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Robot Training With Vector Fields Based on Stroke Survivors' Individual Movement Statistics.

ABSTRACT: The wide variation in upper extremity motor impairments among stroke survivors necessitates more intelligent methods of customized therapy. However, current strategies for characterizing individual motor impairments are limited by the use of traditional clinical assessments (e.g., Fugl-Meyer) and simple engineering metrics (e.g., goal-directed performance). Our overall approach is to statistically identify the range of volitional movement capabilities, and then apply a robot-applied force vector field intervention that encourages under-expressed movements. We investigated whether explorative training with such customized force fields would improve stroke survivors' (n = 11) movement patterns in comparison to a control group that trained without forces (n = 11). Force and control groups increased Fugl-Meyer UE scores (average of 1.0 and 1.1, respectively), which is not considered clinically meaningful. Interestingly, participants from both groups demonstrated dramatic increases in their range of velocity during exploration following only six days of training (average increase of 166.4% and 153.7% for the Force and Control group, respectively). While both groups showed evidence of improvement, we also found evidence that customized forces affected learning in a systematic way. When customized forces were active, we observed broader distributions of velocity that were not present in the controls. Second, we found that these changes led to specific changes in unassisted motion. In addition, while the shape of movement distributions changed significantly for both groups, detailed analysis of the velocity distributions revealed that customized forces promoted a greater proportion of favorable changes. Taken together, these results provide encouraging evidence that patient-specific force fields based on individuals' movement statistics can be used to create new movement patterns and shape them in a customized manner. To the best of our knowledge, this paper is the first to directly link engineering assessments of stroke survivors' exploration movement behaviors to the design of customized robot therapy.

SUBMITTER: Wright ZA

PROVIDER: S-EPMC5901661 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature

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Robot Training With Vector Fields Based on Stroke Survivors' Individual Movement Statistics.

Wright Zachary A ZA Lazzaro Emily E Thielbar Kelly O KO Patton James L JL Huang Felix C FC

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society 20171016 2

The wide variation in upper extremity motor impairments among stroke survivors necessitates more intelligent methods of customized therapy. However, current strategies for characterizing individual motor impairments are limited by the use of traditional clinical assessments (e.g., Fugl-Meyer) and simple engineering metrics (e.g., goal-directed performance). Our overall approach is to statistically identify the range of volitional movement capabilities, and then apply a robot-applied force vector ...[more]

PMID: 29035220

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Project description:BackgroundProprioceptive deficits after stroke are associated with poor upper limb function, slower motor recovery, and decreased self-care ability. Improving proprioception should enhance motor control in stroke survivors, but current evidence is inconclusive. Thus, this study examined whether a robot-aided somatosensory-based training requiring increasingly accurate active wrist movements improves proprioceptive acuity as well as motor performance in chronic stroke.MethodsTwelve adults with chronic stroke completed a 2-day training (age range: 42-74 years; median time-after-stroke: 12 months; median Fugl-Meyer UE: 65). Retention was assessed at Day 5. Grasping the handle of a wrist-robotic exoskeleton, participants trained to roll a virtual ball to a target through continuous wrist adduction/abduction movements. During training vision was occluded, but participants received real-time, vibro-tactile feedback on their forearm about ball position and speed. Primary outcome was the just-noticeable-difference (JND) wrist position sense threshold as a measure of proprioceptive acuity. Secondary outcomes were spatial error in an untrained wrist tracing task and somatosensory-evoked potentials (SEP) as a neural correlate of proprioceptive function. Ten neurologically-intact adults were recruited to serve as non-stroke controls for matched age, gender and hand dominance (age range: 44 to 79 years; 6 women, 4 men).ResultsParticipants significantly reduced JND thresholds at posttest and retention (Stroke group: pretest: mean: 1.77° [SD: 0.54°] to posttest mean: 1.38° [0.34°]; Control group: 1.50° [0.46°] to posttest mean: 1.45° [SD: 0.54°]; F[2,37] = 4.54, p = 0.017, ηp2 = 0.20) in both groups. A higher pretest JND threshold was associated with a higher threshold reduction at posttest and retention (r = - 0.86, - 0.90, p ≤ 0.001) among the stroke participants. Error in the untrained tracing task was reduced by 22 % at posttest, yielding an effect size of w = 0.13. Stroke participants exhibited significantly reduced P27-N30 peak-to-peak SEP amplitude at pretest (U = 11, p = 0.03) compared to the non-stroke group. SEP measures did not change systematically with training.ConclusionsThis study provides proof-of-concept that non-visual, proprioceptive training can induce fast, measurable improvements in proprioceptive function in chronic stroke survivors. There is encouraging but inconclusive evidence that such somatosensory learning transfers to untrained motor tasks. Trial registration Clinicaltrials.gov; Registration ID: NCT02565407; Date of registration: 01/10/2015; URL: https://clinicaltrials.gov/ct2/show/NCT02565407 .

