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Hermus J, Doeringer J, Sternad D, Hogan N. Dynamic primitives in constrained action: systematic changes in the zero-force trajectory. J Neurophysiol 2024; 131:1-15. [PMID: 37820017 DOI: 10.1152/jn.00082.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 10/02/2023] [Accepted: 10/05/2023] [Indexed: 10/13/2023] Open
Abstract
Humans substantially outperform robotic systems in tasks that require physical interaction, despite seemingly inferior muscle bandwidth and slow neural information transmission. The control strategies that enable this performance remain poorly understood. To bridge that gap, this study examined kinematically constrained motion as an intermediate step between the widely studied unconstrained motions and sparsely studied physical interactions. Subjects turned a horizontal planar crank in two directions (clockwise and counterclockwise) at three constant target speeds (fast, medium, and very slow) as instructed via visual display. With the hand constrained to move in a circle, nonzero forces against the constraint were measured. This experiment exposed two observations that could not result from mechanics alone but may be attributed to neural control composed of dynamic primitives. A plausible mathematical model of interactive dynamics (mechanical impedance) was assumed and used to "subtract" peripheral neuromechanics. This method revealed a summary of the underlying neural control in terms of motion, a zero-force trajectory. The estimated zero-force trajectories were approximately elliptical and their orientation differed significantly with turning direction; that is consistent with control using oscillations to generate an elliptical zero-force trajectory. However, for periods longer than 2-5 s, motion can no longer be perceived or executed as periodic. Instead, it decomposes into a sequence of submovements, manifesting as increased variability. These quantifiable performance limitations support the hypothesis that humans simplify this constrained-motion task by exploiting at least three primitive dynamic actions: oscillations, submovements, and mechanical impedance.NEW & NOTEWORTHY Control using primitive dynamic actions may explain why human performance is superior to robots despite seemingly inferior "wetware"; however, this also implies limitations. For a crank-turning task, this work quantified two such informative limitations. Force was exerted even though it produced no mechanical work, the underlying zero-force trajectory was roughly elliptical, and its orientation differed with turning direction, evidence of oscillatory control. At slow speeds, speed variability increased substantially, indicating intermittent control via submovements.
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Affiliation(s)
- James Hermus
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States
| | | | - Dagmar Sternad
- Departments of Biology, Electrical and Computer Engineering, and Physics, Northeastern University, Boston, Massachusetts, United States
| | - Neville Hogan
- Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States
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Nayeem R, Sohn WJ, DiCarlo JA, Gochyyev P, Lin DJ, Sternad D. Novel Platform for Quantitative Assessment of Functional Object Interactions After Stroke. IEEE Trans Neural Syst Rehabil Eng 2022; 31:426-436. [PMID: 36455078 PMCID: PMC10079607 DOI: 10.1109/tnsre.2022.3226067] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022]
Abstract
Many persons with stroke exhibit upper extremity motor impairments. These impairments often lead to dysfunction and affect performance in activities of daily living, where successful manipulation of objects is essential. Hence, understanding how upper extremity motor deficits manifest in functional interactions with objects is critical for rehabilitation. However, quantifying skill in these tasks has been a challenge. Traditional rehabilitation assessments require highly trained clinicians, are time-consuming, and yield subjective scores. This paper introduces a custom-designed device, the "MAGIC Table", that can record real-time kinematics of persons with stroke during interaction with objects, specifically a 'cup of coffee'. The task and its quantitative assessments were derived from previous basic-science studies. Six participants after stroke and six able-bodied participants moved a 3D-printed cup with a rolling ball inside, representing sloshing coffee, with 3 levels of difficulty. Movements were captured via a high-resolution camera above the table. Conventional kinematic metrics (movement time and smoothness) and novel kinematic metrics accounting for object interaction (risk and predictability) evaluated performance. Expectedly, persons with stroke moved more slowly and less smoothly than able-bodied participants, in both simple reaches and during transport of the cup-and-ball system. However, the more sensitive metric was mutual information, which captured the predictability of interactions, essential in cup transport as shown in previous theoretical research. Predictability sensitively measured differences in performance with increasing levels of difficulty. It also showed the best intraclass consistency, promising sensitive differentiation between different levels of impairment. This first study highlights the feasibility of this new device and indicates that examining dynamic object interaction may provide valuable insights into upper extremity function after stroke useful for assessment and rehabilitation.
