Emulator-Based Optimization of a Semi-Active Hip Exoskeleton Concept: Sweeping Impedance Across Walking Speeds.

OBJECTIVE: Semi-active exoskeletons combining lightweight, low powered actuators and passive-elastic elements are a promising approach to portable robotic assistance during locomotion. Here, we introduce a novel semi-active hip exoskeleton concept and evaluate human walking performance across a range of parameters using a tethered robotic testbed. METHODS: We emulated semi-active hip exoskeleton (exo) assistance by applying a virtual torsional spring with a fixed rotational stiffness and an equilibrium angle established in terminal swing phase (i.e., via pre-tension into stance). We performed a 2-D sweep of spring stiffness x equilibrium position parameters (30 combinations) across walking speed (1.0, 1.3, and 1.6 m/s) and measured metabolic rate to identify device parameters for optimal metabolic benefit. RESULTS: At each speed, optimal exoskeleton spring settings provided a ∼10% metabolic benefit compared to zero-impedance (ZI). Higher walking speeds required higher exoskeleton stiffness and lower equilibrium angle for maximal metabolic benefit. Optimal parameters tuned to each individual (user-dependent) provided significantly larger metabolic benefit than the average-best settings (user-independent) at all speeds except the fastest (p = 0.021, p = 0.001, and p = 0.098 at 1.0, 1.3, and 1.6 m/s, respectively). We found significant correlation between changes in user's muscle activity and changes in metabolic rate due to exoskeleton assistance, especially for muscles crossing the hip joint. CONCLUSION: A semi-active hip exoskeleton with spring-parameters personalized to each user could provide metabolic benefit across functional walking speeds. Minimizing muscle activity local to the exoskeleton is a promising approach for tuning assistance on-line on a user-dependent basis.

Neuromechanics and Energetics of Walking With an Ankle Exoskeleton Using Neuromuscular-Model Based Control: A Parameter Study.

Powered ankle exoskeletons that apply assistive torques with optimized timing and magnitude can reduce metabolic cost by ∼10% compared to normal walking. However, finding individualized optimal control parameters is time consuming and must be done independently for different walking modes (e.g., speeds, slopes). Thus, there is a need for exoskeleton controllers that are capable of continuously adapting torque assistance in concert with changing locomotor demands. One option is to use a biologically inspired, model-based control scheme that can capture the adaptive behavior of the human plantarflexors during natural gait. Here, based on previously demonstrated success in a powered ankle-foot prosthesis, we developed an ankle exoskeleton controller that uses a neuromuscular model (NMM) comprised of a Hill type musculotendon driven by a simple positive force feedback reflex loop. To examine the effects of NMM reflex parameter settings on (i) ankle exoskeleton mechanical performance and (ii) users' physiological response, we recruited nine healthy, young adults to walk on a treadmill at a fixed speed of 1.25 m/s while donning bilateral tethered robotic ankle exoskeletons. To quantify exoskeleton mechanics, we measured exoskeleton torque and power output across a range of NMM controller Gain (0.8-2.0) and Delay (10-40 ms) settings, as well as a High Gain/High Delay (2.0/40 ms) combination. To quantify users' physiological response, we compared joint kinematics and kinetics, ankle muscle electromyography and metabolic rate between powered and unpowered/zero-torque conditions. Increasing NMM controller reflex Gain caused increases in average ankle exoskeleton torque and net power output, while increasing NMM controller reflex Delay caused a decrease in net ankle exoskeleton power output. Despite systematic reduction in users' average biological ankle moment with exoskeleton mechanical assistance, we found no NMM controller Gain or Delay settings that yielded changes in metabolic rate. Post hoc analyses revealed weak association at best between exoskeleton and biological mechanics and changes in users' metabolic rate. Instead, changes in users' summed ankle joint muscle activity with powered assistance correlated with changes in their metabolic energy use, highlighting the potential to utilize muscle electromyography as a target for on-line optimization in next generation adaptive exoskeleton controllers.

A rat model for assessing the long-term safety and performance of peripheral nerve electrode arrays.