Project description:Proprioception has a crucial role in promoting or hindering motor learning. In particular, an intact position sense strongly correlates with the chances of recovery after stroke. A great majority of neurological patients present both motor dysfunctions and impairments in kinesthesia, but traditional robot and virtual reality training techniques focus either in recovering motor functions or in assessing proprioceptive deficits. An open challenge is to implement effective and reliable tests and training protocols for proprioception that go beyond the mere position sense evaluation and exploit the intrinsic bidirectionality of the kinesthetic sense, which refers to both sense of position and sense of movement. Modulated haptic interaction has a leading role in promoting sensorimotor integration, and it is a natural way to enhance volitional effort. Therefore, we designed a preliminary clinical study to test a new proprioception-based motor training technique for augmenting kinesthetic awareness via haptic feedback. The feedback was provided by a robotic manipulandum and the test involved seven chronic hemiparetic subjects over 3?weeks. The protocol included evaluation sessions that consisted of a psychometric estimate of the subject's kinesthetic sensation, and training sessions, in which the subject executed planar reaching movements in the absence of vision and under a minimally assistive haptic guidance made by sequences of graded force pulses. The bidirectional haptic interaction between the subject and the robot was optimally adapted to each participant in order to achieve a uniform task difficulty over the workspace. All the subjects consistently improved in the perceptual scores as a consequence of training. Moreover, they could minimize the level of haptic guidance in time. Results suggest that the proposed method is effective in enhancing kinesthetic acuity, but the level of impairment may affect the ability of subjects to retain their improvement in time.

Project description:BackgroundFully adjustable articulators and pantographs record and reproduce individual mandibular movements. Although these instruments are accurate, they are operator-dependant and time-consuming. Pantographic recording is affected by inter and intra operator variability in the individuation of clinical reference points and afterwards in reading pantographic recording themselves. Finally only border movements can be reproduced.MethodsBionic Jaw Motion system is based on two components: a jaw movement analyzer and a robotic device that accurately reproduces recorded movements. The jaw movement analyzer uses an optoelectronic motion system technology made of a high frequency filming camera that acquires 140frames per second and a custom designed software that recognizes and determines the relative distance at each point in time of markers with known geometries connected to each jaw. Circumferential modified retainers connect markers and do not cover any occlusal surfaces neither obstruct occlusion. The recording process takes 5 to 10 s. Mandibular movement performance requires six degrees of freedom of movement, 3 rotations and 3 translations. Other robots are based on the so-called delta mechanics that use several parallel effectors to perform desired movements in order to decompose a complex trajectory into multiple more simple linear movements. However, each parallel effector introduces mechanical inter-component tolerances and mathematical transformations that are required to transform a recorded movement into the combination of movements to be performed by each effector. Bionic Jaw Motion Robot works differently, owing to three motors that perform translational movements and three other motors that perform rotations as a gyroscope. This configuration requires less mechanical components thus reducing mechanical tolerances and production costs. Both the jaw movement analyzer and the robot quantify the movement of the mandible as a rigid body with six degrees of freedom. This represents an additional advantage as no mathematical transformation is needed for the robot to reproduce recorded movements.ResultsBased on the described procedure, Bionic Jaw Motion provide accurate recording and reproduction of maxillomandibular relation in static and dynamic conditions.ConclusionThis robotic system represents an important advancement compared to available analogical and digital alternatives both in clinical and research contexts for cost reduction, precision and time saving opportunities.

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Robot Training With Vector Fields Based on Stroke Survivors' Individual Movement Statistics.

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Robot Training With Vector Fields Based on Stroke Survivors' Individual Movement Statistics.

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