A Comparative Analysis of Speed Profile Models for Ankle Pointing Movements: Evidence that Lower and Upper Extremity Discrete Movements are Controlled by a Single Invariant Strategy.

Little is known about whether our knowledge of how the central nervous system controls the upper extremities (UE), can generalize, and to what extent to the lower limbs. Our continuous efforts to design the ideal adaptive robotic therapy for the lower limbs of stroke patients and children with cerebral palsy highlighted the importance of analyzing and modeling the kinematics of the lower limbs, in general, and those of the ankle joints, in particular. We recruited 15 young healthy adults that performed in total 1,386 visually evoked, visually guided, and target-directed discrete pointing movements with their ankle in dorsal-plantar and inversion-eversion directions. Using a non-linear, least-squares error-minimization procedure, we estimated the parameters for 19 models, which were initially designed to capture the dynamics of upper limb movements of various complexity. We validated our models based on their ability to reconstruct the experimental data. Our results suggest a remarkable similarity between the top-performing models that described the speed profiles of ankle pointing movements and the ones previously found for the UE both during arm reaching and wrist pointing movements. Among the top performers were the support-bounded lognormal and the beta models that have a neurophysiological basis and have been successfully used in upper extremity studies with normal subjects and patients. Our findings suggest that the same model can be applied to different "human" hardware, perhaps revealing a key invariant in human motor control. These findings have a great potential to enhance our rehabilitation efforts in any population with lower extremity deficits by, for example, assessing the level of motor impairment and improvement as well as informing the design of control algorithms for therapeutic ankle robots.

Integration of basic science and clinical medicine: the innovative approach of the cadaver biopsy project at the Boston University School of Medicine.

PROBLEM: Curricular integration has emerged as a consistent theme in medical education reform. Vertical integration of topics such as pathology offers the potential to bring basic science content into the clinical arena, but faculty/student acceptance and curricular design pose challenges for such integration. APPROACH: The authors describe the Cadaver Biopsy Project (CBP) at Boston University School of Medicine as a sustainable model of vertical integration. Faculty and select senior medical students obtained biopsies of cadavers during the first-year gross anatomy course (fall 2009) and used these to develop clinical cases for courses in histology (spring 2010), pathology (fall 2010-spring 2011), and radiology (fall 2011 or spring 2012), thereby linking students' first experiences in basic sciences with other basic science courses and later clinical courses. Project goals included engaging medical stu dents in applying basic science princi ples in all aspects of patient care as they acquire skills. The educational intervention used a patient (cadaver)-centered approach and small-group, collaborative, case-based learning. OUTCOMES: Through this project, the authors involved clinical and basic science faculty-plus senior medical students-in a collaborative project to design and implement an integrated curriculum through which students revisited, at several different points, the microscopic structure and pathophysiology of common diseases. NEXT STEPS: Developing appropriate, measurable out comes for medical education initiatives, including the CBP, is challenging. Accumu lation of qualitative feedback from surveys will guide continuous improvement of the CBP. Documenting longer-term impact of the curricular innovation on test scores and other competency-based outcomes is an ultimate goal.

A comparative analysis of speed profile models for wrist pointing movements.

Following two decades of design and clinical research on robot-mediated therapy for the shoulder and elbow, therapeutic robotic devices for other joints are being proposed: several research groups including ours have designed robots for the wrist, either to be used as stand-alone devices or in conjunction with shoulder and elbow devices. However, in contrast with robots for the shoulder and elbow which were able to take advantage of descriptive kinematic models developed in neuroscience for the past 30 years, design of wrist robots controllers cannot rely on similar prior art: wrist movement kinematics has been largely unexplored. This study aimed at examining speed profiles of fast, visually evoked, visually guided, target-directed human wrist pointing movements. One thousand three-hundred ninety-eight (1398) trials were recorded from seven unimpaired subjects who performed center-out flexion/extension and abduction/adduction wrist movements and fitted with 19 models previously proposed for describing reaching speed profiles. A nonlinear, least squares optimization procedure extracted parameters' sets that minimized error between experimental and reconstructed data. Models' performances were compared based on their ability to reconstruct experimental data. Results suggest that the support-bounded lognormal is the best model for speed profiles of fast, wrist pointing movements. Applications include design of control algorithms for therapeutic wrist robots and quantitative metrics of motor recovery.

Application of singular spectrum-based change-point analysis to EMG-onset detection.

While many approaches have been proposed to identify the signal onset in EMG recordings, there is no standardized method for performing this task. Here, we propose to use a change-point detection procedure based on singular spectrum analysis to determine the onset of EMG signals. This method is suitable for automated real-time implementation, can be applied directly to the raw signal, and does not require any prior knowledge of the EMG signal's properties. The algorithm proposed by Moskvina and Zhigljavsky (2003) was applied to EMG segments recorded from wrist and trunk muscles. Wrist EMG data was collected from 9 Parkinson's disease patients with and without tremor, while trunk EMG data was collected from 13 healthy able-bodied individuals. Along with the change-point detection analysis, two threshold-based onset detection methods were applied, as well as visual estimates of the EMG onset by trained practitioners. In the case of wrist EMG data without tremor, the change-point analysis showed comparable or superior frequency and quality of detection results, as compared to other automatic detection methods. In the case of wrist EMG data with tremor and trunk EMG data, performance suffered because other changes occurring in these signals caused larger changes in the detection statistic than the changes caused by the initial muscle activation, suggesting that additional criteria are needed to identify the onset from the detection statistic other than its magnitude alone. Once this issue is resolved, change-point detection should provide an effective EMG-onset detection method suitable for automated real-time implementation.