A Rehabilitation Walker with a Standing Assistance Device

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Network Distributed Monitoring System Based on Robot Technology Middleware

In this paper, a network distributed monitoring system for human assistance robot system was developed to improve the interaction among the users and local service robotic system and enable a remote user to get a better understanding of what is going on in the local environment. Home integration robot system and network monitoring system using QuickCam Orbit cameras were developed and demonstrated from June 9 to June 19 at the 2005 World Exposition, Aichi, Japan. Improvements of network distributed monitoring system using IEEE1394 cameras with high performance and high resolution have been done in order to extend the application of system. Robot Technology Middleware (RTM) was used in the developed system. By using RTM, we can develop cameras functional elements as “RT software components” that can be implemented by different programming languages, run in different operating system, or connected in different networks to inter-operate. It is also easy to create comprehensive robot system application by re-using existing modules thus facilitating network-distributed software sharing and improving the cost of writing and maintaining software

Construction of Gait Adaptation Model in Human Splitbelt Treadmill Walking

There are a huge number of studies that measure kinematics, dynamics, the oxygen uptake and so on in human walking on the treadmill. Especially in walking on the splitbelt treadmill where the speed of the right and left belt is different, remarkable differences in kinematics are seen between normal and cerebellar disease subjects. In order to construct the gait adaptation model of such human splitbelt treadmill walking, we proposed a simple control model and made a newly developed 2D biped robot walk on the splitbelt treadmill. We combined the conventional limit-cycle based control consisting of joint PD-control, cyclic motion trajectory planning and a stepping reflex with a newly proposed adjustment of P-gain at the hip joint of the stance leg. We showed that the data of robot (normal subject model and cerebellum disease subject model) experiments had high similarities with the data of normal subjects and cerebellum disease subjects experiments carried out by Reisman et al. (2005) and Morton and Bastian (2006) in ratios and patterns. We also showed that P-gain at the hip joint of the stance leg was the control parameter of adaptation for symmetric gaits in splitbelt walking and P-gain adjustment corresponded to muscle stiffness adjustment by the cerebellum. Consequently, we successfully proposed the gait adaptation model in human splitbelt treadmill walking and confirmed the validity of our hypotheses and the proposed model using the biped robot

A DYNAMIC PATTERN RECOGNITION METHOD USING THE PREVIEW CONTROLLED SACCADIC MOVEMENT OF THE DETECTOR Recognition independent of position, rotation, size, or slight deformation of the object.

The purpose of this paper is to suggest the use of biological movements of detectors for pattern recognition. The authors demonstrate a unique method for recognizing patterns composed of straight and curved lines such as characters or edge patterns, independent of position, rotation, size, or slight deformation. The principle of the method is, different from conventional scanning techniques, based on preview controlled curve tracking movement similar to the saccadic motion of the eye. The recognition depends on the time sequence of straight line, curve, angle, intersection, etc. Discrimination between a straight and a curved line, larger or small curvature, and obtuse or acute angle is based on the relative difficulty in tracking the line. For small patterns which lie completely within the field of the detector, a well-known multi-layered recognition device is superior. For larger patterns, however, which lie partially outside the field, the method proposed here is superior. The authors think that their method could be useful for the eyes of robots when combined with the kind of multi-layered pattern recognition devices described by Fukushima*). Notation The following notation is used in the paper: E: edge point I: pulse signal generated from the inside preview photo-cell Ppi Kj: constant K2: constant 0: pulse signal generated from the outside preview photo-cell Pp0 inside preview photo-cell outside preview photo-cell photo-cell for servo-tracking radius of curvature of segment of the pattern distance from an end point of a pattern to the detector tracking time, or T shaped part of a pattern tracking speed of optical detector, or angle wait straight line segment of a pattern straight line segment of a pattern arc or curved line segment of a pattern arc or curved line segment of a pattern intersection angle i.: measurement of pulses intervals-141