A Hessian-based decomposition characterizes how performance in complex motor skills depends on individual strategy and variability.

In complex real-life motor skills such as unconstrained throwing, performance depends on how accurate is on average the outcome of noisy, high-dimensional, and redundant actions. What characteristics of the action distribution relate to performance and how different individuals select specific action distributions are key questions in motor control. Previous computational approaches have highlighted that variability along the directions of first order derivatives of the action-to-outcome mapping affects performance the most, that different mean actions may be associated to regions of the actions space with different sensitivity to noise, and that action covariation in addition to noise magnitude matters. However, a method to relate individual high-dimensional action distribution and performance is still missing. Here we introduce a decomposition of performance into a small set of indicators that compactly and directly characterize the key performance-related features of the distribution of high-dimensional redundant actions. Central to the method is the observation that, if performance is quantified as a mean score, the Hessian (second order derivatives) of the action-to-score function determines how the noise of the action distribution affects performance. We can then approximate the mean score as the sum of the score of the mean action and a tolerance-variability index which depends on both Hessian and action covariance. Such index can be expressed as the product of three terms capturing noise magnitude, noise sensitivity, and alignment of the most variable and most noise sensitive directions. We apply this method to the analysis of unconstrained throwing actions by non-expert participants and show that, consistently across four different throwing targets, each participant shows a specific selection of mean action score and tolerance-variability index as well as specific selection of noise magnitude and alignment indicators. Thus, participants with different strategies may display the same performance because they can trade off suboptimal mean action for better tolerance-variability and higher action variability for better alignment with more tolerant directions in action space.

A Complementary Filter Design on SE(3) to IdentifyMicro-Motions during 3D Motion Tracking.

In 3D motion capture, multiple methods have been developed in order to optimize thequality of the captured data. While certain technologies, such as inertial measurement units (IMU),are mostly suitable for 3D orientation estimation at relatively high frequencies, other technologies,such as marker-based motion capture, are more suitable for 3D position estimations at a lower frequencyrange. In this work, we introduce a complementary filter that complements 3D motion capture datawith high-frequency acceleration signals from an IMU. While the local optimization reduces the error ofthe motion tracking, the additional accelerations can help to detect micro-motions that are useful whendealing with high-frequency human motions or robotic applications. The combination of high-frequencyaccelerometers improves the accuracy of the data and helps to overcome limitations in motion capturewhen micro-motions are not traceable with 3D motion tracking system. In our experimental evaluation,we demonstrate the improvements of the motion capture results during translational, rotational,and combined movements.

Estimating Human Wrist Stiffness during a Tooling Task.

In this work, we propose a practical approach to estimate human joint stiffness during tooling tasks for the purpose of programming a robot by demonstration. More specifically, we estimate the stiffness along the wrist radial-ulnar deviation while a human operator performs flexion-extension movements during a polishing task. The joint stiffness information allows to transfer skills from expert human operators to industrial robots. A typical hand-held, abrasive tool used by humans during finishing tasks was instrumented at the handle (through which both robots and humans are attached to the tool) to assess the 3D force/torque interactions between operator and tool during finishing task, as well as the 3D kinematics of the tool itself. Building upon stochastic methods for human arm impedance estimation, the novelty of our approach is that we rely on the natural variability taking place during the multi-passes task itself to estimate (neuro-)mechanical impedance during motion. Our apparatus (hand-held, finishing tool instrumented with motion capture and multi-axis force/torque sensors) and algorithms (for filtering and impedance estimation) were first tested on an impedance-controlled industrial robot carrying out the finishing task of interest, where the impedance could be pre-programmed. We were able to accurately estimate impedance in this case. The same apparatus and algorithms were then applied to the same task performed by a human operators. The stiffness values of the human operator, at different force level, correlated positively with the muscular activity, measured during the same task.

A geometric framework for the estimation of joint stiffness of the human wrist.

Estimating joint stiffness is of paramount importance for studying human motor control and for clinical assessment of neurological diseases. Usually stiffness estimation is performed using cumbersome instrumentations (e.g. robots), and by approximating robot joint angles and torques to the human ones. This paper proposes a methodology and an experimental setup to measure wrist joint stiffness in unstructured environments, with the twofold aim of: 1) providing a geometric framework in order to derive angular displacements and torques at the wrist Flexion/Extension (FE) and Radial/Ulnar Deviation (RUD) axes of rotation, using a subject specific kinematic model; 2) suggesting an experimental setup made of two portable sensors for motion tracking and one load cell, to allow for measurements in out-of-the-lab scenarios. We tested our method on a hardware mockup of wrist kinematics, providing a ground truth for estimated angles and torques at FE and RUD joints. The experimental validation showed average absolute errors in FE and RUD angles of 0.005 rad and 0.0167 rad respectively, and an average error of FE and RUD torques of 0.006 Nm and 0.003 Nm.

An Extended Passive Motion Paradigm for Human-Like Posture and Movement Planning in Redundant Manipulators.

