Multimodal decoding and congruent sensory information enhance reaching performance in subjects with cervical spinal cord injury

EMG and SSVEP-based bimodal estimation of elbow angle trajectory

Detecting intentions and estimating movement trajectories in a human-machine interface (HMI) using electromyogram (EMG) signals is particularly challenging, especially for individuals with movement impairments. Therefore, incorporating additional information from other biological sources, potential discrete information in the movement, and the EMG signal can be practical. This study combined EMG and target information to enhance estimation performance during reaching movements. EMG activity of the shoulder and arm muscles, elbow angle, and the electroencephalogram signals of ten healthy subjects were recorded while they reached blinking targets. The reaching target was recognized by steady-state visual evoked potential (SSVEP). The selected target's final angle and EMG were then mapped to the elbow angle trajectory. The proposed bimodal structure, which integrates EMG and final elbow angle information, outperformed the EMG-based decoder. Even under conditions of higher fatigue, the proposed structure provided better performance than the EMG decoder. Including additional information about the recognized reaching target in the trajectory model improved the estimation of the reaching profile. Consequently, this study's findings suggest that bimodal decoders are highly beneficial for enhancing assistive robotic devices and prostheses, especially for real-time upper limb rehabilitation.

Feature evaluation for myoelectric pattern recognition of multiple nearby reaching targets

Intention detection of the reaching movement is considerable for myoelectric human and machine collaboration applications. A comprehensive set of handcrafted features was mined from windows of electromyogram (EMG) of the upper-limb muscles while reaching nine nearby targets like activities of daily living. The feature selection-based scoring method, neighborhood component analysis (NCA), selected the relevant feature subset. Finally, the target was recognized by the support vector machine (SVM) model. The classification performance was generalized by a nested cross-validation structure that selected the optimal feature subset in the inner loop. According to the low spatial resolution of the target location on display and following the slight discrimination of signals between targets, the best classification accuracy of 77.11 % was achieved for concatenating the features of two segments with a length of 2 and 0.25 s. Due to the lack of subtle variation in EMG, while reaching different targets, a wide range of features was applied to consider additional aspects of the knowledge contained in EMG signals. Furthermore, since NCA selected features that provided more discriminant power, it became achievable to employ various combinations of features and even concatenated features extracted from different movement parts to improve classification performance.

A Multimodal Assistive-Robotic-Arm Control System to Increase Independence After Tetraplegia

Following tetraplegia, independence for completing essential daily tasks, such as opening doors and eating, significantly declines. Assistive robotic manipulators (ARMs) could restore independence, but typically input devices for these manipulators require functional use of the hands. We created and validated a hands-free multimodal input system for controlling an ARM in virtual reality using combinations of a gyroscope, eye-tracking, and heterologous surface electromyography (sEMG). These input modalities are mapped to ARM functions based on the user's preferences and to maximize the utility of their residual volitional capabilities following tetraplegia. The two participants in this study with tetraplegia preferred to use the control mapping with sEMG button functions and disliked winking commands. Non-disabled participants were more varied in their preferences and performance, further suggesting that customizability is an advantageous component of the control system. Replacing buttons from a traditional handheld controller with sEMG did not substantively reduce performance. The system provided adequate control to all participants to complete functional tasks in virtual reality such as opening door handles, turning stove dials, eating, and drinking, all of which enable independence and improved quality of life for these individuals.

Plasticity in Cervical Motor Circuits following Spinal Cord Injury and Rehabilitation

Simple Summary

Spinal cord injury results in a decreased quality of life and impacts hundreds of thousands of people in the US alone. This review discusses the underlying cellular mechanisms of injury and the concurrent therapeutic hurdles that impede recovery. It then describes the phenomena of neural plasticity—the nervous system’s ability to change. The primary focus of the review is on the impact of cervical spinal cord injury on control of the upper limbs. The neural plasticity that occurs without intervention is discussed, which shows new connections growing around the injury site and the involvement of compensatory movements. Rehabilitation-driven neural plasticity is shown to have the ability to guide connections to create more normal functions. Various novel stimulation and recording technologies are outlined for their role in further improving rehabilitative outcomes and gains in independence. Finally, the importance of sensory input, an often-overlooked aspect of motor control, is shown in driving neural plasticity. Overall, this review seeks to delineate the historical and contemporary research into neural plasticity following injury and rehabilitation to guide future studies.

Abstract

Neuroplasticity is a robust mechanism by which the central nervous system attempts to adapt to a structural or chemical disruption of functional connections between neurons. Mechanical damage from spinal cord injury potentiates via neuroinflammation and can cause aberrant changes in neural circuitry known as maladaptive plasticity. Together, these alterations greatly diminish function and quality of life. This review discusses contemporary efforts to harness neuroplasticity through rehabilitation and neuromodulation to restore function with a focus on motor recovery following cervical spinal cord injury. Background information on the general mechanisms of plasticity and long-term potentiation of the nervous system, most well studied in the learning and memory fields, will be reviewed. Spontaneous plasticity of the nervous system, both maladaptive and during natural recovery following spinal cord injury is outlined to provide a baseline from which rehabilitation builds. Previous research has focused on the impact of descending motor commands in driving spinal plasticity. However, this review focuses on the influence of physical therapy and primary afferent input and interneuron modulation in driving plasticity within the spinal cord. Finally, future directions into previously untargeted primary afferent populations are presented.

