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.
||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.
||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.
||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.
||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.
|Virtual 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.
- Abe, M.O., & Sternad, D. (2013). Directionality
in distribution and temporal structure of variability in skill
acquisition. Frontiers in Human Neuroscience, 7:225.
- 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,
- Cohen, R.G., & Sternad, D. (2009). Variability in
motor learning: Relocating, channeling and reducing noise. Experimental
Brain Research, 193, 1, 69-83.
- Sternad, D., Park, S., Muller, H., & Hogan, N.
(2010). Coordinate dependence of variability analysis. PLoS
Computational Biology, 6, 4, e1000751.
- 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
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.
- Nasseroleslami, B., Hasson, C.J., & Sternad, D.
(2014). Rhythmic manipulation of objects with complex dynamics:
Predictability over chaos. PLoS Computational Biology.
- 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
- Hasson, C.J., Shen, T., & Sternad, D. (2012).
Energy margins in dynamic object manipulation. Journal of
Neurophysiology, 108, 5, 1349-65.
Daily 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
- 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.
- Hogan, N. & Sternad, D.
(2013). Dynamic primitives in the control of locomotion. Frontiers in
Computational Neuroscience, 7:71.
- Hogan, N., & Sternad, D. (2012). Dynamic primitives of motor behavior. Biological Cybernetics, 106 (11-12), 727-739.
- 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
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.
- 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,
- Park, S-W & Sternad, D (under review). Self-guided learning and long-term retention.
Brain Measurements and Stimulation
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
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.
- 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.
- 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.
- 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.