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Interactions with simulated robots are typically presented on screens. Virtual reality (VR) offers an attractive alternative as it provides visual cues that are more similar to the real world. In this paper, we explore how virtual reality mediates human-robot interactions through two user studies. The first study shows that in situations where perception of the robot is challenging, a VR display provides...
Identifying an object of interest, grasping it, and handing it over are key capabilities of collaborative robots. In this context we propose a fast, supervised learning framework for learning associations between human hand gestures and the intended robotic manipulation actions. This framework enables the robot to learn associations on the fly while performing a task with the user. We consider a domestic...
This research paper explores the possibility of using Electromyogram (EMG) signals for classifying point to point upper limb movements during dynamic muscle contraction in the context of human-robot interactions. Previous studies have mostly focused on classifiers for gesture recognition using steady state EMG. Only few studies have used non-steady-state EMG classifier when gross upper arm muscles...
This study presents an age and gender estimation system that considers ethnic difference in face images using a Convolutional Neural Network(CNN) and Support Vector Machine(SVM). Most age and gender estimation systems using face images are trained on ethnicity-biased databases. Therefore, these systems show limited performance on face images of ethnic groups occupying a small proportion of the training...
The goal of the Internet of Things (IoT) is to transform any thing around us, such as a trash can or a street light, into a smart thing. A smart thing has the ability of sensing, processing, communicating and/or actuating. In order to achieve the goal of a smart IoT application, such as minimizing waste transportation costs or reducing energy consumption, the smart things in the application scenario...
Localizing functional regions of objects or affordances is an important aspect of scene understanding and relevant for many robotics applications. In this work, we introduce a pixel-wise annotated affordance dataset of 3090 images containing 9916 object instances. Since parts of an object can have multiple affordances, we address this by a convolutional neural network for multilabel affordance segmentation...
We present DeepNav, a Convolutional Neural Network (CNN) based algorithm for navigating large cities using locally visible street-view images. The DeepNav agent learns to reach its destination quickly by making the correct navigation decisions at intersections. We collect a large-scale dataset of street-view images organized in a graph where nodes are connected by roads. This dataset contains 10 city...
This is a placeholder for the invited session presenting the results of the Data Fusion Contest 2017. Papers in the session do not enter the usual review process. Instead, winners of the 2017 Data Fusion Contest will write 4-page papers evaluated by the Contest Committee, that will be submitted as camera-ready papers in due date. So, DO NOT REVIEW THIS PAPER. If by mistake it enters the usual review...
While there has been recent success with robotic therapy approaches, individual differences in motor impairments motivate the need for customized therapy. Our latest work with healthy participants considered the likelihood of one's error to construct a customized force field training environment, which we termed an error field. We believe error statistics could characterize individual motor impairments...
The combined use of Functional Electrical Stimulation (FES) and robotic technologies is advocated to improve rehabilitation outcomes after stroke. This work describes an arm rehabilitation system developed within the European project RETRAINER. The system consists of a passive 4-degrees-of-freedom exoskeleton equipped with springs to provide gravity compensation and electromagnetic brakes to hold...
The authors are developing a talking robot which is a mechanical vocalization system modeling the human articulatory system. The talking robot is constructed with mechanical parts that are made by referring to human vocal organs biologically and functionally. In this study, a newly redesign artificial vocal cord is developed for the purpose of extending the speaking capability of the talking robot...
Uniform test suites consist of test cases exclusively differing in test inputs - not in test goals. Intended to gain confidence that a given invariant holds, these inputs trigger particular behavior of the system under test. Equipped with a simulation of the system under test we are able to cheaply explore this behavior virtually. When changing over to reality, testing the system within its real context,...
In daily life it is necessary to learn skills that can be applied in different tasks and different contexts. Usually these skills are acquired by observation or by direct physical training with another expert person. The critical point is to know which is the best possible way to achieve this knowledge acquisition. In this work we have proposed a collaborative environment where subjects with different...
In this paper, we investigate active brain regions for motor execution and motor imagination tasks after training with a rehabilitation robot. Functional near-infrared spectroscopy (fNIRS) is used to measure the hemodynamic responses in the motor cortices of five subjects. An assistive robot (IMT 2.0, connected to the right hand) is used during the training session to make the subject to reach a target...
Electroactile feedback is crucial to close the loop systems of the teleoperation system, virtual reality system, and prosthetic system. Feedback devices are always limited in application due to large size for their inconvenience. In the present study, a wearable armband named “iFeel” is developed, which includes five pairs of electrodes for electrotactile stimulation is introduced for feedback to...
We investigate different strategies for active learning with Bayesian deep neural networks. We focus our analysis on scenarios where new, unlabeled data is obtained episodically, such as commonly encountered in mobile robotics applications. An evaluation of different strategies for acquisition, updating, and final training on the CIFAR-10 dataset shows that incremental network updates with final training...
This paper presents the iterative development of an artificially intelligent system to promote home-based neurorehabilitation. Although proper, structured practice of rehabilitation exercises at home is the key to successful recovery of motor functions, there is no home-program out there which can monitor a patient's exercise-related activities and provide corrective feedback in real time. To this...
There is a demand for a new neurorehabilitation modality with a brain-computer interface for stroke patients with insufficient or no remaining hand motor function. We previously developed a robotic hand rehabilitation system triggered by multichannel near-infrared spectroscopy (NIRS) to address this demand. In a preliminary prototype system, a robotic hand orthosis, providing one degree-of-freedom...
Motor relearning after stroke is a lengthy process which should be continued after patients get discharged from the clinic. This project aims at developing a system for telerehabilitation which enables stroke patients to exercise at home autonomously or under supervision of a therapist. The system includes haptic therapy devices which are more promising and beneficial for stroke rehabilitation than...
To prevent learned non-use of the affected hand in chronic stroke survivors, rehabilitative training should be continued after discharge from the hospital. Robotic hand orthoses are a promising approach for home rehabilitation. When combined with intuitive control based on electromyography, the therapy outcome can be improved. However, such systems often require extensive cabling, experience in electrode...
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