Early Predictability of Grasping Movements by Neurofunctional Representations: A Feasibility Study

Human grasping is a relatively fast process and control signals for upper limb prosthetics cannot be generated and processed in a sufficiently timely manner. The aim of this study was to examine whether discriminating between different grasping movements at a cortical level can provide information prior to the actual grasping process, allowing for more intuitive prosthetic control. EEG datasets were captured from 13 healthy subjects who repeatedly performed 16 activities of daily living. Common classifiers were trained on features extracted from the waking-state frequency and total-frequency time domains. Different training scenarios were used to investigate whether classifiers can already be pre-trained by base networks for fine-tuning with data of a target person. A support vector machine algorithm with spatial covariance matrices as EEG signal descriptors based on Riemannian geometry showed the highest balanced accuracy (0.91 ± 0.05 SD) in discriminating five grasping categories according to the Cutkosky taxonomy in an interval from 1.0 s before to 0.5 s after the initial movement. Fine-tuning did not improve any classifier. No significant accuracy differences between the two frequency domains were apparent (p > 0.07). Neurofunctional representations enabled highly accurate discrimination of five different grasping movements. Our results indicate that, for upper limb prosthetics, it is possible to use them in a sufficiently timely manner and to predict the respective grasping task as a discrete category to kinematically prepare the prosthetic hand

U-Limb: A multi-modal, multi-center database on arm motion control in healthy and post-stroke conditions

BACKGROUND: Shedding light on the neuroscientific mechanisms of human upper limb motor control, in both healthy and disease conditions (e.g., after a stroke), can help to devise effective tools for a quantitative evaluation of the impaired conditions, and to properly inform the rehabilitative process. Furthermore, the design and control of mechatronic devices can also benefit from such neuroscientific outcomes, with important implications for assistive and rehabilitation robotics and advanced human-machine interaction. To reach these goals, we believe that an exhaustive data collection on human behavior is a mandatory step. For this reason, we release U-Limb, a large, multi-modal, multi-center data collection on human upper limb movements, with the aim of fostering trans-disciplinary cross-fertilization. CONTRIBUTION: This collection of signals consists of data from 91 able-bodied and 65 post-stroke participants and is organized at 3 levels: (i) upper limb daily living activities, during which kinematic and physiological signals (electromyography, electro-encephalography, and electrocardiography) were recorded; (ii) force-kinematic behavior during precise manipulation tasks with a haptic device; and (iii) brain activity during hand control using functional magnetic resonance imaging

What Is the Impact of a CAM Impingement on the Gait Cycle in Patients with Progressive Osteoarthritis of the Hip?

(1) Background: The femoroacetabular impingement (FAI) type cam leads to a conflict between the acetabular rim and a bony thickening of the femoral neck junction. While maximal excursions in flexion, adduction and internal rotation provoke pain, the aim of this study was to analyze if a cam morphology shows an impact on gait pattern. (2) Methods: Fifty-five patients with end-stage hip osteoarthritis performed gait analysis before hip replacement as well as three, six and 12 months postoperatively. Thirty-three (60%) of them presented an FAI type cam. An ANOVA was used to compare the hip angles in sagittal, frontal and transversal planes between patients with a FAI type cam (group “+cam”) and without (group “−cam”). (3) Results: Before surgery the patients of the +cam-group showed a tendency towards a reduced flexion and internal rotation at the heel strike (p > 0.05). Over time, the differences were adjusted by total hip arthroplasty. (4) Conclusions: We did not find any differences in the gait analysis of patients with a FAI type cam compared to patients without

Predictive simulation of post-stroke gait with functional electrical stimulation

International audienceAbstract Post-stroke patients present various gait abnormalities such as drop foot, stiff-knee gait (SKG), and knee hyperextension. Functional electrical stimulation (FES) improves drop foot gait although the mechanistic basis for this effect is not well understood. To answer this question, we evaluated the gait of a post-stroke patient walking with and without FES by inverse dynamics analysis and compared the results to an optimal control framework. The effect of FES and cause-effect relationship of changes in knee and ankle muscle strength were investigated; personalized muscle–tendon parameters allowed the prediction of pathologic gait. We also predicted healthy gait patterns at different speeds to simulate the subject walking without impairment. The passive moment of the knee played an important role in the estimation of muscle force with knee hyperextension, which was decreased during FES and knee extensor strengthening. Weakening the knee extensors and strengthening the flexors improved SKG. During FES, weak ankle plantarflexors and strong ankle dorsiflexors resulted in increased ankle dorsiflexion, which reduced drop foot. FES also improved gait speed and reduced circumduction. These findings provide insight into compensatory strategies adopted by post-stroke patients that can guide the design of individualized rehabilitation and treatment programs

