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Groups ■ Motor Learning and Rehab

Motor Learning and Rehabilitation

The activity of the Motor Learning and Rehabilitation Lab is two folds:

  • Developing cutting edge mechatronic and robotic technology to enhance/augment human robot interaction with a special focus on robot aided rehabilitation.
  • Study of the neural plasticity that underlies the organization of the human sensorimotor system and its capacity to learn motor skills in the context of a complex environment

We see adaptive behaviour as the emergent property of the bi-directional interactions of the nervous system with the body and the environment. This implies a continuous exchange of signals/energy between the nervous system, the body and the surrounding environment. Within this background the Lab design and develop custom robotic interface to investigate paradigms of motor learning and control, employing visuo-haptic, dynamic virtual environments and measuring the motor response (in terms of kinematics and kinetics) and the corresponding neural correlates (in terms of muscle activity -EMG- and brain activity)

Research topics:

Robotic Rehabilitation Haptic Perception Sensorimotor Adaptation
alt one of the main purpose of the lab is to design robotic interface and particular assistive control to enhance and to facilitate motor recovery after brain injury. alt we use robotic interfaces to study perception thresholds in humans when they interact with simulated virtual objects. alt studying how humans react and learn from a robot generated structured dynamic environment.
Ergonomics Carapace Fast estimation of muscular stiffness
alt investigating how haptic rendering provided to the user by force reflecting devices can influence strategies in human computer interaction. alt Compliant Advanced Robotic Actuation Powering Composite Exoskeleton: the design of novel technology able to redefine the standard in robotic rehabilitation and augmenting devices based on multistable compliant composite material. alt we aim to design novel mechatronic devices and protocol to online estimation of muscular stiffness.

EXTERNAL PROJECTS:

VIACTORS
Variable Impedance ACTuatORS
European Commission, STREP, Project ICT-231554, Cognitive Systems and Robotics
HUMOUR
HUman behavioral Modeling for enhancing learning by Optimizing hUman-Robot interaction
European Commission, STREP Project ICT-231724, Cognitive Systems and Robotics
IIT-INAIL
Joint facility Italiano di Tecnologia and INAIL (National workers compensation Authority)
IIT-GASLINI
Joint facility Istituto Italiano di Tecnologia and Pediatric Hospital Gaslini plan to start up a multidisciplinary approach to Pediatric Orthopedics and Neural Rehabilitation using the technology developed by my team under license of IIT

SELECTED PUBLICATIONS:


