Given that human-like robots are finding their place in different areas of our world, research is being carried out in order to improve human-robot interac-tion. Humanoid robots not only require a human appearance but also require human-like movements. Along these lines we present this project which tries to address the question of human to robot arm mapping with the objective to control in real time and to teach a robot by imitation. For the technical implementation we have worked with a SR3000 ToF camera to sense the human movements which allows us to perform a marker-less arm tracking based on depth information. The robot used is a Robotis Bioloid; still, the software has been designed to be adapted with few modifications to other robots having similar arm structures.

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This paper presents a new 3D object representation called a modified exoskeleton (mES) which is another type of skeleton that is more stable and less sensitive to rotation and noise than the conventional skeletons. The mES preserves significant characteristics about an object, that are meaningful for object recognition and reconstruction, as the skeleton does. Then a fast and reliable matching algorithm for recognition and classification system which can identify and classify an observed object is introduced. The main advantage of our matching algorithm is that it can recognize the observed 3D object regardless of its orientation and noise. The first experiment was conducted on a set of artificial 3D objects and the average recognition rate of 92.60% based on the mES is achieved. The second experiment was tested on the classification of volumetric lung tumor data sets and the obtained results are promising.

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Electrocorticography (ECoG) has emerged as a new sig-nal platform for brain–computer interface (BCI) systems. Classically, the cortical physiology that has be-en commonly investigated and utilized for device control in humans has been brain signals from the sensorimotor cortex. Hence, it was unknown whether other neurophysiological substrates, such as the speech network, could be used to further improve on or complement existing motor-based control paradigms. We de-monstrate here for the first time that ECoG signals associated with different overt and imagined phoneme articulation can enable invasively monitored human patients to control a one-dimensional computer cursor rapidly and accurately. This phonetic content was distinguishable within higher gamma frequency osci-llations and enabled users to achieve final target accuracies between 68% and 91% within 15 min. Addi-tionally, one of the patients achieved robust control using recordings from a microarray consisting of 1 mm spaced microwires. These findings suggest that the cortical network associated with speech could pro-vide an additional cognitive and physiologic substrate for BCI operation and that these signals can be ac-quired from a cortical array that is small and minimally invasive.

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This paper presents a sensor fusion strategy to estimate the absolute position and to identify the gait events of an active ankle-foot orthosis (AAFO). Sensor fusion is implemented using a Kalman filter with the signals of a gyroscope and an accelerometer, both present in an inertial measurement unit (IMU), besides the signals of three force sensitive resistors (FSRs) mounted in a sole of shoe. For correcting the gyroscope-based position, the redundant accelerometer-based position must be used only under reliable conditions, which are obtained by a proposed switching logic to enable the Kalman filter. Experimental results of the AAFO confirm the efficiency of the proposed sensor fusion strategy.

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Lifting patients is a demanding task for care providers. In addition, the number of patients is rising and the number of people with obesity is expanding. Current lifting aids are single purpose and time consuming to use. Exoskeletons can fulfill the demand for a versatile and easy to use lifting aid. A drawback of a typical exoskeleton is that in order to function correctly the axes need to be aligned to the human joints, which is time consuming. Furthermore, an exoskeleton for healthcare must reduce the reaction forces in the user while lifting. It is chosen to design an exoskeleton, which requires no adjustment and can cope with power enhancement. The goal of this paper is to design an exoske-leton that is fast, easy to use and reduces the reaction forces in the user. A design is proposed which easily fits different sized users. In addition the reaction forces in the human skeleton are eliminated. The model is tested by means of a demonstrator. Elastic tension elements are used as a gravity compensator. By reducing the potential energy fluctuation the required external input is reduced. Optimizing a number of design parameters leads to a calculated moment reduction of 98.8% and moment fluctuation reduction of 96.8%. This is the first exoskeleton to combine fast to use design with high-energy efficiency gravity compensation.

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