Robotic mobility assistance devices provide a purposeful and adaptive support for elderly humans. To achieve assistance which is specifically adapted to the individual needs and capabilities of the user, the device should be capable of estimating the overall gait performance as well as the current state of the subject on-line. Information gathered by on-board sensors could be exploited to obtain, amongst other values, the interaction forces, positions and velocities of the subject. Although in principle it would be desirable to obtain whole-body motion information of the precision of data collected by marker-based motion capture systems, this is not possible by the on-board sensors and computational units. It is generally also not acceptable to make subjects wear additional accessories during the every-day usage of the device. In this paper, we present methods to identify gait parameters based on easily accessible on-line sensor information. We derive temporo-spatial gait performance indicators which can be used by robotic mobility aid platforms to evaluate the performance of the user and apply individualized user-adaptive assistance strategies. By analyzing the correlation between these parameters we propose a set of gait performance indicators and derive performance classes that can be used in combination or isolated.
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