  • Masia L., Krebs H. I., Cappa P. and Hogan N. (2007)
    Design and Characterization of a Hand-module for whole Arm Rehabilitation following stroke
    IEEE-ASME Transactions on Mechatronics, vol. 12, (no. 4), Pisa, Italy, 1083-4435
  • Masia L., Casadio M., Sandini G. and Morasso P. (2009)
    Eye-hand coordination during dynamic visuomotor rotations
    PLoS ONE, vol. 4, (no. 9), pp. E7004, 1932-6203
  • Masia L., Casadio M., Giannoni P., Sandini G. and Morasso P. (2009)
    Performance adaptive training control strategy for recovering wrist movements in stroke patients: a preliminary, feasibility study.
    Journal of NeuroEngineering and Rehabilitation, vol. 6, (no. 44), pp. 1-11
  • Squeri V., Casadio M., Vergaro E., Giannoni P., Morasso P. and Sanguineti V. (2009)
    Bilateral robot therapy based on haptics and reinforcement learning: Feasibility study of a new concept for treatment of patient
    Journal of Rehabilitation Medicine, vol. 41, (no. 12), pp. 961-5
  • Sciutti A., Squeri V., Gori M., Masia L., Sandini G. and Konczak J. (2010)
    Predicted sensory feedback derived from motor commands does not improve haptic sensitivity
    Experimental Brain Research, vol. 200, (no. 3), pp. 259 - 267
  • Casadio M., Sanguineti V., Squeri V., Masia L. and Morasso P. (2010)
    Inter-limb interference during bimanual adaptation to dynamic environments
    Experimental Brain Research, vol. 202, (no. 3), pp. 693-707
  • Squeri V., Masia L., Casadio M., Vergaro E. and Morasso P.
    Force/Movement Control in a hybrid task: pushing & visuo-manual tracking
    PLoS ONE. 2010; 5(6): e11189
  • Melendez Calderon A., *Masia L., Gassert R., *Sandini G. and Burdet E. (2011)
    Force Field Adaptation Can Be Learned Using Vision in the Absence of Proprioceptive Error
    IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 19, (no. 3), pp. 298-306
  • *Masia L., Frascarelli F., *Morasso P., Di Rosa G., Petrarca M., Castelli E. and Cappa P. (2011)
    Reduced short term adaptation to robot generated dynamic environment in children affected by Cerebral Palsy
    Journal of NeuroEngineering and Rehabilitation, vol. 8, (no. 28)
  • *Squeri V., *Sciutti A., *Gori M., *Masia L., *Sandini G. and Konczak J. (2012)
    Two hands, one perception: how bimanual haptic information is combined by the brain
    Journal of Neurophysiology, vol. 107, pp. 544-550, 0022-3077
  • *Fiorilla A. E., *Nori F., *Masia L. and *Sandini G. (2011)
    Finger Impedance Evaluation by means of Hand Exoskeleton
    Annals of Biomedical Engineering, vol. 39, (no. 12), pp. 2945-2954
  • *Masia L., *Squeri V., Burdet E., *Sandini G. and *Morasso P. (2012)
    Wrist Coordination in a Kinematically Redundant Stabilization Task
    IEEE Transactions on Haptics, vol. 5, (no. 3), pp. 231-239, 1939-1412
  • Konczak J., *Sciutti A., Avanzino L., *Squeri V., *Gori M., *Masia L., Abbruzzese G. and *Sandini G. (2012)
    Parkinson's disease accelerates age-related decline in haptic perception by altering somatosensory integration
    Brain, vol. 135, pp. 3371-3379, 1460-2156
  • Gori M, Squeri V, Sciutti A, Masia L, Sandini G and Konczac J.
    Motor commands in children interfere with their haptic perception of objects
    Experimental Brain Research, 2012 Nov;223(1):149-57
alt Robotic Rehabilitation
People involved:

Lorenzo Masia, Valentina Squeri, Leonardo Cappello, Pietro Morasso

External collaborations

Joint Facilitiy Pediatric Hospital Gaslini

When making movements in a dynamically changing environments, healthy subjects make adaptive compensatory adjustments partially counteracting the environmental changes [1]. The comparison of force field adaptation in neurological patients and healthy subjects shows that compensatory strategy utilized is similar [2-4]. These imply that the injured motor system can reorganize in the course of motor practice. During the last few years, a considerable effort has been devoted to the application of robots as aids to the treatment of persons with motor disabilities [5-7]. The use of robotic devices in rehabilitation can provide high intensity, repetitive, task-specific, and interactive treatment of the impaired upper limb and an objective, reliable means of monitoring patients progress. Robot rehabilitation, through the application of resistive, assistive and other forces [8], is ideal to promote exploration of movement strategies because the difficulty level of robotic therapy can be titrated to the patients' impairment level to promote unlearning of compensatory habits and reduction in impairment [9]. However the optimal training technique is still unclear.

The Motor Learning and Rehabilitation Lab develops new robotic rehabilitation treatments that aim to promote functional and motor recovery in children and adults affected by neurological disorders (i.e. celebral palsy, oncological trauma, ataxia, stroke, multiple sclerosis).

Projects:

Robotic Rehabilitation

A, B: Squeri V., Zenzeri J., et al. (2011). Integrating proprioceptive assessment with proprioceptive training of stroke patients. Rehabilitation Robotics (ICORR), IEEE International Conference on.

C, D: Squeri V., Masia L., et al. (2011). Improving the ROM of Wrist Movements in Stroke Patients by means of a Haptic Wrist Robot. 33rd Annual International IEEE EMBS Conference. Aug 30- Sep 3, 2011, Boston, MA, USA.