Predicting the effects of knee extensor muscle weakening and strengthening on a post-stroke gait

International audienceStroke may cause different gait abnormalities, such as knee hyperextension and stiff-knee gait. This study used predictive simulation to investigate how the weakening and strengthening of the knee extensor muscles affect the gait pattern of a post-stoke patient. The prediction result showed impairments similar to those observed in the gait obtained by the inverse dynamics. While the predictive simulation of muscle weakening corrected the stiff-knee gait, the gait prediction of muscle strengthening decreased the knee hyperextension exhibited in the gait pattern of the patient

Predicting the effects of knee extensor muscle weakening and strengthening on a post-stroke gait

International audienceStroke may cause different gait abnormalities, such as knee hyperextension and stiff-knee gait. This study used predictive simulation to investigate how the weakening and strengthening of the knee extensor muscles affect the gait pattern of a post-stoke patient. The prediction result showed impairments similar to those observed in the gait obtained by the inverse dynamics. While the predictive simulation of muscle weakening corrected the stiff-knee gait, the gait prediction of muscle strengthening decreased the knee hyperextension exhibited in the gait pattern of the patient

The influence of stiff-knee gait on EMG and muscle activation during predictive simulation of a knee disarticulation patient and a healthy subject

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Primary rotational stability of various megaprostheses in a biomechanical sawbone model with proximal femoral defects extending to the isthmus.

Fixation of proximal femoral megaprostheses is achieved in the diaphyseal isthmus. We hypothesized that after extended bone resection including the proximal part of the isthmus a reduced length of fixation will affect the stability and fixation characteristics of these megaprostheses. The aim of this study was to analyze in a validated sawbone model with extended proximal femoral defects which types of implants have sufficient primary stability to allow osteointegration and to describe their fixation characteristics.Four different cementless megaprostheses were implanted into 16 Sawbones with an AAOS type III defect after resection 11 cm below the lesser trochanter involving the proximal isthmus. To determine the primary implant stability relative micromotions between bone and implant were measured in relation to a cyclic torque of 7Nm applied on the longitudinal axis of the implant. We determined the fixation characteristics of the different implant designs by comparing these relative micromotions along the longitudinal stem axis.In the tested sawbones all studied implants showed sufficient primary stability to admit bone integration with relative micromotions below 150 µm after adapting our results to physiologic hip joint loadings. Different fixation characteristics of the megaprostheses were determined, which could be explained by their differing design and fixation concepts.Cementless megaprostheses of different designs seem to provide sufficient primary stability to bridge proximal femoral defects if the diaphyseal isthmus is partially preserved. In our sawbone model the different implant fixation patterns can be related to their stem designs. No evidence can be provided to favor one of the studied implants in this setting. However, femoral morphology is variable and in different isthmus configurations specific implant designs might be appropriate to achieve the most favorable primary stability, which enables bone integration and consequently long term implant stability

Early Predictability of Grasping Movements by Neurofunctional Representations: A Feasibility Study

Human grasping is a relatively fast process and control signals for upper limb prosthetics cannot be generated and processed in a sufficiently timely manner. The aim of this study was to examine whether discriminating between different grasping movements at a cortical level can provide information prior to the actual grasping process, allowing for more intuitive prosthetic control. EEG datasets were captured from 13 healthy subjects who repeatedly performed 16 activities of daily living. Common classifiers were trained on features extracted from the waking-state frequency and total-frequency time domains. Different training scenarios were used to investigate whether classifiers can already be pre-trained by base networks for fine-tuning with data of a target person. A support vector machine algorithm with spatial covariance matrices as EEG signal descriptors based on Riemannian geometry showed the highest balanced accuracy (0.91 ± 0.05 SD) in discriminating five grasping categories according to the Cutkosky taxonomy in an interval from 1.0 s before to 0.5 s after the initial movement. Fine-tuning did not improve any classifier. No significant accuracy differences between the two frequency domains were apparent (p > 0.07). Neurofunctional representations enabled highly accurate discrimination of five different grasping movements. Our results indicate that, for upper limb prosthetics, it is possible to use them in a sufficiently timely manner and to predict the respective grasping task as a discrete category to kinematically prepare the prosthetic hand