Properties of the surface electromyogram following traumatic spinal cord injury: a scoping review

Traumatic spinal cord injury (SCI) disrupts spinal and supraspinal pathways, and this process is reflected in changes in surface electromyography (sEMG). sEMG is an informative complement to current clinical testing and can capture the residual motor command in great detail-including in muscles below the level of injury with seemingly absent motor activities. In this comprehensive review, we sought to describe how the sEMG properties are changed after SCI. We conducted a systematic literature search followed by a narrative review focusing on sEMG analysis techniques and signal properties post-SCI. We found that early reports were mostly focused on the qualitative analysis of sEMG patterns and evolved to semi-quantitative scores and a more detailed amplitude-based quantification. Nonetheless, recent studies are still constrained to an amplitude-based analysis of the sEMG, and there are opportunities to more broadly characterize the time- and frequency-domain properties of the signal as well as to take fuller advantage of high-density EMG techniques. We recommend the incorporation of a broader range of signal properties into the neurophysiological assessment post-SCI and the development of a greater understanding of the relation between these sEMG properties and underlying physiology. Enhanced sEMG analysis could contribute to a more complete description of the effects of SCI on upper and lower motor neuron function and their interactions, and also assist in understanding the mechanisms of change following neuromodulation or exercise therapy.

Fatigue in complete spinal cord injury and implications on total delay

The use of neuromuscular electrical stimulation (NMES) to artificially restore movement in people with complete spinal cord injury (SCI) induces an accelerated process of muscle fatigue. Fatigue increases the time between the beginning of NMES and the onset of muscle force (Delay_TOT ). Understanding how much muscle fatigue affects the Delay_TOT in people with SCI could help in the design of closed-loop neuroprostheses that compensate for this delay, thus making the control system more stable. The aim of this study was to evaluate the impact of the extent of fatigue on Delay_TOT and peak force of the lower limbs in people with complete SCI. Fifteen men-young adults with complete SCI (paraplegia and tetraplegia) and stable health-participated in the experiment. Delay_TOT was defined as the time interval between the beginning of NMES application until the onset of muscle force. The electrical intensity of NMES applied was adjusted individually and consisted of the amplitude required to obtain a full extension of the knee (0°), considering the maximum electrically stimulated extension (MESE). Subsequently, 70% of the MESE was applied during the fatigue induction protocol. Significant differences were identified between the moments before and after the fatigue protocol, both for peak force (P ≤ .026) and Delay_TOT (P ≤ .001). The medians and interquartile range of the Delay_TOT were higher in postfatigue (199.0 ms) when compared to the moment before fatigue (146.5 ms). The medians and interquartile range of the peak force were higher in unfatigued lower limbs (0.43 kgf) when compared to the moment postfatigue (0.27 kgf). The results support the hypothesis that muscle fatigue influences the increase in Delay_TOT and decrease in force production in people with SCI. For future applications, the combined evaluation of the delay and force in SCI patients provides valuable feedback for NMES paradigms. The study will provide potentially critical muscle mechanical evidence for the investigation of the evolution of atrophy.

Reachy, a 3D-Printed Human-Like Robotic Arm as a Testbed for Human-Robot Control Strategies

To this day, despite the increasing motor capability of robotic devices, elaborating efficient control strategies is still a key challenge in the field of humanoid robotic arms. In particular, providing a human “pilot” with efficient ways to drive such a robotic arm requires thorough testing prior to integration into a finished system. Additionally, when it is needed to preserve anatomical consistency between pilot and robot, such testing requires to employ devices showing human-like features. To fulfill this need for a biomimetic test platform, we present Reachy, a human-like life-scale robotic arm with seven joints from shoulder to wrist. Although Reachy does not include a poly-articulated hand and is therefore more suitable for studying reaching than manipulation, a robotic hand prototype from available third-party projects could be integrated to it. Its 3D-printed structure and off-the-shelf actuators make it inexpensive relatively to the price of an industrial-grade robot. Using an open-source architecture, its design makes it broadly connectable and customizable, so it can be integrated into many applications. To illustrate how Reachy can connect to external devices, this paper presents several proofs of concept where it is operated with various control strategies, such as tele-operation or gaze-driven control. In this way, Reachy can help researchers to explore, develop and test innovative control strategies and interfaces on a human-like robot.

A Hybrid FPGA-Based System for EEG- and EMG-Based Online Movement Prediction

A current trend in the development of assistive devices for rehabilitation, for example exoskeletons or active orthoses, is to utilize physiological data to enhance their functionality and usability, for example by predicting the patient's upcoming movements using electroencephalography (EEG) or electromyography (EMG). However, these modalities have different temporal properties and classification accuracies, which results in specific advantages and disadvantages. To use physiological data analysis in rehabilitation devices, the processing should be performed in real-time, guarantee close to natural movement onset support, provide high mobility, and should be performed by miniaturized systems that can be embedded into the rehabilitation device. We present a novel Field Programmable Gate Array (FPGA) -based system for real-time movement prediction using physiological data. Its parallel processing capabilities allows the combination of movement predictions based on EEG and EMG and additionally a P300 detection, which is likely evoked by instructions of the therapist. The system is evaluated in an offline and an online study with twelve healthy subjects in total. We show that it provides a high computational performance and significantly lower power consumption in comparison to a standard PC. Furthermore, despite the usage of fixed-point computations, the proposed system achieves a classification accuracy similar to systems with double precision floating-point precision.