Is gait training with the elliptically based robotic gait trainer (EBRGT) feasible in ambulatory patients after stroke?

In response to the potential benefits of task specific training in rehabilitation of gait after stroke and the need for affordable, simple ways to implement it, our group designed the elliptically based robotic gait trainer (EBRGT). A design review of the EBRGT, covering the design goals, an overview of the mechanical and electrical design, and a discussion of the novelty of the device and why it may be beneficial for individuals with hemiparesis secondary to stroke is discussed (Chapter 2). To characterize the new device, a study was performed to determine if the EBRGT produced a gait pattern that mimicked level surface walking in healthy adults (chapter 3). Sagittal plane kinematic analysis suggested the EBRGT produced joint movement patterns that are similar to level surface walking at the hip and knee with less similarity between activities at the ankle. Electromyography (EMG) revealed that the EBRGT induced a cyclic muscle firing pattern that had some similarities when compared to level surface walking. We also examined the feasibility of ambulatory individuals after stroke to use the EBRGT and if their movement patterns were similar to healthy adults walking on the same device (Chapter 4). All six participants were able to walk on the device with minimal assistance. These participants had joint kinematics and EMG similar to healthy adults, suggesting that individuals with hemiparesis perform a gait like movement when using the EBRGT. Lastly, a study was performed to determine if the EBRGT could improve gait parameters and function in ambulatory individuals with hemiparesis after stroke (chapter 5). Four participants walked on the EBRGT 3x/week for 4 or 8 weeks. After the intervention, all 4 participants increased their preferred gait speed. One participant had an improvement in gait speed that indicated functional gains. The results of this research suggest that the EBRGT can produce a gait pattern that has some similarities to level surface walking and that it is feasible for ambulatory individuals with hemiparesis to use the device. The device may also improve gait parameters in ambulatory individuals after stroke, but future studies with a control group need to be performed

High-density surface and intramuscular EMG recordings of the tibialis anterior muscle during isolated, dynamic contractions

Valid approaches for interfacing with and deciphering neural commands related to movement are critical to understanding muscular coordination and to developing viable prostheses and wearable robotics. While electromyography (EMG) has been an established approach for years, there is still a lack of adaptability to dynamic environments, due to a lack of data from dynamic movements. This report presents data consisting of simultaneously recorded high density surface EMG, intramuscular EMG, and joint dynamics from the tibialis anterior during static and dynamic muscle contractions. The dataset comes from seven subjects performing three to five trials each of different types of muscle contractions, both static (isometric) and dynamic (isotonic and isokinetic). Each subject was seated in an isokinetic dynamometer such that ankle movement was isolated and instrumented with four fine wire electrodes and a 126 electrode surface EMG grid. This data set can be used to i) validate methods for extracting neural signals from surface EMG, ii) develop models for predicting joint torque output, or iii) develop classifiers for human movement intent

High-density Surface and Intramuscular EMG Data from the Tibialis Anterior During Dynamic Contractions

Abstract Valid approaches for interfacing with and deciphering neural commands related to movement are critical to understanding muscular coordination and developing viable prostheses and wearable robotics. While electromyography (EMG) has been an established approach for mapping neural input to mechanical output, there is a lack of adaptability to dynamic environments due to a lack of data from dynamic movements. This report presents data consisting of simultaneously recorded high density surface EMG, intramuscular EMG, and joint dynamics from the tibialis anterior during static and dynamic muscle contractions. The dataset comes from seven subjects performing three to five trials each of different types of muscle contractions, both static (isometric) and dynamic (isotonic and isokinetic). Each subject was seated in an isokinetic dynamometer such that ankle movement was isolated and instrumented with four fine wire electrodes and a 126-electrode surface EMG grid. This data set can be used to (i) validate methods for extracting neural signals from surface EMG, (ii) develop models for predicting torque output, or (iii) develop classifiers for movement intent

