Learning new movements after paralysis: Results from a home-based study

open8siBody-machine interfaces (BMIs) decode upper-body motion for operating devices, such as computers and wheelchairs. We developed a low-cost portable BMI for survivors of cervical spinal cord injury and investigated it as a means to support personalized assistance and therapy within the home environment. Depending on the specific impairment of each participant, we modified the interface gains to restore a higher level of upper body mobility. The use of the BMI over one month led to increased range of motion and force at the shoulders in chronic survivors. Concurrently, subjects learned to reorganize their body motions as they practiced the control of a computer cursor to perform different tasks and games. The BMI allowed subjects to generate any movement of the cursor with different motions of their body. Through practice subjects demonstrated a tendency to increase the similarity between the body motions used to control the cursor in distinct tasks. Nevertheless, by the end of learning, some significant and persistent differences appeared to persist. This suggests the ability of the central nervous system to concurrently learn operating the BMI while exploiting the possibility to adapt the available mobility to the specific spatio-temporal requirements of each task.openPierella, Camilla; Abdollahi, Farnaz; Thorp, Elias; Farshchiansadegh, Ali; Pedersen, Jessica; Seanez-Gonzalez, Ismael; Mussa-Ivaldi, Ferdinando A.; Casadio, MauraPierella, Camilla; Abdollahi, Farnaz; Thorp, Elias; Farshchiansadegh, Ali; Pedersen, Jessica; Seanez-Gonzalez, Ismael; Mussa-Ivaldi, Ferdinando A.; Casadio, Maur

Upper Body-Based Power Wheelchair Control Interface for Individuals with Tetraplegia

Many power wheelchair control interfaces are not sufficient for individuals with severely limited upper limb mobility. The majority of controllers that do not rely on coordinated arm and hand movements provide users a limited vocabulary of commands and often do not take advantage of the user's residual motion. We developed a body-machine interface (BMI) that leverages the flexibility and customizability of redundant control by using high dimensional changes in shoulder kinematics to generate proportional control commands for a power wheelchair. In this study, three individuals with cervical spinal cord injuries were able to control a power wheelchair safely and accurately using only small shoulder movements. With the BMI, participants were able to achieve their desired trajectories and, after five sessions driving, were able to achieve smoothness that was similar to the smoothness with their current joystick. All participants were twice as slow using the BMI however improved with practice. Importantly, users were able to generalize training controlling a computer to driving a power wheelchair, and employed similar strategies when controlling both devices. Overall, this work suggests that the BMI can be an effective wheelchair control interface for individuals with high-level spinal cord injuries who have limited arm and hand control

Movement distributions of stroke survivors exhibit distinct patterns that evolve with training

BACKGROUND: While clinical assessments provide tools for characterizing abilities in motor-impaired individuals, concerns remain over their repeatability and reliability. Typical robot-assisted training studies focus on repetition of prescribed actions, yet such movement data provides an incomplete account of abnormal patterns of coordination. Recent studies have shown positive effects from self-directed movement, yet such a training paradigm leads to challenges in how to quantify and interpret performance. METHODS: With data from chronic stroke survivors (n = 10, practicing for 3 days), we tabulated histograms of the displacement, velocity, and acceleration for planar motion, and examined whether modeling of distributions could reveal changes in available movement patterns. We contrasted these results with scalar measures of the range of motion. We performed linear discriminant analysis (LDA) classification with selected histogram features to compare predictions versus actual subject identifiers. As a basis of comparison, we also present an age-matched control group of healthy individuals (n = 10, practicing for 1 day). RESULTS: Analysis of range of motion did not show improvement from self-directed movement training for the stroke survivors in this study. However, examination of distributions indicated that increased multivariate normal components were needed to accurately model the patterns of movement after training. Stroke survivors generally exhibited more complex distributions of motor exploration compared to the age-matched control group. Classification using linear discriminant analysis revealed that movement patterns were identifiable by individual. Individuals in the control group were more difficult to identify using classification methods, consistent with the idea that motor deficits contribute significantly to unique movement signatures. CONCLUSIONS: Distribution analysis revealed individual patterns of abnormal coordination in stroke survivors and changes in these patterns with training. These findings were not apparent from scalar metrics that simply summarized properties of motor exploration. Our results suggest new methods for characterizing motor capabilities, and could provide the basis for powerful tools for designing customized therapy

Sensory agreement guides kinetic energy optimization of arm movements during object manipulation

The laws of physics establish the energetic efficiency of our movements. In some cases, like locomotion, the mechanics of the body dominate in determining the energetically optimal course of action. In other tasks, such as manipulation, energetic costs depend critically upon the variable properties of objects in the environment. Can the brain identify and follow energy-optimal motions when these motions require moving along unfamiliar trajectories? What feedback information is required for such optimal behavior to occur? To answer these questions, we asked participants to move their dominant hand between different positions while holding a virtual mechanical system with complex dynamics (a planar double pendulum). In this task, trajectories of minimum kinetic energy were along curvilinear paths. Our findings demonstrate that participants were capable of finding the energy-optimal paths, but only when provided with veridical visual and haptic information pertaining to the object, lacking which the trajectories were executed along rectilinear paths

A body-machine interface for training selective pelvis movements in stroke survivors: A pilot study

The body-machine interfaces (BMIs) map the subjects' movements into the low dimensional control space of external devices to reach assistive and/or rehabilitative goals. This work is a first proof of concept of this kind of BMI as tool for rehabilitation after stroke. We designed an exercise to improve the control of selective movements of the pelvis in stroke survivors, increasing the ability to decouple the motion in the sagittal and frontal planes and decreasing compensatory adjustments at the shoulder girdle. A Kinect sensor recorded the movements of the subjects. Subjects played different games by controlling the vertical and horizontal motion of a cursor on a screen with respectively the lateral tilt and the ante/retroversion of their pelvis. We monitored also the degrees of freedom not directly involved in cursor control, thus subjects could complete the task only with a correct posture. Our preliminary results highlight significant improvement not only in cursor control, but also in the Trunk Impairment Scale (TIS) and in the Five Times Sit to Stand Test (5xSST)