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

The Action Lab is a research facility dedicated to the experimental study of human motor control. More specifically, we study human action and perception focusing on physical and mechanical aspects of both the performer and the task.

Five major lines of our research investigate the generation of perceptually controlled behavior in biological systems.


Discrete and rhythmic elements in single- and multi-joint movements. The hypothesis is that unconstrained multi-joint movements can be understood in terms of two fundamental units of action: discrete movements and rhythmic movements. 

bullet Dual-task locomotor taining in older individuals and stroke survivors

bullet Tuning into dynamic stability. The complex task of bouncing a ball is studied in a task-based approach, where dynamical stability provides the framework for defining successful performance.

bullet The role of resonance properties of the limb in rhythmic movements. We show that resonance properties are essential in understanding rhythmic variability and tracking performance.

bullet Variability and stability in skill acquisition. We developed a new approach to quantify performance and change in redundant tasks. The method decomposes variability into three components that quantify different aspects and stages of skill improvement.

This research is supported by supported by the National Science Foundation grant BCS-0904464, DMS-0928587, Deutsche Forschungsgesellschaft DFG-MU 1374/3-1, National Institutes of Health R01HD045639.

Discrete and Rhythmic Elements in Single and Multi-Joint Movements

Daily activities consist of a coordinated sequence or combination of cyclic and translatory elements. 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 is motor control is whether such complex actions can be decomposed into simpler units that can be regarded as primitives. The hypothesis of this line of research is that multijoint coordination can be understood as consisting of discrete and rhythmic primitives that are coupled to produce complex movements. To test this hypothesis, we examine single-joint and multi-joint tasks consisting of rhythmic and discrete task elements. We measure kinematic trajectories and muscular activity measured by EMG. In one experiment participants perform oscillatory movements in the horizontal plane paced by a metronome interspersed with discrete changes in their trajectory. We identified constraints in how these two units can be coupled together. Analyses showed that these constraints arise at the neuro-muscular level, such that EMG bursts of the discrete and rhythmic movement have a tendency to synchronize.

Sternad, D., de Rugy, A., Pataky, T., & Dean, W.J. (2002). Interactions between rhythmic and discrete elements over a wide range of movement periods. Experimental Brain Research, 147, 162-174. 

De Rugy, A., & Sternad, D. (2003). Interaction between discrete and rhythmic movements: reaction time and phase of discrete movement initiation against oscillatory movements. Brain Research, 994, 160-174. 

Sternad, D. & Dean, W.J. (2003). Rhythmic and discrete elements in multi-joint coordination. Brain Research, 989, 152-171. 

Wei, K., Wertman, G., & Sternad, D. (2003). Interactions between rhythmic and discrete components in a bimanual task. Motor Control, 7, 2, 134-155.

The Role of Resonance Properties of the Limb in Rhythmic Movements

Timing is an essential component in the control of all human movements. Behavioral studies of rhythmic timing have a long tradition where rhythmic finger tapping has been the most common paradigm. In order to investigate the influence of inertial properties of the moving limb, particularly its resonance frequency, we have used wrist-pendular movements where the pendular manipulanda easily allow modification of the resonance frequency. In a variety of experimental manipulations we showed that the resonance frequency systematically determines the subjectively preferred frequency. In movements paced at different frequencies we observed an increase in the variability of timing proportional to the discrepancy between the pacing and the preferred frequency. Additionally, in longer trials we observed a systematic drift towards the resonance frequency. These results were captured in a coupled oscillator model consisting of internal and mechanical oscillators. In a recent experiment, we investigated the robustness of the preferred frequency by submitting rhythmic movements to external force fields. The presence of external torques did not affect the preferred frequency following the removal of the force field. These results give evidence that the neuromuscular frequency is a relatively robust quantity.

Yu, H., Russell, D.M., & Sternad, D. (2003). Task-effector asymmetries in a rhythmic continuation task. Journal of Experimental Psychology: Human Perception and Performance, 29, 3, 616-630.

Russell, D.M., & Sternad, D. (2001). Sinusoidal visuomotor tracking: Intermittent servo-control or coupled oscillations? Journal of Motor Behavior, 33, 4, 329-349.

Russell, D., de Rugy, A., & Sternad (submitted). The role of resonance frequency in rhythmic visuo-motor control. Experimental Brain Research.

Yu, H., Kalveram, K-T., & Sternad, D. (in preparation). Robustness of neuromechanical resonance frequency under force field perturbations.

Tuning into Dynamical Stability - Bouncing a Ball

Rhythmically bouncing a ball is a perceptual-motor task that poses all the challenges present in the control of movements. How do we control arm and racket movements to hit a ball at the appropriate position to achieve a given target height? This study presents a task-based approach in understanding the principles in movement control. Starting with a kinematic model of the ball-racket system, we derived predictions for a dynamically stable control of the ball. This strategy is computationally efficient as small errors do not require explicit corrections. A series of experiments evaluated whether human actors exploit and optimize dynamical stability and what perceptual support is necessary for stable behavior. In a series of experiments we showed that participants performed the task with ball-racket contacts that were consistent with model predictions: Humans indeed tuned their performance to exploit dynamical stability. This line of research was extended by developing a virtual set-up in which subjects manipulate a racket, but the ball only exists in the virtual environment. In one experiment we applied large perturbations to study how actors regain stability. Results revealed that an adjustment of the racket period ensured that the impacts occurred at a phase associated with dynamical stability. These findings were simulated in a model consisting of a neural oscillator that drives a mechanical actuator (forearm holding the racket) to bounce the ball

Dijkstra, T.M.H.,  Katsumata, H., de Rugy, A., & Sternad, D. (2004). The dialogue between data and model: Passive stability and relaxation behavior in a ball bouncing task. Journal of Nonlinear Science.11, 3, 319- 345.

De Rugy, A., Wei, K., Muller, H., & Sternad, D. (2003). Actively tracking 'passive' stability. Brain Research, 982, 1, 64-78.

Sternad, D., Duarte, M., Katsumata, H., & Schaal, S. (2001). Bouncing a ball: Tuning into dynamic stability. Journal of Experimental Psychology: Human Perception and Performance, 27, 5, 1163-1184.

Sternad, D., Duarte, M., Katsumata, H., & Schaal, S. (2000). Dynamics of a bouncing ball in human performance. Physical Review E, 63, 011902-1 -011902-8.

Variability and Stability in Skill Acquisition

In the inquiry of acquisition and control of skills the concepts of stability and variability have played a central role, albeit with many different definitions and levels of rigor. 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 adaptation to new tasks. 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, covariation, and noise (TCN-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 as described above. Experiments examine how acquisition of the skill is characterized by an increasing reliance on dynamical stability. In conjunction, performance variability is analyzed using the TCN-method to examine how different components contribute to this change in stability.

Muller, H., & Sternad, D. (2004). Decomposition of variability in the execution of goal-oriented tasks - Three components of skill improvement.  Journal of Experimental Psychology: Human Perception and Performance, 30,1, 212-233.

Muller, H. & Sternad, D. (2003). A randomization method for the calculation of covariation in multiple nonlinear relations: Illustrated at the example of goal-directed movements. Biological Cybernetics, 89, 22-33.