Background: Robotic exoskeleton (RE) based gait training involves repetitive task-oriented movements and weight shifts to promote functional recovery. To effectively understand the neuromuscular alterations occurring due to hemiplegia as well as due to the utilization of RE in acute stroke, there is a need for electromyography (EMG) techniques that not only quantify the intensity of muscle activations but also quantify and compare activation timings in different gait training environments.Purpose: To examine the applicability of a novel EMG analysis technique, Burst Duration Similarity Index (BDSI) during a single session of inpatient gait training in RE and during traditional overground gait training for individuals with acute stroke.Methods: Surface EMG was collected bilaterally with and without the RE device for five participants with acute stroke during the normalized gait cycle to measure lower limb muscle activations. EMG outcomes included integrated EMG (iEMG) calculated from the root-mean-square profiles, and a novel measure, BDSI derived from activation timing comparisons.Results: EMG data demonstrated volitional although varied levels of muscle activations on the affected and unaffected limbs, during gait with and without the RE. During the stance phase mean iEMG of the soleus (p = 0.019) and rectus femoris (RF) (p = 0.017) on the affected side significantly decreased with RE, as compared to without the RE. The differences in mean BDSI scores on the affected side with RE were significantly higher than without RE for the vastus lateralis (VL) (p = 0.010) and RF (p = 0.019).Conclusions: A traditional amplitude analysis (iEMG) and a novel timing analysis (BDSI) techniques were presented to assess the neuromuscular adaptations resulting in lower extremities muscles during RE assisted hemiplegic gait post acute stroke. The RE gait training environment allowed participants with hemiplegia post acute stroke to preserve their volitional neuromuscular activations during gait iEMG and BDSI analyses showed that the neuromuscular changes occurring in the RE environment were characterized by correctly timed amplitude and temporal adaptations. As a result of these adaptations, VL and RF on the affected side closely matched the activation patterns of healthy gait. Preliminary EMG data suggests that the RE provides an effective gait training environment for in acute stroke rehabilitation.

Background: Co-occurring mobility and cognitive impairments are common, debilitating, and poorly-managed with pharmacological therapies in persons with multiple sclerosis (MS). Exercise rehabilitation (ER), particularly walking ER, has been suggested as one of the best approaches for managing these manifestations of MS. However, there is a focal lack of efficacy of ER on mobility and cognitive outcomes in persons with MS who present with substantial neurological disability. Such severe neurological disability oftentimes precludes the ability for participation in highly-intensive and repetitive ER that is necessary for eliciting adaptations in mobility and cognition. To address such a concern, robotic exoskeleton-assisted ER (REAER) might represent a promising intervention approach for managing co-occurring mobility and cognitive impairments in those with substantial MS disability who might not benefit from traditional ER. Methods: The current pilot single-blind, randomized controlled trial (RCT) compared the effects of 4-weeks of REAER with 4-weeks of conventional gait training (CGT) as a standard-of-care control condition on functional mobility (timed up-and-go; TUG), walking endurance (six-minute walk test; 6MWT), cognitive processing speed (CPS; Symbol Digit Modalities Test; SDMT), and brain connectivity (thalamocortical resting-state functional connectivity (RSFC) based on fMRI) outcomes in 10 persons with substantial MS-related neurological disability. Results: Overall, compared with CGT, 4-weeks of REAER was associated with large improvements in functional mobility (η p 2 =.38), CPS (η p 2 =.53), and RSFC between the thalamus and ventromedial prefrontal cortex (η p 2 =.72),but not walking endurance (η p 2 =.01). Further, changes in RSFC were moderately associated with changes in TUG, 6MWT, and SDMT performance, respectively, whereby increased thalamocortical RSFC was associated with improved functional mobility, walking endurance, and CPS (|ρ|>.36). Conclusion:The current pilot RCT provides initial support for REAER as an approach for improving functional mobility and CPS, perhaps based on adaptive and integrative central nervous system plasticity, namely increases in RSFC between the thalamus and ventromedial prefrontal cortex, in a small sample of persons with substantial MS disability. Such a pilot trial provides proof-of-concept data for the design and implementation of an appropriately-powered RCT of REAER in a larger sample of persons with MS who present with co-occurring impairments in both mobility and cognitive functioning.

Background: Few studies have systematically investigated robust controllers for lower limb rehabilitation exoskeletons (LLREs) that can safely and effectively assist users with a variety of neuromuscular disorders to walk with full autonomy. One of the key challenges for developing such a robust controller is to handle different degrees of uncertain human-exoskeleton interaction forces from the patients. Consequently, conventional walking controllers either are patient-condition specific or involve tuning of many control parameters, which could behave unreliably and even fail to maintain balance. Methods: We present a novel and robust controller for a LLRE based on a decoupled deep reinforcement learning framework with three independent networks, which aims to provide reliable walking assistance against various and uncertain human-exoskeleton interaction forces. The exoskeleton controller is driven by a neural network control policy that acts on a stream of the LLRE’s proprioceptive signals, including joint kinematic states, and subsequently predicts real-time position control targets for the actuated joints. To handle uncertain human-interaction forces, the control policy is trained intentionally with an integrated human musculoskeletal model and realistic human-exoskeleton interaction forces. Two other neural networks are connected with the control policy network to predict the interaction forces and muscle coordination. To further increase the robustness of the control policy, we employ domain randomization during training that includes not only randomization of exoskeleton dynamics properties but, more importantly, randomization of human muscle strength to simulate the variability of the patient’s disability. Through this decoupled deep reinforcement learning framework, the trained controller of LLREs is able to provide reliable walking assistance to the human with different degrees of neuromuscular disorders. Results and Conclusion: A universal, RL-based walking controller is trained and virtually tested on a LLRE system to verify its effectiveness and robustness in assisting users with different disabilities such as passive muscles (quadriplegic), muscle weakness, or hemiplegic conditions. An ablation study demonstrates strong robustness of the control policy under large exoskeleton dynamic property ranges and various human-exoskeleton interaction forces. The decoupled network structure allows us to isolate the LLRE control policy network for testing and sim-to-real transfer since it uses only proprioception information of the LLRE (joint sensory state) as the input. Furthermore, the controller is shown to be able to handle different patient conditions without the need for patient-specific control parameters tuning.