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Modern wearable robots are not yet intelligent enough to fully satisfy the demands of end-users, as they lack the sensor fusion algorithms needed to provide optimal assistance and react quickly to perturbations or changes in user intentions. Sensor fusion applications such as intention detection have been emphasized as a major challenge for both robotic orthoses and prostheses. In order to better examine the strengths and shortcomings of the field, this paper presents a review of existing sensor fusion methods for wearable robots, both stationary ones such as rehabilitation exoskeletons and portable ones such as active prostheses and full-body exoskeletons. Fusion methods are first presented as applied to individual sensing modalities (primarily electromyography, electroencephalography and mechanical sensors), and then four approaches to combining multiple modalities are presented. The strengths and weaknesses of the different methods are compared, and recommendations are made for future sensor fusion research. Overview of sensor fusion in wearable robots like prostheses and exoskeletons.Main sensors: electromyography, electroencephalography, and mechanical sensors.Emphasizes multimodality, adaptation and switching between sensor fusion schemes.Online evaluation of sensor fusion methods is crucial.

This paper presents low computational-complexity methods for micro-aerial-vehicle localization in GPS-denied environments. All the presented algorithms rely only on the data provided by a single onboard camera and an Inertial Measurement Unit (IMU). This paper deals with outlier rejection and relative-pose estimation. Regarding outlier rejection, we describe two methods. The former only requires the observation of a single feature in the scene and the knowledge of the angular rates from an IMU, under the assumption that the local camera motion lies in a plane perpendicular to the gravity vector. The latter requires the observation of at least two features, but it relaxes the hypothesis on the vehicle motion, being therefore suitable to tackle the outlier detection problem in the case of a 6DoF motion. We show also that if the camera is rigidly attached to the vehicle, motion priors from the IMU can be exploited to discard wrong estimations in the framework of a 2-point-RANSAC-based approach. Thanks to their inherent efficiency, the proposed methods are very suitable for resource-constrained systems. Regarding the pose estimation problem, we introduce a simple algorithm that computes the vehicle pose from the observation of three point features in a single camera image, once that the roll and pitch angles are estimated from IMU measurements. The proposed algorithm is based on the minimization of a cost function. The proposed method is very simple in terms of computational cost and, therefore, very suitable for real-time implementation. All the proposed methods are evaluated on both synthetic and real data. Low computational complexity methods for MAVs localization in GPS-denied environments and outlier detection.Platform: quadrotor equipped with a monocular camera and an IMU.Performance evaluation on both synthetic and real data.

A prosthetic device for disabled people requires new and reliable robotics technology. This paper describes the interesting reaction of our brain to an adaptable prosthetic system. The adaptable prosthetic system is composed of an EMG signal controlled robot hand with an EMG pattern recognition learning function for Transradial (below elbow) prostheses. The mutual adaptation between the system and the human body is analyzed using functional magnetic resonance imaging (f-MRI) in order to clarify the plasticity of the motor and sensory cortex area according to the changes in the prosthetic system. The developed prosthetic hand has 13 DOF: three motors on the thumb, two motors for each finger, and two motors for the wrist. The tactile feedback is applied by using surface electrical stimulus. The f-MRI data shows the process of replacement from a phantom limb image to the prosthetic hand image.

This paper describes the interactive tactile luminous floor that was constructed and used as the skin of the playful interactive space Ada, which ran as a public exhibit for five months in 2002 and had over 550,000 visitors. Ada's floor was custom-built to provide a means for individual and collective user interaction. It consists of 360 hexagonal 66 cm tiles covering a total area of 136 m^2, each with analogue tactile load sensors based on force-sensitive resistors and dimmable neon red, green and blue (RGB) lamps. The tiles are constructed from extruded aluminum with glass tops. An Interbus factory automation bus senses and controls the tiles. Software is described for rendering fluid, dynamic visual effects on the floor, for signal processing of the load information, for real-time visitor tracking and for a variety of behavioural modes, games and interactions. Data from single tiles and from tracking are shown. This floor offers new modalities of human-computer interaction and human-robot interaction for autonomous robotic spaces.