Most clinical tools for measuring spasticity, such as the Modified Ashworth Scale (MAS) and the Modified Tardieu Scale (MTS), are not sufficiently accurate or reliable. This study investigated the clinimetric properties of an instrumented spasticity assessment. Twenty-eight children with spastic cerebral palsy (CP) and 10 typically developing (TD) children were included. Six of the children with CP were retested to evaluate reliability. To quantify spasticity in the gastrocnemius (GAS) and medial hamstrings (MEH), three synchronized signals were collected and integrated: surface electromyography (sEMG); joint-angle characteristics; and torque. Muscles were manually stretched at low velocity (LV) and high velocity (HV). Spasticity parameters were extracted from the change in sEMG and in torque between LV and HV. Reliability was determined with intraclass-correlation coefficients and the standard error of measurement; validity by assessing group differences and correlating spasticity parameters with the MAS and MTS. Reliability was moderately high for both muscles. Spasticity parameters in both muscles were higher in children with CP than in TD children, showed moderate correlation with the MAS for both muscles and good correlation to the MTS for the MEH. Spasticity assessment based on multidimensional signals therefore provides reliable and clinically relevant measures of spasticity. Moreover, the moderate correlations of the MAS and MTS with the objective parameters further stress the added value of the instrumented measurements to detect and investigate spasticity, especially for the GAS.

This paper introduces the OROCOS project (www. orocos.org), that aims at becoming a general-purpose and open robot control software package. OROCOS follows the Open Source development model that has been proven to work in many other general-purpose software packages, such as Linux, Apache, Perl, or L A T E X. The paper focuses on the long-term vision of this start-up project, motivates which strategic and innovative design decisions are to be taken (a CORBA(like) component architecture being the most important one, [14]), and lists other projects on which ORO-COS could build. The success of OROCOS depends critically on how many researchers and engineers can be motivated to contribute code, documentation and feedback to the project.

This paper introduces a systematic constraint-based approach to specify complex tasks of general sensorbased robot systems consisting of rigid links and joints. The approach integrates both instantaneous task specification and estimation of geometric uncertainty in a unified framework. Major components are the use of feature coordinates, defined with respect to object and feature frames, which facilitate the task specification, and the introduction of uncertainty coordinates to model geometric uncertainty. While the focus of the paper is on task specification, an existing velocity based control scheme is reformulated in terms of these feature and uncertainty coordinates. This control scheme compensates for the effect of time varying uncertainty coordinates. Constraint weighting results in an invariant robot behavior in case of conflicting constraints with heterogeneous units.The approach applies to a large variety of robot systems (mobile robots, multiple robot systems, dynamic human-robot interaction, etc.), various sensor systems, and different robot tasks. Ample simulation and experimental results are presented.

Paper [1] generalizes the Kalman Filter to nonlinear systems by transforming approximations of the probability distributions through the nonlinear process and measurement functions. This Comment derives exactly the same estimator by linearizing the process and measurement functions by a statistical linear regression through some sampling points (in contrast with the Extended Kalman Filter which uses an analytic linearization in one point). This insight allows (i) to understand/predict the performance of the estimator for specific applications and (ii) to make adaptations to the estimator (i.e., the choice of the sampling points and their weights) in those cases where the original formulation does not assure good results.

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