Dagmar Sternad

   Departments of Biology, Electrical & Computer Engineering,
   and Physics

Northeastern University
134 Mugar Life Science Building

360 Huntington Avenue

Boston, MA 02115

Phone : 617.373.5093

e-mail : dagmar@neu.edu

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Research in the Action Lab

Research in the Action Lab is dedicated to the experimental and theoretical study of human motor control and learning. Our experiments collect kinematic and kinetic data, complemented by electromyographic and brain imaging data. Physical models of the task provide understanding of the task solutions as basis for comparison with human data.

  Six major lines of research investigate the acquisition, control, and retention of motor skills.


Variability and Stability in Skill Acquisition We developed a new approach that quantifies acquisition and performance in motor skills by decomposing performance variability into three components: Tolerance, Noise, Covariation. These concepts serve to design novel interventions to improve and accelerate skill acquisition.

bullet From Action to Interaction: Complex Object Manipulation This research examines human interaction with complex objects, in particular complex objects with internal dynamics. Using the exemplary task of carrying a cup of coffee we examine how interaction ensure predictability of the object dynamics.

bullet Dynamic Primitives We test the hypothesis that complex human actions are generated by employing dynamic primitives: submovements, oscillations and impedance. These basic units are combined to control complex interactive behavior.

bullet Learning and Retention of Asymmetric Bimanual Actions This line of work examines self-guided learning over extensive practice period until individualistic movement patterns are stabilized. We further use retention tests after a long interval to establish behavioral correlates of neuroplasticity.

bullet Brain Measurements and Stimulation We use Electroencephalography (EEG), Transcranial Magnetic Stimulation (TMS), and Transcranial Direct Current Stimulation (tDCS) to obtain data for the three research questions above.

bulletVirtual Rehabilitation Using the Kinect system, we developed a clinician-friendly interactive platform to deliver therapeutic exercises to patients.

This research is supported by the National Institute of Child Health and Human Development (NICHD) R01 HD045639, National Science Foundation NSF-DMS0928587, the U.S. Army Research Institute for the Behavioral and Social Sciences (W5J9CQ-12-C-0046), and Northeastern Provost Tier I Funding. Students and Postdocs have been funded by fellowships from the Northeastern University Graduate School of Engineering, The Mathworks, and a NIH Kirschstein Postdoctoral Fellowship.

Variability and Stability in Skill Acquisition
In the inquiry of acquisition and control of motor skills the concepts of stability and variability have played a central role, albeit with many different definitions. Most commonly, improvement of performance is associated with a decrease in variability of some task parameters. This reduced variability, in turn, has been interpreted as an increase in stability. This simple inverse relationship obscures that empirical variability can be indicative of many different facets, ranging from the obvious "lack of control", seen as errors in target-oriented tasks, to more beneficial aspects, such as compensatory variation between parameters, and exploration. Dynamical stability is also a formally rigorous concept that can be quantified independently from measured variability. This research examines skill acquisition in two selected tasks to differentiate our understanding of variability and stability in human performance.
In skittles, a target-oriented throwing action predominantly under feedforward control, we develop a method to decompose variability into three independent components: Tolerance, Noise, and Covariation (TNC-decomposition), each capturing a different contribution to successful performance. Experiments test how different components of variability contribute in different stages of learning, and how stochastic noise can be a means to find successful solutions.
The second task is the continuous perceptually-guided skill of rhythmically bouncing a ball. Experiments examine how acquisition of the skill is characterized by an increasing reliance on dynamical stability. In conjunction, performance variability is analyzed using the TNC-method to examine how different components contribute to this change in stability.

Selected Publications:
  1. Abe, M.O., & Sternad, D. (2013). Directionality in distribution and temporal structure of variability in skill acquisition. Frontiers in Human Neuroscience, 7:225.
  2. Sternad, D., Abe, M.O., Hu, X., & Muller, H. (2011). Neuromotor noise, sensitivity to error and signal-dependent noise in trial-to-trial learning. PLoS Computational Biology, 7, 9, e1002159.
  3. Cohen, R.G., & Sternad, D. (2009). Variability in motor learning: Relocating, channeling and reducing noise. Experimental Brain Research, 193, 1, 69-83.
  4. Sternad, D., Park, S., Muller, H., & Hogan, N. (2010). Coordinate dependence of variability analysis. PLoS Computational Biology, 6, 4, e1000751.
  5. Ronsse, R. & Sternad, D. (2010). Bouncing between model and data: stability, passivity, and optimality in hybrid dynamics. Journal of Motor Behavior, 6, 387-397.

From Action to Interaction: Complex Object Manipulation

object manipulation
The manipulation of complex objects, or tool use, is ubiquitous in everyday life and has given humans their evolutionary advantage. Such object interactions become particularly intriguing when the objects themselves have internal degrees of freedom and add complex dynamics to the interaction. For example, when bringing a cup of coffee to one's mouth, the coffee is only indirectly controlled via moving its container. The dynamics of the sloshing coffee creates complex interaction forces that the person has to take into account during control. To gain insight into the control mechanisms underlying this remarkable skill, this research examines the strategies that humans choose when manipulating an object with complex internal dynamics, mimicking a cup of coffee. Using the cart-and-pendulum model implemented in a virtual environment as a cup containing a rolling ball, our research studies the strategies that people choose when manipulating this object via a robotic manipulandum. One study examined point-to-point movements, quantifying how the safety margin changes with practice as a function of time constraints. The same task sensitively shows how older people have larger safety margins, i.e. a higher risk of spilling. When subjects move the "cup of coffee" in continuous rhythmic fashion, the complex nonlinear dynamics comes to the fore. Analysis of forces show that subjects seek strategies that make the interaction object more predictable, avoiding chaos.