[3] Shadmehr, R. and F.A. Mussa-Ivaldi
Adaptive representation of dynamics during learning of a motor task
J Neurosci, 1994. 14(5 Pt 2): p. 3208-24

Casadio, M., et al.
Abnormal sensorimotor control, but intact force field adaptation, in multiple sclerosis subjects with no clinical disability
Mult Scler, 2008. 14(3): p. 330-42

[3] Smith, M.A. and R. Shadmehr
Intact ability to learn internal models of arm dynamics in Huntington's disease but not cerebellar degeneration
J Neurophysiol, 2005. 93: p. 2809 - 2821

[4] Maschke, M., et al.
Hereditary cerebellar ataxia progressively impairs force adaptation during goal-directed arm movements
J Neurophysiol, 2004. 91: p. 230-8

Kwakkel, G., B.J. Kollen, and H.I. Krebs
Effects of robot-assisted therapy on upper limb recovery after stroke: a systematic review
Neurorehabil Neural Repair, 2008. 22(2): p. 111-21

[6] Prange, G.B., et al.
Systematic review of the effect of robot-aided therapy on recovery of the hemiparetic arm after stroke
J Rehabil Res Dev, 2006. 43(2): p. 171-84

[7] Huang, V.S. and J.W. Krakauer
Robotic neurorehabilitation: a computational motor learning perspective
J Neuroeng Rehabil, 2009. 6: p. 5

Marchal-Crespo, L. and D.J. Reinkensmeyer
Review of control strategies for robotic movement training after neurologic injury
J Neuroeng Rehabil, 2009. 6: p. 20

Sanguineti V., Casadio M., Vergaro E., Squeri V., Giannoni P. and Morasso P. (2009)
Robot therapy for stroke survivors: proprioceptive training and regulation of assistance
Advanced Technologies in Rehabilitation - Empowering Cognitive, Physical, Social and Communicative Skills through Virtual Realit, pp. 126 - 142, 978-1-60750-018-6

Squeri V., Casadio M., Vergaro E., Giannoni P., Morasso P. and Sanguineti V. (2009)
Bilateral robot therapy based on haptics and reinforcement learning: Feasibility study of a new concept for treatment of patient
Journal of Rehabilitation Medicine, vol. 41, (no. 12), pp. 961-5

*Squeri V., *Zenzeri J., *Morasso P. and Basteris A. (2011)
Integrating proprioceptive assessment with proprioceptive training of stroke patients
International Conference on Rehabilitation Robotics, ICORR 2011, pp. 1-6, Zurich Switzerland

Vergaro E., Casadio M., Squeri V., Giannoni P., Morasso P. and Sanguineti V. (2010)
Self-adaptive robot training of stroke survivors for continuous tracking movements.
Journal of NeuroEngineering and Rehabilitation, vol. 7:13

Vergaro E., Squeri V., Brichetto G., Casadio M., Morasso P., Solaro C. and Sanguineti V. (2010)
Adaptive robot training for the treatment of incoordination in multiple sclerosis.
Journal of NeuroEngineering and Rehabilitation, vol. 7:37

*Squeri V., *Masia L., *Taverna L. and *Morasso P. (2011)
Improving the ROM of Wrist Movements in Stroke Patients by means of a Haptic Wrist Robot
33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC '11), pp. 1268-1271, Boston, Massachusetts USA, 30.08.2011-03.09.2011

Masia L., Casadio M., Giannoni P., Sandini G. and Morasso P. (2009)
Performance adaptive training control strategy for recovering wrist movements in stroke patients: a preliminary, feasibility study.
Journal of NeuroEngineering and Rehabilitation, vol. 6, (no. 44), pp. 1-11

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alt Haptic Perception
People involved:

Valentina Squeri, Lorenzo Masia, Alessandra Sciutti, Monica Gori, Juergen Konczak

The term haptic perception refers to an individual's sensitivity to its adjustment surroundings by the use of its body [1]. Haptic perception is fundamental for the ability of humans to manipulate objects in their environment. It is based on the integration of afferent proprioceptive and tactile signals. Moving one's hand along a curved path provides a method for investigating the acuity of the haptic sense. Recent studies examining haptic perception have used a two-joint robot manipulandum to create virtual curved objects which healthy subjects and Parkinson Disease patients [2-4] explored by moving a handle attached to the end of the robotic arm. The use of a haptic interface permits to reproduce mechanical signals that are normally experienced when haptically exploring real, everyday environments.

