Characterizing and predicting submovements during human three-dimensional arm reaches

Novel evaluation of upper-limb motor performance after stroke based on normal reaching movement model

BACKGROUND

Upper-limb rehabilitation robots provide repetitive reaching movement training to post-stroke patients. Beyond a pre-determined set of movements, a robot-aided training protocol requires optimization to account for the individuals' unique motor characteristics. Therefore, an objective evaluation method should consider the pre-stroke motor performance of the affected arm to compare one's performance relative to normalcy. However, no study has attempted to evaluate performance based on an individual's normal performance. Herein, we present a novel method for evaluating upper limb motor performance after a stroke based on a normal reaching movement model.

METHODS

To represent the normal reaching performance of individuals, we opted for three candidate models: (1) Fitts' law for the speed-accuracy relationship, (2) the Almanji model for the mouse-pointing task of cerebral palsy, and (3) our proposed model. We first obtained the kinematic data of healthy (n = 12) and post-stroke (n = 7) subjects with a robot to validate the model and evaluation method and conducted a pilot study with a group of post-stroke patients (n = 12) in a clinical setting. Using the models obtained from the reaching performance of the less-affected arm, we predicted the patients' normal reaching performance to set the standard for evaluating the affected arm.

RESULTS

We verified that the proposed normal reaching model identifies the reaching of all healthy (n = 12) and less-affected arm (n = 19; 16 of them showed an R² > 0.7) but did not identify erroneous reaching of the affected arm. Furthermore, our evaluation method intuitively and visually demonstrated the unique motor characteristics of the affected arms.

CONCLUSIONS

The proposed method can be used to evaluate an individual's reaching characteristics based on an individuals normal reaching model. It has the potential to provide individualized training by prioritizing a set of reaching movements.

Improving the objectivity of the current World Para Swimming motor coordination test for swimmers with hypertonia, ataxia and athetosis using measures of movement smoothness, rhythm and accuracy

The current protocol for classifying Para swimmers with hypertonia, ataxia and athetosis involves a physical assessment where the individual's ability to coordinate their limbs is scored by subjective clinical judgment. The lack of objective measurement renders the current test unsuitable for evidence-based classification. This study evaluated a revised version of the Para swimming assessment for motor coordination, incorporating practical, objective measures of movement smoothness, rhythm error and accuracy. Nineteen Para athletes with hypertonia and 19 non-disabled participants performed 30 s trials of bilateral alternating shoulder flexion-extension at 30 bpm and 120 bpm. Accelerometry was used to quantify movement smoothness; rhythm error and accuracy were obtained from video. Para athletes presented significantly less smooth movement and higher rhythm error than the non-disabled participants (p < 0.05). Random forest algorithm successfully classified 89% of participants with hypertonia during out-of-bag predictions. The most important predictors in classifying participants were movement smoothness at both movement speeds, and rhythm error at 120 bpm. Our results suggest objective measures of movement smoothness and rhythm error included in the current motor coordination test protocols can be used to infer impairment in Para swimmers with hypertonia. Further research is merited to establish the relationship of these measures with swimming performance.

The reaching movement in breast cancer survivors: Attention to the principles of rehabilitation

INTRODUCTION

Breast-cancer is leading cause of morbidity and mortality in women. The prognosis and survival rate of women with breast-cancer have significantly improved worldwide; more attention needs to be paid to rehabilitative interventions after surgery. This paper describes use of reaching movement to assess upper limb motorcontrol and functional ability after breast-cancer surgery (BC).

MATERIAL AND METHODS

We conducted a cross-sectional observational study consisting of biomechanical evaluation of upper limb limitations in women BC, versus a controlgroup (CG). Thirty breast-cancer survivors and thirty healthy women participated in this study. Both groups were subjected to clinical evaluation of the shoulder joint ROM on the operated side, as an assessment of the muscular-strength of the shoulder with the MRC-scale. The Functional-Assessment was evaluated by the DASH and Constant-Murley-Score. The EORTC QLQ-C30 and VAS were used to measure the quality of life assessment and pain respectively. A Biomechanical evaluation was performed, using Reaching-Task and Surface-EMG.

RESULTS

Normal Jerk for BC was higher than CG. Target approaching velocity and movement duration BC was lower than CG. Synergy Anterior Deltoid/Triceps Brachii muscles in CG was higher than BC.

