Treatment planning and monitoring for motor neurorehabilitation still relies mostly on coarse clinical scales and clinician observation. Distribution analysis of action patterns recorded by sensors can provide a quantitative and easily visualizable representation of complex individualized dynamics. Here, we review the use of distributions to analyze human action in health and following neurologic injury. The aim is to demonstrate the potential of distribution analysis as a simple yet effective framework for scanning a patient prior to therapy, assisting clinicians in quantitative evaluations, and thereby uncovering underlying pathophysiological mechanisms to optimize therapy. In this review paper, we first describe how idealized distributions can model movement and capture spatiotemporal patterns. We then review metrics that can holistically quantify changes between distributions. Next, we highlight applications in different data domains that have used these approaches and precise comparison techniques to identify motor deficits and personalize therapy. Together, these methods have uncovered upper extremity flexion synergy patterns in half of stroke survivors, improved classification accuracy in data-driven models, and reduced movement errors threefold. Finally, we discuss the challenges and opportunities facing the application of distribution analysis for neurorehabilitation and precision medicine.

Effective rehabilitation of tongue movement relies on delivering personalized interventions and quantitatively assessing the intervention's impact. Current approaches primarily focus on enhancing tongue movement control and strength through oromotor exercises and audio feedback. Here, we introduce the Tongue- Trackpad a novel solution for tongue movement rehabilitation and progress tracking. The Tongue- Trackpad is a wireless intra-oral device that provides real-time visual feedback of tongue movement while quantifying the movement, thus enabling personalized interventions. In this preliminary feasibility study, a stroke survivor diag-nosed with dysarthria participated in eighteen sessions of per-sonalized visual-feedback pursuit intervention using the Tongue-Trackpad. The intervention was customized to the participant's tongue movement deficit areas identified during the initial evaluation. Despite the participant's severe speech disorder, characterized by a Diadochokinetic rate of zero, the results indicated a modest positive change in tongue movement both within a single session and over the intervention period. The results showed an average increase of 3.5$\pm 4.7{\%}$in coverage area, a reduction of 1.5$\pm 1.9{\%}$in the deficit area, and a 1.7$\pm$3.1 % reduction in the excess area within a single session. Similar modest trends were observed throughout the entire intervention period. Although further studies with more participants are needed for robust conclusions, this preliminary feasibility study suggests provided preliminary that providing personalized visual-feedback intervention using the Tongue- Trackpad holds promise for tongue movement rehabilitation.

During collaborative object manipulation, the interaction forces provide a communication channel through which humans coordinate their actions. In order for the robots to engage in physical collaboration with humans, it is necessary to understand this coordination process. Unfortunately, there is no intrinsic way to define the interaction forces. In this study, we propose a model that allows us to compute the interaction force during a dyadic cooperative object manipulation task. The model is derived directly from the existing theories on human arm movements. The results of a user study with 22 human subjects prove the validity of the proposed model. The model is then embedded in a control strategy that enables the robot to engage in a cooperative task with a human. The performance evaluation of the controller through simulation shows that the control strategy is a promising candidate for a cooperative human-robot interaction.

Error feedback is critical for supporting motor adaptation in rehabilitation, sports, piloting, and skilled manual tasks. Error augmentation interventions, in which participants' errors are amplified with either visual or haptic feedback during training has shown success over repetitive practice. Here we show that the statistical tendencies arising from free movement exploration can improve error augmentation with customized training forces that vary across the trajectory. We hypothesized that with customized error augmentation participants will adapt faster to learning a visual-motor distortion and have greater improvement than participants receiving standard error augmentation and participants repetitively practicing the task. We tested twenty-one participants using a robotic exoskeleton device restricted to two degrees of freedom. We found that participants receiving customized forces adapted faster and consequently changed with smaller forces. Further, change in error was greatest for participants receiving customized forces. These promising results support the need for customization to target subject specific errors.

Interactive technologies can help people acquire movement skills, and one way is by using visual distortions to boost neural adaptation. An extreme version of such approach is to train a movement without moving by creating a synesthetic illusion of movement - displaying virtual motions when there is none. While this approach uses no proprioceptive error to drive adaptation, our results show encouraging evidence that motor skills can be acquired through such illusions of movement.

Motor Learning is heavily governed by sensory feedback, and artificially enhancing feedback influences learning as seen in our previous works. This study provides a model-based approach in determining the optimal gain schedules for augmented feedback on a visuomotor learning task. Using Gaussian process regression, we modeled the phenomenological process of learning to operate a robot with visual rotation. We then used Pontryagin's minimum principle to achieve the optimal feedback gain schedules that yield the fastest learning, the highest post-training performance, and both at the same time. Our results reveal that the instantaneous error feedback should be doubled (×1.92) throughout the training if the fastest learning is desired. However if the highest post-training performance is desired along with the fastest learning, the feedback gain should be gradually varied from 1.92 to 1. This study explores a novel approach to optimize specific aspects of training for areas such as robotic-neuro-rehabilitation, teleoperation, sports coaching, and human-machine interactions.