Projects:

  • Differences between passive and active exploration in healthy subjects [5]
  • Bimanual haptic exploration in healthy subjects [6]
  • Development of haptic exploration in children [7]
  • Haptic perception in PD patients
  • Bimanual exploration in left handers
Haptic Perception

Left panel: bimanual robotic manipulandum

Right panel: different experimental conditions. Squeri, V., et al., Two hands, one perception: how bimanual haptic information is combined by the brain. J Neurophysiol, 2012. 107(2): p. 544-50

Gibson, J.J.
The Senses Considered as Perceptual Systems
1966, Boston: Houghton Mifflin

Fasse, E.D., et al.
Haptic interaction with virtual objects
Biol Cybern, 2000. 82: p. 69-83

Henriques, D.Y., M. Flanders, and J.F. Soechting
Haptic synthesis of shapes and sequences
J Neurophysiol, 2004. 91(4): p. 1808-21

Konczak, J., et al.
Haptic perception of object curvature in Parkinson's disease
PLoS One, 2008. 3: p. e2625

Sciutti A., Squeri V., Gori M., Masia L., Sandini G. and Konczak J. (2010)
Predicted sensory feedback derived from motor commands does not improve haptic sensitivity
Experimental Brain Research, vol. 200, (no. 3), pp. 259 - 267

*Squeri V., *Sciutti A., *Gori M., *Masia L., *Sandini G. and Konczak J. (2012)
Two hands, one perception: how bimanual haptic information is combined by the brain
Journal of Neurophysiology, vol. 107, pp. 544-550, 0022-3077

*Gori M., *Squeri V., *Sciutti A., *Masia L., *Sandini G. and Konczak J. (2012)
Motor commands in children interfere with their haptic perception of objects
Experimental Brain Research, vol. 223, (no. 1), pp. 149-157

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alt Sensorimotor Adaptation
People involved:

Valentina Squeri, Lorenzo Masia, Pietro Morasso, Maura Casadio, Vittorio Sanguineti

Mechanical perturbations delivered to subjects by a robotic manipulandum resulted in an initial decrease in performance, but with continued practice, participants begin to make feed-forward and feedback adjustments to the motor commands in order to return performance levels to those achieved prior to perturbation exposure [1-4]. Under the altered dynamic conditions, motor commands are insufficient to compensate for the applied force, and this leads to distortions in the trajectories and large end-point errors. During repeated trials, the inverse dynamics model changes to the inverse of the combined arm dynamics and the applied force field. Then, normal trajectories reappear and the end-point errors are reduced. This adaptation is assumed to involve plastic changes of the synaptic efficacy of neurons constituting the inverse dynamics model.
In contrast, real life tasks that require skilled control of tools in a variable, partially unknown environment are likely to require the ability to switch from one strategy to another. Moreover, in the course of an action subjects will likely accept suboptimal criteria that are sufficient to satisfy the task requirements. In this sense, the existence of multiple optimal and the ability of the subjects to access them is a key element of skilled behavior.

The Motor Learning and Rehabilitation Lab studies the healthy subjects behavior in different dynamic conditions.