Elbow angle generation during activities of daily living using a submovement prediction model

The present study aimed to develop a realistic model for the generation of human activities of daily living (ADL) movements. The angular profiles of the elbow joint during functional ADL tasks such as eating and drinking were generated by a submovement-based closed-loop model. First, the ADL movements recorded from three human participants were broken down into logical phases, and each phase was decomposed into submovement components. Three separate artificial neural networks were trained to learn the submovement parameters and were then incorporated into a closed-loop model with error correction ability. The model was able to predict angular trajectories of human ADL movements with target access rate = 100%, VAF = 98.9%, and NRMSE = 4.7% relative to the actual trajectories. In addition, the model can be used to provide the desired target for practical trajectory planning in rehabilitation systems such as functional electrical stimulation, robot therapy, brain-computer interface, and prosthetic devices.

Signal-independent noise in intracortical brain-computer interfaces causes movement time properties inconsistent with Fitts' law

OBJECTIVE

Do movements made with an intracortical BCI (iBCI) have the same movement time properties as able-bodied movements? Able-bodied movement times typically obey Fitts' law: [Formula: see text] (where MT is movement time, D is target distance, R is target radius, and [Formula: see text] are parameters). Fitts' law expresses two properties of natural movement that would be ideal for iBCIs to restore: (1) that movement times are insensitive to the absolute scale of the task (since movement time depends only on the ratio [Formula: see text]) and (2) that movements have a large dynamic range of accuracy (since movement time is logarithmically proportional to [Formula: see text]).

APPROACH

Two participants in the BrainGate2 pilot clinical trial made cortically controlled cursor movements with a linear velocity decoder and acquired targets by dwelling on them. We investigated whether the movement times were well described by Fitts' law.

MAIN RESULTS

We found that movement times were better described by the equation [Formula: see text], which captures how movement time increases sharply as the target radius becomes smaller, independently of distance. In contrast to able-bodied movements, the iBCI movements we studied had a low dynamic range of accuracy (absence of logarithmic proportionality) and were sensitive to the absolute scale of the task (small targets had long movement times regardless of the [Formula: see text] ratio). We argue that this relationship emerges due to noise in the decoder output whose magnitude is largely independent of the user's motor command (signal-independent noise). Signal-independent noise creates a baseline level of variability that cannot be decreased by trying to move slowly or hold still, making targets below a certain size very hard to acquire with a standard decoder.

SIGNIFICANCE

The results give new insight into how iBCI movements currently differ from able-bodied movements and suggest that restoring a Fitts' law-like relationship to iBCI movements may require non-linear decoding strategies.

A fresh perspective on dissecting action into discrete submotions

The hypothesis that reaching motions are constructed from discrete components has been explored since the earliest scientific investigations of human movement, although composition specifics have been contentious. We reinspect this process by analyzing the underlying motor intent (rather than actual motion) using our recently-developed intent determination technique. First, synthetic data analysis was used to determine our accuracy in detecting submotion events. Next, we evaluated this on healthy reaching movements and overcame the problem of indistinguishably blended submotions by exposing subjects to strong, abruptly changing forces, which lead to clear corrections identifiable using direction-based clustering. We were able to accurately recover submotion parameters and identify patterns in submotion count, peak kinetic energy, and peak-to-peak duration. These values were all exponentially distributed, which implies that selection of submotions may follow simple rules. This provides a novel opportunity to investigate human motor action using the tools of statistics.

Velocity neurons improve performance more than goal or position neurons do in a simulated closed-loop BCI arm-reaching task

Brain-Computer Interfaces (BCIs) that convert brain-recorded neural signals into intended movement commands could eventually be combined with Functional Electrical Stimulation to allow individuals with Spinal Cord Injury to regain effective and intuitive control of their paralyzed limbs. To accelerate the development of such an approach, we developed a model of closed-loop BCI control of arm movements that (1) generates realistic arm movements (based on experimentally measured, visually-guided movements with real-time error correction), (2) simulates cortical neurons with firing properties consistent with literature reports, and (3) decodes intended movements from the noisy neural ensemble. With this model we explored (1) the relative utility of neurons tuned for different movement parameters (position, velocity, and goal) and (2) the utility of recording from larger numbers of neurons-critical issues for technology development and for determining appropriate brain areas for recording. We simulated arm movements that could be practically restored to individuals with severe paralysis, i.e., movements from an armrest to a volume in front of the person. Performance was evaluated by calculating the smallest movement endpoint target radius within which the decoded cursor position could dwell for 1 s. Our results show that goal, position, and velocity neurons all contribute to improve performance. However, velocity neurons enabled smaller targets to be reached in shorter amounts of time than goal or position neurons. Increasing the number of neurons also improved performance, although performance saturated at 30-50 neurons for most neuron types. Overall, our work presents a closed-loop BCI simulator that models error corrections and the firing properties of various movement-related neurons that can be easily modified to incorporate different neural properties. We anticipate that this kind of tool will be important for development of future BCIs.