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Affiliation(s)
- Rashida Nayeem
- Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, USA
| | - Won Joon Sohn
- Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, USA
| | - Julie A. DiCarlo
- Department of Neurology, Massachusetts General Hospital, Center for Neurotechnology and Neurorecovery, Harvard Medical School, Boston, MA, USA
| | - Perman Gochyyev
- Department of Neurology, Massachusetts General Hospital, Center for Neurotechnology and Neurorecovery, Harvard Medical School, Boston, MA, USA
| | - David J. Lin
- Department of Neurology, Massachusetts General Hospital, Center for Neurotechnology and Neurorecovery, Harvard Medical School, Boston, MA, USA
| | - Dagmar Sternad
- Department of Electrical and Computer Engineering and the Department of Biology and Physics, Northeastern University, Boston, MA, USA
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Nayeem R, Bazzi S, Sadeghi M, Hogan N, Sternad D. Preparing to move: Setting initial conditions to simplify interactions with complex objects. PLoS Comput Biol 2021; 17:e1009597. [PMID: 34919539 PMCID: PMC8683040 DOI: 10.1371/journal.pcbi.1009597] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Accepted: 10/28/2021] [Indexed: 12/15/2022] Open
Abstract
Humans dexterously interact with a variety of objects, including those with complex internal dynamics. Even in the simple action of carrying a cup of coffee, the hand not only applies a force to the cup, but also indirectly to the liquid, which elicits complex reaction forces back on the hand. Due to underactuation and nonlinearity, the object's dynamic response to an action sensitively depends on its initial state and can display unpredictable, even chaotic behavior. With the overarching hypothesis that subjects strive for predictable object-hand interactions, this study examined how subjects explored and prepared the dynamics of an object for subsequent execution of the target task. We specifically hypothesized that subjects find initial conditions that shorten the transients prior to reaching a stable and predictable steady state. Reaching a predictable steady state is desirable as it may reduce the need for online error corrections and facilitate feed forward control. Alternative hypotheses were that subjects seek to reduce effort, increase smoothness, and reduce risk of failure. Motivated by the task of 'carrying a cup of coffee', a simplified cup-and-ball model was implemented in a virtual environment. Human subjects interacted with this virtual object via a robotic manipulandum that provided force feedback. Subjects were encouraged to first explore and prepare the cup-and-ball before initiating a rhythmic movement at a specified frequency between two targets without losing the ball. Consistent with the hypotheses, subjects increased the predictability of interaction forces between hand and object and converged to a set of initial conditions followed by significantly decreased transients. The three alternative hypotheses were not supported. Surprisingly, the subjects' strategy was more effortful and less smooth, unlike the observed behavior in simple reaching movements. Inverse dynamics of the cup-and-ball system and forward simulations with an impedance controller successfully described subjects' behavior. The initial conditions chosen by the subjects in the experiment matched those that produced the most predictable interactions in simulation. These results present first support for the hypothesis that humans prepare the object to minimize transients and increase stability and, overall, the predictability of hand-object interactions.
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Affiliation(s)
- Rashida Nayeem
- Department of Electrical and Computer Engineering, Northeastern University, Boston, Massachusetts, United States of America
| | - Salah Bazzi
- Department of Electrical and Computer Engineering, Northeastern University, Boston, Massachusetts, United States of America
- Department of Biology, Northeastern University, Boston, Massachusetts, United States of America
- Institute for Experiential Robotics, Northeastern University, Boston, Massachusetts, United States of America
| | - Mohsen Sadeghi
- Department of Electrical and Computer Engineering, Northeastern University, Boston, Massachusetts, United States of America
- Department of Biology, Northeastern University, Boston, Massachusetts, United States of America
| | - Neville Hogan
- Departments of Mechanical Engineering and Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America
| | - Dagmar Sternad
- Department of Electrical and Computer Engineering, Northeastern University, Boston, Massachusetts, United States of America
- Department of Biology, Northeastern University, Boston, Massachusetts, United States of America
- Institute for Experiential Robotics, Northeastern University, Boston, Massachusetts, United States of America
- Department of Physics, Northeastern University, Boston, Massachusetts, United States of America
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Bazzi S, Sternad D. Human control of complex objects: Towards more dexterous robots. Adv Robot 2020; 34:1137-1155. [PMID: 33100448 PMCID: PMC7577404 DOI: 10.1080/01691864.2020.1777198] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2020] [Revised: 05/08/2020] [Accepted: 05/27/2020] [Indexed: 10/24/2022]
Abstract
Manipulation of objects with underactuated dynamics remains a challenge for robots. In contrast, humans excel at 'tool use' and more insight into human control strategies may inform robotic control architectures. We examined human control of objects that exhibit complex - underactuated, nonlinear, and potentially chaotic dynamics, such as transporting a cup of coffee. Simple control strategies appropriate for unconstrained movements, such as maximizing smoothness, fail as interaction forces have to be compensated or preempted. However, predictive control based on internal models appears daunting when the objects have nonlinear and unpredictable dynamics. We hypothesized that humans learn strategies that make these interactions predictable. Using a virtual environment subjects interacted with a virtual cup and rolling ball using a robotic visual and haptic interface. Two different metrics quantified predictability: stability or contraction, and mutual information between controller and object. In point-to-point displacements subjects exploited the contracting regions of the object dynamics to safely navigate perturbations. Control contraction metrics showed that subjects used a controller that exponentially stabilized trajectories. During continuous cup-and-ball displacements subjects developed predictable solutions sacrificing smoothness and energy efficiency. These results may stimulate control strategies for dexterous robotic manipulators and human-robot interaction.
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Affiliation(s)
- Salah Bazzi
- Department of Biology, Northeastern University, Boston, Massachusetts 02115, USA
- Department of Electrical and Computer Engineering, Northeastern University, Boston, Massachusetts 02115, USA
| | - Dagmar Sternad
- Department of Biology, Northeastern University, Boston, Massachusetts 02115, USA
- Department of Electrical and Computer Engineering, Northeastern University, Boston, Massachusetts 02115, USA
- Department of Physics, Northeastern University, Boston, Massachusetts 02115, USA
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