Self-aided manipulator system for bed-ridden patients - Evaluation of psychological influence for the generated approach motion -
ABSTRACT Rehabilitation robots that are used to assist patients should be able to move naturally and cause no discomfort to the patients. In this study, a self-aided manipulator system that can generate natural motion and assist bed-ridden patients has been developed. The self-aided manipulator is designed so that it can grasp a glass of water from a side table for the patient. The system can detect the starting position (position of the glass) and the target position (position of the patient's lips) by using a video camera. The direction angle of the patient's face is also determined, and approach motions are generated on the basis of these angles. Approach motions are generated by changing the peak velocity time, maximum speed, and detour point position of the manipulator, and the psychological influence on the patient is evaluated on the basis of their heart rate variability (HRV), skin potential response (SPR), and response to questionnaires. The results suggested that a peak velocity time of 75% of the total movement time, a maximum speed of 36 cm/s, and an approach path from the right when the patient's head is facing straight or to the left tended to have the strongest psychological influence on the patient. From these results, it was indicated that the following conditions are preferable for the approach motion of the manipulator: a peak velocity time of approximately 25 to 50% of the total movement time, a maximum speed from 18 to 24 cm/s, and an approach path in the direction in which the head faces.
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Article: Rehabilitation robotics: a review.[show abstract] [hide abstract]
ABSTRACT: This article reviews various selected literature on rehabilitation robotics. The literature was obtained mainly from journals and conference proceedings of the robotic, rehabilitative or biomedical engineering associations. It has been classified into three categories: rehabilitation robot systems, evaluation and key technologies. Commercially available robots, new projects, users' experiences and requirements, and fundamental research are introduced. A comprehensive list of references is provided.Advanced Robotics. 01/2001; 14:551-564.
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ABSTRACT: The authors argue that intelligence is necessary in robots used for rehabilitation in order to reduce the amount of mental activity needed by the user of these robots. With this in mind, the areas of research relevant to imparting robotic systems with the capability of assuming a more intelligent role are identified. The authors describe their implementation of functionalities such as fuzzy command interpretation, object recognition, face tracking, and task planning and learning, which are part of the ISAC, an intelligent system designed to feed individuals with physical disabilitiesIEEE Transactions on Rehabilitation Engineering 04/1995;
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ABSTRACT: The authors describe a general-purpose, representation-independent method for the accurate and computationally efficient registration of 3-D shapes including free-form curves and surfaces. The method handles the full six degrees of freedom and is based on the iterative closest point (ICP) algorithm, which requires only a procedure to find the closest point on a geometric entity to a given point. The ICP algorithm always converges monotonically to the nearest local minimum of a mean-square distance metric, and the rate of convergence is rapid during the first few iterations. Therefore, given an adequate set of initial rotations and translations for a particular class of objects with a certain level of `shape complexity', one can globally minimize the mean-square distance metric over all six degrees of freedom by testing each initial registration. One important application of this method is to register sensed data from unfixtured rigid objects with an ideal geometric model, prior to shape inspection. Experimental results show the capabilities of the registration algorithm on point sets, curves, and surfacesIEEE Transactions on Pattern Analysis and Machine Intelligence 03/1992; 14(2):239-256. · 4.80 Impact Factor