calculated from the energy required to remove a bone chip with an estimated thickness and known material stiffness. The total force acting on the cutter at a particular instant is obtained by integrating the differential forces over all cutting elements engaged. A voxel representation is used to represent the virtual bone and removed chips for calculating forces of machining. We use voxels that carry bone material properties to represent the volumetric haptic body and to apply underlying physical changes during machining. Experimental results of machining samples of a real bone confirm the force model. A real-time haptic implementation of the method in a dental training simulator is described.

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