Projects:

  • Bimanual adaptation to dynamic environment [5]
  • Sensorimotor adaptation during continuous tracking motion [6]
  • Bimanual stabilization of a mass [7]
Sensorimotor Adaptation

Conditt, M.A., F. Gandolfo, and F.A. Mussa-Ivaldi
The motor system does not learn the dynamics of the arm by rote memorization of past experience
J Neurophysiol, 1997. 78: p. 554-560

Gandolfo, F., F.A. Mussa-Ivaldi, and E. Bizzi
Motor learning by field approximation
Proc Natl Acad Sci U S A, 1996. 93(9): p. 3843-6

[3] Shadmehr, R. and F.A. Mussa-Ivaldi
Adaptive representation of dynamics during learning of a motor task
J Neurosci, 1994. 14(5 Pt 2): p. 3208-24

Shadmehr, R. and S.P. Wise
Computational Neurobiology of Reaching and Pointing. A Foundation for Motor Learning
ed. ma. 2005, Cambridge: MIT Press

Casadio M., Sanguineti V., Squeri V., Masia L. and Morasso P. (2010)
Inter-limb interference during bimanual adaptation to dynamic environments
Experimental Brain Research, vol. 202, (no. 3), pp. 693-707

Squeri V., Masia L., Casadio M., Morasso P. and Vergaro E. (2010)
Force-Field Compensation in a Manual Tracking Task
PLoS ONE, vol. 5, (no. 6), pp. e11189, 1932-6203

Saha D. and *Morasso P. (2012)
Stabilization strategies for unstable dynamics
PLoS ONE, vol. 7, (no. 1), pp. e30301, 1932-6203

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alt Ergonomics
People involved:

Lorenzo Masia, Valentina Squeri, Pietro Morasso, Etienne Burdet

We investigate how haptic rendering provided to the user by force reflecting devices can influence strategies of interaction. The problem of coordinating movements in dynamic environments may be particularly relevant to tasks requiring haptic feedback and stability during the interaction, such as in surgery. The primary target of the investigation is to understand if different haptic responses can lead a subject to modify the movement patterns. The main question will then be focused on creating artificial visuo-haptic miscalibrations by means of a haptic device and a virtual reality environment in order to train humans to remap hand movements in a modified geometrical and dynamical environment, guiding them in choosing specified joint coordination when performing actions. Outcomes will serve to design and develop novel mechatronic devices for the implementation of advanced Human Computer Interfaces which will be able not only to be input channels for the interface but also to provide augmented feedback information to the user about the environment in which the manipulation is performed, providing guidance and error correction in case of failure.

  • Masia L., Casadio M., Sandini G. and Morasso P. 2009, "Eye-hand coordination during dynamic visuomotor rotations", PLoS ONE, vol. 4,no. 9, E7004
  • Wrist coordination in a kinematically redundant task
  • Squeri, V., et al., Force-field compensation in a manual tracking task. PLoS One, 2010. 5(6): p. e11189
  • Masia L. , Squeri V., Sandini G. and Morasso P. "Wrist coordination in a kinematically redundant stabilization task". IEEE Transaction on Haptics, pp. 231-239, Third Quarter, 2012
Ergonomics The Motor Learning and Rehabilitation Lab studies the healthy subjects behavior in different dynamic conditions by simulating various objects and allow humans to dynamically interact with them by custom designed haptic devices.

[1] Harris, C. M., and Wolpert, D. M. (1998)
Signal-dependent noise determines motor planning
Nature 394, 780-784

[2] Dingwell, J.B., Mah,C.D., Mussa-Ivaldi, F.A.(2004)
Experimentally confirmed mathematical model for human control of a non-rigid object
J. Neurophysiol. 91,1158-1170

[3] Franklin D.W., Burdet E., Osu R., Kawato M., and Milner T.E.
Functional significance of stiffness in adaptation of multijoint arm movements to stable and unstable environments
Experimental Brain Research. 151, 145-157 (2003)

[4] Guigon, E., Baraduc, P., & Desmurget, M. (2007)
Computational motor control: Redundancy and invariance
Journal of Neurophysiology, 97(1), 331-347

Saha D. and *Morasso P. (2012)
Stabilization strategies for unstable dynamics
PLoS ONE, vol. 7, (no. 1), pp. e30301, 1932-6203

[6] Loram I.D., Gollee H., Lakie M. and Gawthrop P.J.
Human control of an inverted pendulum: Is continuous control necessary? Is intermittent control effective? Is intermittent control physiological?
2011, J Physiol, 589, 307-324

[7] Chew J.Z., Gandevia S.C., Fitzpatrick R.C.
Postural control at the human wrist
J Physiol. 2008 Mar 1; 586(5):1265-75. Epub 2008 Jan 10