Selected Publications:
  1. Nasseroleslami, B., Hasson, C.J., & Sternad, D. (2014). Rhythmic manipulation of objects with complex dynamics: Predictability over chaos. PLoS Computational Biology.
  2. Hasson, CJ & Sternad, D (2014). Safety margins in older adults increase with improved control of a dynamic object. Frontiers in Aging Neuroscience, 6: 158, doi: 10.3389/fnagi.2014.00158
  3. Hasson, C.J., Shen, T., & Sternad, D. (2012). Energy margins in dynamic object manipulation. Journal of Neurophysiology, 108, 5, 1349-65.

Dynamic Primitives

Dynamic PrimitivesDaily activities consist of a coordinated sequence or combination of rhythmic and discrete movements. Examples range from rhythmic locomotion when it is combined with stepping over obstacles, to rhythmic finger actions in piano playing while translating the hand over the keyboard. A longstanding question in motor control is whether such complex actions are controlled by simpler units that can be regarded as control primitives. Due to fundamental features of the neuromuscular system, most notably its slow response, we argue control may be in terms of parameterized primitives, which may simplify learning, performance, and retention of complex skills. Specifically, the hypothesis is that submovements, oscillations, and impedance primitives that are coupled to generate complex movements, the latter necessary for interaction with objects and the environment.
To test this hypothesis, we examine single-joint and multi-joint movements in unconstrained and interactive tasks. In the experiments participants perform rhythmic movements paced by a metronome interspersed with discrete changes in their trajectory, or in transition from slow to fast movements to identify constraints of these primitives and how they may be coupled.

Selected Publications:

  1. Sternad, D., Marino, H., Duarte, M., Dipietro, L., Charles, S., & Hogan, N. (2013). Transitions between discrete and rhythmic primitives in a unimanual task. Frontiers in Computational Neuroscience, 7:90.
  2. Hogan, N. & Sternad, D. (2013). Dynamic primitives in the control of locomotion. Frontiers in Computational Neuroscience, 7:71.
  3. Hogan, N., & Sternad, D. (2012). Dynamic primitives of motor behavior. Biological Cybernetics, 106 (11-12), 727-739.
  4. Sternad, D. & Dean, W.J. (2003). Rhythmic and discrete elements in multi-joint coordination. Brain Research, 989, 152-171.

Learning and Retention of Asymmetric Bimanual Actions

Se-Woong text
Despite anecdotal reports that humans retain acquired motor skills for many years, if not a lifetime, long-term memory of motor skills has received little attention. Several recent neuroimaging and electrophysiological studies on animals and humans revealed details of neuroplasticity underlying motor skill learning and motor memory. Advances notwithstanding, characterization of changes in observable behavior has been limited to short-term retention and to relatively gross measures of task achievement. To complement the understanding of practice-induced neural changes, our longitudinal studies present fine-grained kinematic characterization over extensive practice, followed by retention tests after 3-6 months and after 8 years! The experiments involve asymmetric bimanual tasks, performed either in continuous rhythmic fashion or as combination of rhythmic and discrete elements. Results suggest that motor memory may comprise not only higher-level task achievement, but also individual kinematic signatures.

Selected Publications:
  1. Park, S-W, Dijkstra, TMH, & Sternad, D (2013). Learning to never forget: Time scales and specificity of long-term memory of a motor skill. Frontiers in Computational Neuroscience, 7:111. 
  2. Park, S-W & Sternad, D (under review). Self-guided learning and long-term retention.

Brain Measurements and Stimulation

EEGComplementing our behavioral measurements and modeling, we recently started to use Electroencephalography (EEG), Transcranial Magnetic Stimulation (TMS), and Transcranial Direct Current Stimulation (tDCS). With single-pulse TMS we examine the question how discrete and rhythmic movements have differential involvement of cortical regions. EEG and tDCS is used to obtain additional measurements to better characterize the learning process, both in bimanual skill acquisition and in the throwing task skittles.

Virtual Rehabilitation

virtual rehabilitation
Physical rehabilitation based on virtual reality, or virtual rehabilitation, provides several advantages over conventional therapy, including an increased capacity to (1) deliver real-time performance feedback through visual and auditory modalities, (2) obtain quantitative measures of progress and compliance, (3) enhance motivation and entertainment to improve adherence, and (4) deliver patient-specific treatment that adapts with functional improvements over training. We aim to expedite and enhance motor recovery by utilizing motor learning principles derived from our basic science research on human motor control into virtual rehabilitation protocols. We also use our expertise in quantifying human movements to test the validity and reliability of sensors for in-home rehabilitation.

Selected Publications:
  1. Huber, M.E., Seitz, A., Leeser, M., & Sternad, D. (2014). Validity and reliability of Kinect for measuring shoulder joint angles. IEEE Proceedings of the 40th Northeast Bioengineering Conference, Boston, MA, April 25-27.
  2. Huber, M.E., Leeser, M., & Sternad, D. (2013). Development of a low-cost, adaptive, clinician-friendly virtual rehabilitation system. Proceedings for the 10th International Conference on Rehabilitation, Virtual Rehabilitation (ICVR), pp.172-173. Philadelphia, PA, August 26-29.
  3. Huber, M., Rabin, B., Docan, C., Burdea, G. C., AbdelBaky, M., & Golomb, M. R. (2010). Feasibility of modified remotely monitored in-home gaming technology for improving hand function in adolescents with cerebral palsy. Information Technology in Biomedicine, IEEE Transactions on, 14(2), pp. 526-534.