Torque Estimation Using Neural Drive for a Concentric Contraction

© 2020 IEEE. The scope and relevance of wearable robotics spans across a number of research fields with a variety of applications. A challenge across these research areas is improving user-interface control. One established approach is using neural control interfaces derived from surface electromyography (sEMG). Although there has been some success with sEMG controlled prosthetics, the coarse nature of traditional sEMG processing has limited the development of fully functional prosthetics and wearable robotics. To solve this problem, blind source separation (BSS) techniques have been implemented to extract the user's movement intent from high-density sEMG (HDsEMG) measurements; however, current methods have only been well validated during static, low-level muscle contractions, and it is unclear how they will perform during movement. In this paper we present a neural drive based method for predicting output torque during a constant force, concentric contraction. This was achieved by modifying an existing HDsEMG decomposition algorithm to decompose 1 sec. overlapping windows. The neural drive profile was computed using both rate coding and kernel smoothing. Neither rate coding nor kernel smoothing performed as well as HDsEMG amplitude estimation, indicating that there are still significant limitations in adapting current methods to decompose dynamic contractions, and that sEMG amplitude estimation methods still remain highly reliable estimators

Carbon nanofiber-filled conductive silicone elastomers as soft, dry bioelectronic interfaces

<div><p>Soft and pliable conductive polymer composites hold promise for application as bioelectronic interfaces such as for electroencephalography (EEG). In clinical, laboratory, and real-world EEG there is a desire for dry, soft, and comfortable interfaces to the scalp that are capable of relaying the μV-level scalp potentials to signal processing electronics. A key challenge is that most material approaches are sensitive to deformation-induced shifts in electrical impedance associated with decreased signal-to-noise ratio. This is a particular concern in real-world environments where human motion is present. The entire set of brain information outside of tightly controlled laboratory or clinical settings are currently unobtainable due to this challenge. Here we explore the performance of an elastomeric material solution purposefully designed for dry, soft, comfortable scalp contact electrodes for EEG that is specifically targeted to have flat electrical impedance response to deformation to enable utilization in real world environments. A conductive carbon nanofiber filled polydimethylsiloxane (CNF-PDMS) elastomer was evaluated at three fill ratios (3, 4 and 7 volume percent). Electromechanical testing data is presented showing the influence of large compressive deformations on electrical impedance as well as the impact of filler loading on the elastomer stiffness. To evaluate usability for EEG, pre-recorded human EEG signals were replayed through the contact electrodes subjected to quasi-static compressive strains between zero and 35%. These tests show that conductive filler ratios well above the electrical percolation threshold are desirable in order to maximize signal-to-noise ratio and signal correlation with an ideal baseline. Increasing fill ratios yield increasingly flat electrical impedance response to large applied compressive deformations with a trade in increased material stiffness, and with nominal electrical impedance tunable over greater than 4 orders of magnitude. EEG performance was independent of filler loading above 4 vol % CNF (< 10³ ohms).</p></div

Compressive stress-strain curves for electrode filler loadings of 3, 4 and 7 vol % carbon nanofiber in PDMS.

<p>Compressive stress-strain curves for electrode filler loadings of 3, 4 and 7 vol % carbon nanofiber in PDMS.</p

Spectral power for conditions of eyes-open and eyes-closed for a sample subject using CNF-PDMS and standard Ag-AgCl electrodes.

<p>Note close correspondence with the typical peak in the alpha (8–14 Hz) band during eyes-closed conditions for both CNF-PDMS (black) and Ag-AgCl (red).</p

Single frequency (10Hz) electrical impedance performance over a single strain cycle (increasing/decreasing) for electrode filler loadings of 3, 4 and 7 vol % carbon nanofiber in PDMS.

<p>Single frequency (10Hz) electrical impedance performance over a single strain cycle (increasing/decreasing) for electrode filler loadings of 3, 4 and 7 vol % carbon nanofiber in PDMS.</p

Resistance as a function of CNF concentration in the CNF-PDMS composites.

<p>Resistance as a function of CNF concentration in the CNF-PDMS composites.</p