[8] Loram I.D., Lakie M.V. and Gawthrop P.J.
Visual control of stable and unstable loads: what is the feedback delay and extent of linear time-invariant control?
2009 The Journal of Physiology, 587, 1343-1365

[9] Fitzpatrick R.C., Taylor J.L., McCloskey D.I.
Ankle stiffness of standing humans in response to imperceptible perturbation: reflex and task-dependent components
J Physiol. 1992 Aug; 454:533-47

[10] Loram I.D., Gawthrop P.J., Lakie M.
The frequency of human, manual adjustments in balancing an inverted pendulum is constrained by intrinsic physiological factors
J Physiol. 2006 Nov 15;577(Pt 1):417-32. Epub 2006 Sep 14

[11] Gielen C.C., Houk J.C., Marcus S.L., Miller L.E.
Viscoelastic properties of the wrist motor servo in man
Ann Biomed Eng. 1984;12(6):599-620

[12] Lametti D.R., Ostry D.J
Postural constraints on movement variability
J Neurophysiol. 2010 Aug;104(2):1061-7. Epub 2010 Jun 16

[13] Slutsky D.J. and Herman M
Rehabilitation of distal radius fractures: a biomechanical guide
Hand Clinics, 21, 2005, 455-468

[14] Huang F.C., Mussa-Ivaldi F.A., Pugh, C.M., Patton J.L. (2011)
Learning Kinematic Constraints in Laparoscopic Surgery
IEEE Transactions on Haptics, In Press

[15] Wentink M., Breedveld P., Stassen L.P.S., Oei I.H., Wieringa P.A. (2002)
A clearly visible endoscopic instrument shaft on the monitor facilitates hand-eye coordination
Surg Endosc 16(11): 1533-7

[16] DeJong B.P., Colgate J.E., Peshkin M.A. (2004)
Improving Teleoperation: Reducing Mental Rotations and Translations
Proc. of IEEE International Conference on Robotics and Automation New Orleans, Louisiana

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alt Carapace
People involved:

Lorenzo Masia, Valentina Squeri, Pietro Morasso, Prof. Paul Weaver, Dr. Alberto Pirrera, Xavier Lachenal

This project aims to propose an unconventional and bio-inspired approach to robotic assistive/augmenting design, able to provide a superior wearability, to deliver assistance as needed or to enhance physical performance, continuously monitoring subject's interaction due to its extraordinary ergonomics. The principal aim is the development of a new generation of adaptive/multifunctional structures working in nonlinear regimes taking inspiration from nature. The purpose is to study compliant exoskeleton architectures in order to overcome the joint misalignment problem, allowing a gentle interaction between humans and robotic devices by lightweight and human friendly solutions. The main idea is studying flexible structures and developing Shape-Morphing Compliant Mechanisms with embedded actuators (Kota et al., 1999; Ananthasuresh et al., 1995; Saggere et al., 1999) which can be integrated in the exoskeleton at the level of joint or even providing motion and torque by a tendon driven transmission or Novel Multistable Composite Structures (Lachenal et al., 2011-2012). A distributed compliant architecture will allow to overcome the problem of joint misalignment providing a more comfortable lightweight and human friendly structure. The benefits of this approach are twofold:

  • It would allow removing unnecessary stiffness thus realizing substantial weight reduction.
  • It would allow structures to deform in a well-behaved manner by incorporating a functional kinematics that so far have been distinctive of mechanical devices.
Carapace The Motor Learning and Rehabilitation Lab studies how to design novel actuators to mimic dynamics of human interaction.