A major challenge in robotics and computational neuroscience is relative to the posture/movement problem in presence of kinematic redundancy. We recently addressed this issue using a principled approach which, in conjunction with nonlinear inverse optimization, allowed capturing postural strategies such as Donders' law. In this work, after presenting this general model specifying it as an extension of the Passive Motion Paradigm, we show how, once fitted to capture experimental postural strategies, the model is actually able to also predict movements. More specifically, the passive motion paradigm embeds two main intrinsic components: joint damping and joint stiffness. In previous work we showed that joint stiffness is responsible for static postures and, in this sense, its parameters are regressed to fit to experimental postural strategies. Here, we show how joint damping, in particular its anisotropy, directly affects task-space movements. Rather than using damping parameters to fit a posteriori task-space motions, we make the a priori hypothesis that damping is proportional to stiffness. This remarkably allows a postural-fitted model to also capture dynamic performance such as curvature and hysteresis of task-space trajectories during wrist pointing tasks, confirming and extending previous findings in literature.

Identification of shoulder muscle synergies in healthy subjects during an isometric task.

Muscle Synergy method has been proposed in the literature to provide a lower dimensional representation of motor commands from the central nervous system (CNS). Studies on post-stroke patients highlighted how features such as the minimum number of motor synergies accounting for most of the data variance correlate with impairments and motor function. In this study, we target healthy subjects to establish normative data in isometric tasks involving shoulder muscles. Five subjects performed an isometric, two-dimensional force-matching task in twelve planar directions with two force levels across shoulder joint. Muscle synergies and their respective activation curves were computed from nine upper limb muscles via a nonnegative matrix factorization (NNMF) algorithm. Four synergies, on an average, were able to explain 95% of the variance in EMG datasets across all subjects. The cosine similarity of the muscle synergies among the subjects on an average is found to be 0.79±0.20. Two subjects revealed the presence of subject-specific synergies which will require further investigation before examining impaired subjects.

Task-space separation principle: a force-field approach to motion planning for redundant manipulators.

In this work, we address human-like motor planning in redundant manipulators. Specifically, we want to capture postural synergies such as Donders' law, experimentally observed in humans during kinematically redundant tasks, and infer a minimal set of parameters to implement similar postural synergies in a kinematic model. For the model itself, although the focus of this paper is to solve redundancy by implementing postural strategies derived from experimental data, we also want to ensure that such postural control strategies do not interfere with other possible forms of motion control (in the task-space), i.e. solving the posture/movement problem. The redundancy problem is framed as a constrained optimization problem, traditionally solved via the method of Lagrange multipliers. The posture/movement problem can be tackled via the separation principle which, derived from experimental evidence, posits that the brain processes static torques (i.e. posture-dependent, such as gravitational torques) separately from dynamic torques (i.e. velocity-dependent). The separation principle has traditionally been applied at a joint torque level. Our main contribution is to apply the separation principle to Lagrange multipliers, which act as task-space force fields, leading to a task-space separation principle. In this way, we can separate postural control (implementing Donders' law) from various types of tasks-space movement planners. As an example, the proposed framework is applied to the (redundant) task of pointing with the human wrist. Nonlinear inverse optimization (NIO) is used to fit the model parameters and to capture motor strategies displayed by six human subjects during pointing tasks. The novelty of our NIO approach is that (i) the fitted motor strategy, rather than raw data, is used to filter and down-sample human behaviours; (ii) our framework is used to efficiently simulate model behaviour iteratively, until it converges towards the experimental human strategies.

Upper extremity proprioception in healthy aging and stroke populations, and the effects of therapist- and robot-based rehabilitation therapies on proprioceptive function.

The world's population is aging, with the number of people ages 65 or older expected to surpass 1.5 billion people, or 16% of the global total. As people age, there are notable declines in proprioception due to changes in the central and peripheral nervous systems. Moreover, the risk of stroke increases with age, with approximately two-thirds of stroke-related hospitalizations occurring in people over the age of 65. In this literature review, we first summarize behavioral studies investigating proprioceptive deficits in normally aging older adults and stroke patients, and discuss the differences in proprioceptive function between these populations. We then provide a state of the art review the literature regarding therapist- and robot-based rehabilitation of the upper extremity proprioceptive dysfunction in stroke populations and discuss avenues of future research.

H-Man: a planar, H-shape cabled differential robotic manipulandum for experiments on human motor control.

In the last decades more robotic manipulanda have been employed to investigate the effect of haptic environments on motor learning and rehabilitation. However, implementing complex haptic renderings can be challenging from technological and control perspectives. We propose a novel robot (H-Man) characterized by a mechanical design based on cabled differential transmission providing advantages over current robotic technology. The H-Man transmission translates to extremely simplified kinematics and homogenous dynamic properties, offering the possibility to generate haptic channels by passively blocking the mechanics, and eliminating stability concerns. We report results of experiments characterizing the performance of the device (haptic bandwidth, Z-width, and perceived impedance). We also present the results of a study investigating the influence of haptic channel compliance on motor learning in healthy individuals, which highlights the effects of channel compliance in enhancing proprioceptive information. The generation of haptic channels to study motor redundancy is not easy for actual robots because of the needs of powerful actuation and complex real-time control implementation. The mechanical design of H-Man affords the possibility to promptly create haptic channels by mechanical stoppers (on one of the motors) without compromising the superior backdriveability and high isotropic manipulability. This paper presents a novel robotic device for motor control studies and robotic rehabilitation. The hardware was designed with specific emphasis on the mechanics that result in a system that is easy to control, homogeneous, and is intrinsically safe for use.