Many of those who survive a stroke develop a gait disability known as stiff-knee gait (SKG). Characterized by reduced knee flexion angle during swing, people with SKG walk with poor energy efficiency and asymmetry due to the compensatory mechanisms required to clear the foot. Previous modeling studies have shown that knee flexion activity directly before the foot leaves the ground, and this should result in improved knee flexion angle during swing. The goal of this research is to physically test this hypothesis using robotic intervention. We developed a device that is capable of assisting knee flexion torque before swing but feels imperceptible (transparent) for the rest of the gait cycle. This device uses sheathed Bowden cable to control the deflection of a compliant torsional spring in a configuration known as a Series Elastic Remote Knee Actuator (SERKA). In this investigation, we describe the design and evaluation of SERKA, which includes a pilot experiment on stroke subjects. SERKA could supply a substantial torque (12 N· m) in less than 20 ms, with a maximum torque of 41 N·m. The device resisted knee flexion imperceptibly when desired, at less than 1 N·m rms torque during normal gait. With the remote location of the actuator, the user experiences a mass of only 1.2 kg on the knee. We found that the device was capable of increasing both peak knee flexion angle and velocity during gait in stroke subjects. Thus, the SERKA is a valid experimental device that selectively alters knee kinetics and kinematics in gait after stroke.

Machines might physically interact with humans more smoothly if we better understood the subtlety of human-human physical interaction. We recently reported that two people working cooperatively on a physical task will quickly negotiate an emergent strategy: typically subjects formed a temporal specialization such that one member commands the early parts of motion and the other the late parts. In our study, we replaced one of the humans with a robot programmed to perform one of the typical human specialized roles. We expected the remaining human to adopt the complementary specialized role. Subjects did believe that they were interacting with another human but did not adopt a specialized behavior as subjects would when physically working with another human; our negative result suggests a very subtle negotiation takes place in human-human physical interaction.

Haptic interaction between people and machines might benefit from an understanding of haptic communication between one person and another. We recently reported results showing that two people performing a physically shared dyadic task can outperform either person alone, even when the perception of each participant is that the other is a hindrance. Evidently a dyad quickly negotiates a more efficient motion strategy than is available to individuals. This negotiation must take place through a haptic channel of communication, and it is apparently at a level below the awareness of the participants. Here we report results on the motion strategy that emerged. By recording forces and motions we show that the dyads "specialized" temporally such that one member took on early parts of the motion and the other late parts. Tests in which one participant's contribution was surreptitiously replaced by a motor did not elicit a similar cooperative response from the remaining human participant, showing that the language of haptic communication between people must be rather subtle

Robot-mediated neurorehabilitation is a rapidly advancing field that seeks to use advances in robotics, virtual realities, and haptic interfaces, coupled with theories in neuroscience and rehabilitation to define new methods for treating neurological injuries such as stroke, spinal cord injury, and traumatic brain injury. The field is nascent and much work is needed to identify efficient hardware, software, and control system designs alongside the most effective methods for delivering treatment in home and hospital settings. This paper identifies the need for robots in neurorehabilitation and identifies important goals that will allow this field to advance

We developed a real-time controller for a 2 degree-of-freedom robotic system using xPC Target. This system was used to investigate how different methods of performance error feedback can lead to faster and more complete motor learning in individuals asked to compensate for a novel visuo-motor transformation (a 30 degree rotation). Four groups of human subjects were asked to reach with their unseen arm to visual targets surrounding a central starting location. A cursor tracking hand motion was provided during each reach. For one group of subjects, deviations from the “ideal” compensatory hand movement (i.e. trajectory errors) were amplified with a gain of 2 whereas another group was provided visual feedback with a gain of 3.1. Yet another group was provided cursor feedback wherein the cursor was rotated by an additional (constant) offset angle. We compared the rates at which the hand paths converged to the steady-state trajectories. Our results demonstrate that error-augmentation can improve the rate and extent of motor learning of visuomotor rotations in healthy subjects. Furthermore, our results suggest that both error amplification and offset-augmentation may facilitate neuro-rehabilitation strategies that restore function in brain injuries such as stroke.

Often two people must work together physically on a common task, such as lifting and positioning; a long board, or, in our model experimental system, turning a two-handled crank. Such tasks involve communication between the people, mediated by the task kinematics and dynamics: each person feels forces and motions produced by the other and derive some meaning from them. Tasks may include a degree of competition: the two people may not have exactly the same goal in mind, and must negotiate a compromise. Understanding human-human communication is important in designing robots for interaction with humans, and for robots that provide powered assistance for human-human tasks (such as physical therapy). In this paper we describe early experiments in human-human physical interaction, with a 1 dof robot included in order to give experimental access to the exchange of forces and motions between the people. We report on Fitts' law-like tasks, in which the two people cooperate to move a cursor to a common target, or to targets that do not completely overlap. Our results suggest that human-human physical communication may be a rich area of study.

Describes a new theoretical framework for robot-aided training of arm movements. This framework is based on recent studies of motor adaptation in human subjects and on general considerations about adaptive control of artificial and biological systems. The authors propose to take advantage of the adaptive processes through which subjects, when exposed to a perturbing field, develop an internal model of the field as a relation between experienced limb states and forces. The problem of teaching new movements is then reduced to the problem of designing force fields capable of inducing the desired movements as after-effects of the adaptation triggered by prolonged exposure to the fields. This approach is an alternative to more standard training methods based on the explicit specification of the desired movement to the learner. Unlike these methods, the adaptive process does not require explicit awareness of the desired movement as adaptation is uniquely concerned with restoring a preexisting kinematic pattern after a change in dynamical environment.