[1] Ananthasuresh G.K, S. Kota
Designing Compliant Mechanisms
ASME Mechanical Engineering, November 1995

[2] Kota S, Hetrick J, Li Z, Saggere L.
Tailoring Unconventional Actuators with Compliant Transmissions: Design Methods and Applications
IEEE/ASME Transactions on Mechatronics, Vol. 4, No. 4 December 1999 pp396-408

[3] Lachenal, X, Daynes, S & Weaver, PM.
Concept For a Bistable Composite Twisting Structure
ASME Conference on Smart Materials, Adaptive Structures and Intelligent Systems, Scottsdale, AZ, (pp. 573-581), 2011. ISBN: 9780791854716

[4] Lachenal, X, Weaver, PM & Daynes, S.
Multistable composite twisting structure for morphing applications
Proceedings of the Royal Society A, 468(2141), (pp. 1230-1251), 2012

[5] Saggere, L., Kota S
Static Shape Control of Smart Structures Using Compliant Mechanisms
AIAA Journal, Volume 37, Number 5, pp.572-578, May 1999

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alt Fast estimation of muscular stiffness
People involved:

Lorenzo Masia, Valentina Squeri, Pietro Morasso

Measuring the endpoint stiffness of the human arm in different tasks, including human-robot or human-human interaction, is crucial for understanding the neural control of movement and the corresponding learning process during skill acquisition (normal subjects) or functional recovery (neurological patients). However, it is a complex and time consuming procedure. Most systems described in the literature use planar robotic manipulanda and the estimates obtained in this way are still considered robust and accurate, although with rather strong limitations in terms of speed and bandwidth. For this reason a novel mechatronic device was designed that carries out endpoint estimation directly, in a single trial within a fraction of a second. The device can be operated in a stand-alone configuration or can be mounted on a robotic manipulandum, allowing to perform the measurement during the execution of a dynamic task. The device consists of a rotary mechanism which applies cyclic radial perturbations to the human arm with a preset displacement profile and measures the corresponding restoring force by means of a 6-axes load cell. It was tested in different experimental contexts and the outcomes suggest that the system is not only reliable in standalone mode but allows obtaining a reliable bi-dimensional estimation of arm stiffness when plugged in a planar manipulandum, dramatically reducing the amount of time for measurement and allowing to decouple the two controllers of the planar manipulator on which is mounted and the device itself.

Fast estimation of muscular stiffness The Motor Learning and Rehabilitation Lab studies how to estimate muscular stiffness by introducing a novel mechatronic sensor able to provide an extremely fast measurement bandwidth.

Acosta AM, Kirsch RF, Perreault EJ
A robotic manipulator for the characterization of two-dimensional dynamic stiffness using stochastic displacement perturbations
J Neurosci Methods 2000;102:177-86

Mussa-Ivaldi, F.A., Hogan, N., Bizzi, E., 1985
Neural, mechanical, and geometric factors subserving arm posture in humans
Journal of Neuroscience 5, 2732-2743

Norton R.L.
Cam Design and Manufacturing Handbook
Industrial Press Copyright 2009, 592 pp.ISBN 0-8311-3367-2

Perreault EJ, Kirsch RF, Acosta AM (1999)
Multiple-input, multiple-output system identification for the characterization of limb stiffness dynamics
Biological Cybernetics 80: 327-337

Bennett, D.J., Hollerbach, J.M., Xu, Y., Hunter, I.W., 1992
Timevarying stiffness of human elbow joint during cyclic voluntary movement
Experimental Brain Research 88, 433-442

Burdet E, Osu R, Franklin DW, Milner TE and Kawato M (2000)
A Method for Measuring Hand Stiffness during Multi-joint Arm Movements
Journal of Biomechanics 33: 1705-09

Burdet, E., Osu, R., 1999
Development of a new method for identifying muscle stiffness during human arm movements
Report 1-21, Kawato Dynamic Brain Project, ERATO, JST, Japan

*Masia L., *Sandini G. and *Morasso P. (2011)
A novel mechatronic system for measuring end-point stiffness: Mechanical design and preliminary tests
International Conference on Rehabilitation Robotics, ICORR 2011, Zurich Switzerland

*Masia L., *Squeri V., *Sandini G. and *Morasso P. (2012)
Measuring end-point stiffness by means of a modular mechatronic system
IEEE International Conference on Robotics and Automation ICRA 2012, pp. 2471-2478, St. Paul Minnesota USA

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Last Updated on Friday, 27 September 2013 16:14