Targeted walking in incomplete spinal cord injury: Role of corticospinal control

Locomotor recovery after incomplete spinal cord injury (iSCI) is influenced by spinal and supraspinal networks. Conventional clinical gait analysis fails to differentiate between these components. There is evidence that corticospinal control is enhanced during targeted walking, where each foot must be continuously placed on visual targets in randomized order. This study investigates the potential of targeted walking in the functional assessment of corticospinal integrity. Twenty-one controls and 16 individuals with chronic iSCI performed normal and targeted walking on a treadmill while electromyograms (EMGs) and kinematics were recorded. Precision (% of accurate foot placements) in targeted walking was significantly lower in individuals with iSCI (82.9±14.7%, controls: 94.9±4.0%). While the overall kinematic pattern was comparable between walking conditions, controls showed significantly higher semitendinosus activity before heel-strike during targeted walking. This was accompanied by a shift of relative EMG intensity from 90-120Hz to lower frequencies of 20-60 Hz, previously associated with corticospinal control of muscle activity. Targeted walking in iSCI individuals evoked smaller EMG changes, suggesting that the switch to more corticospinal control is impaired. Accordingly, mildly impaired iSCI individuals revealed higher adaptations to the targeted walking task than more-impaired individuals. Recording of EMGs during targeted walking holds potential as a research tool to reveal further insights into the neuromuscular control of locomotion. It also complements findings of preclinical studies and is a promising novel surrogate marker of integrity of corticospinal control in individuals with iSCI and other neurological impairments. Future studies should investigate its potential for diagnosis or tracking recovery during rehabilitation

Overground walking patterns after chronic incomplete spinal cord injury show distinct response patterns to unloading

Abstract Background Body weight support (BWS) is often provided to incomplete spinal cord injury (iSCI) patients during rehabilitation to enable gait training before full weight-bearing is recovered. Emerging robotic devices enable BWS during overground walking, increasing task-specificity of the locomotor training. However, in contrast to a treadmill setting, there is little information on how unloading is integrated into overground locomotion. We investigated the effect of a transparent multi-directional BWS system on overground walking patterns at different levels of unloading in individuals with chronic iSCI (CiSCI) compared to controls. Methods Kinematics of 12 CiSCI were analyzed at six different BWS levels from 0 to 50% body weight unloading during overground walking at 2kmh− 1 and compared to speed-matched controls. Results In controls, temporal parameters, single joint trajectories, and intralimb coordination responded proportionally to the level of unloading, while spatial parameters remained unaffected. In CiSCI, unloading induced similar changes in temporal parameters. CiSCI, however, did not adapt their intralimb coordination or single joint trajectories to the level of unloading. Conclusions The findings revealed that continuous, dynamic unloading during overground walking results in subtle and proportional gait adjustments corresponding to changes in body load. CiSCI demonstrated diminished responses in specific domains of gait, indicating that their altered neural processing impeded the adjustment to environmental constraints. CiSCI retain their movement patterns under overground unloading, indicating that this is a viable locomotor therapy tool that may also offer a potential window on the diminished neural control of intralimb coordination

DHP19: Dynamic Vision Sensor 3D Human Pose Dataset

Human pose estimation has dramatically improved thanks to the continuous developments in deep learning. However, marker-free human pose estimation based on standard frame-based cameras is still slow and power hungry for real-time feedback interaction because of the huge number of operations necessary for large Convolutional Neural Network (CNN) inference. Event-based cameras such as the Dynamic Vision Sensor (DVS) quickly output sparse moving-edge information. Their sparse and rapid output is ideal for driving low-latency CNNs, thus potentially allowing real-time interaction for human pose estimators. Although the application of CNNs to standard frame-based cameras for human pose estimation is well established, their application to event-based cameras is still under study. This paper proposes a novel benchmark dataset of human body movements, the Dynamic Vision Sensor Human Pose dataset (DHP19). It consists of recordings from 4 synchronized 346x260 pixel DVS cameras, for a set of 33 movements with 17 subjects. DHP19 also includes a 3D pose estimation model that achieves an average 3D pose estimation error of about 8 cm, despite the sparse and reduced input data from the DVS