BACKGROUND: High-resolution peripheral nerve interfaces (PNIs) can provide amputees with intuitive motor control and sensory feedback. Current PNIs are limited by early device failure and suboptimal long-term stability. The present study aims to incorporate functional assessment into an in vivo test platform to assess the long-term safety and performance of PNIs for recording and stimulation. NEW METHODS: Utah electrode arrays (EA) were implanted in the rat sciatic nerve along with electromyography wires in the gastrocnemius and tibialis anterior. Cranial EEG screws were implanted in the somatosensory cortex for 12 weeks. Spontaneous neural activity was recorded using the implanted EA and stimulation-induced activity was monitored throughout the experiment. The impedance of each electrode was measured, and nerve function tests were conducted throughout the EA lifetime. Post-hoc safety assessments included scanning electron microscopy (SEM) of the EA and nerve histomorphometric analysis. RESULTS: EA recordings were stable, and stimulation with EA elicited somatosensory evoked potentials and muscle contractions. Motor and sensory function tests indicated minimal deficits. Histomorphometric analysis indicated changes in nerve microstructure. SEM indicated EA-tip fracture, while lead wire breakage primarily caused device failure. COMPARISON WITH EXISTING METHODS: We improved our prior platform with the addition of functional assessments of sensory pathways, a robust EMG array design to increase device longevity, and quantitative analysis of nerve microstructure. CONCLUSION: We present a test platform for long-term assessment of peripheral nerve interfaces for stimulation and recording. Using this platform, we demonstrate recording and stimulation with minimal impact on nerve function, while EA lead wire breakage and tip fracture could limit long-term device use.

Toward optical coherence tomography angiography-based biomarkers to assess the safety of peripheral nerve electrostimulation.

OBJECTIVE: Peripheral nerves serve as a link between the central nervous system and its targets. Altering peripheral nerve activity through targeted electrical stimulation is being investigated as a therapy for modulating end organ function. To support rapid advancement in the field, novel approaches to predict and prevent nerve injury resulting from electrical stimulation must be developed to overcome the limitations of traditional histological methods. The present study aims to develop an optical imaging-based approach for real-time assessment of peripheral nerve injury associated with electrical stimulation. APPROACH: We developed an optical coherence tomography (OCT) angiography system and a 3D printed stimulating nerve stabilizer (sNS) to assess the real-time microvascular and blood flow changes associated with electrical stimulation of peripheral nerves. We then compared the microvascular changes with established nerve function analysis and immunohistochemistry to correlate changes with nerve injury. MAIN RESULTS: Electrical stimulation of peripheral nerves has a direct influence on vessel diameter and capillary flow. The stimulation used in this study did not alter motor function significantly, but a delayed onset of mechanical allodynia at lower thresholds was observed using a sensory function test. Immunohistochemical analysis pointed to an increased number of macrophages within nerve fascicles and axon sprouting potentially related to nerve injury. SIGNIFICANCE: This study is the first to demonstrate the ability to image peripheral nerve microvasculature changes during electrical stimulation. This expands the knowledge in the field and can be used to develop potential biomarkers to predict nerve injury resulting from electrical stimulation.

Characterizing Longitudinal Changes in the Impedance Spectra of In-Vivo Peripheral Nerve Electrodes.

Characterizing the aging processes of electrodes in vivo is essential in order to elucidate the changes of the electrode⁻tissue interface and the device. However, commonly used impedance measurements at 1 kHz are insufficient for determining electrode viability, with measurements being prone to false positives. We implanted cohorts of five iridium oxide (IrOx) and six platinum (Pt) Utah arrays into the sciatic nerve of rats, and collected the electrochemical impedance spectroscopy (EIS) up to 12 weeks or until array failure. We developed a method to classify the shapes of the magnitude and phase spectra, and correlated the classifications to circuit models and electrochemical processes at the interface likely responsible. We found categories of EIS characteristic of iridium oxide tip metallization, platinum tip metallization, tip metal degradation, encapsulation degradation, and wire breakage in the lead. We also fitted the impedance spectra as features to a fine-Gaussian support vector machine (SVM) algorithm for both IrOx and Pt tipped arrays, with a prediction accuracy for categories of 95% and 99%, respectively. Together, this suggests that these simple and computationally efficient algorithms are sufficient to explain the majority of variance across a wide range of EIS data describing Utah arrays. These categories were assessed over time, providing insights into the degradation and failure mechanisms for both the electrode⁻tissue interface and wire bundle. Methods developed in this study will allow for a better understanding of how EIS can characterize the physical changes to electrodes in vivo.

Short-Time Fourier Transform Based Spike Detection of Spontaneous Peripheral Nerve Activity.

Peripheral nerve interfaces are designed to record neural activity from residual nerves in amputees. Reliable detection of neural events from these recordings dictate the performance of neuroprosthetic device control. Extraction of neural events from peripheral nerve recordings is challenging because of low signal to noise ratio (SNR), sparse spiking pattern and the presence of electromyographic signal contamination from the surrounding muscles. In this study, we developed a spike detection algorithm based on Short-time Fourier Transform (STFT) and compared its performance to simple thresholding technique using synthesized nerve recordings. To mimic peripheral nerve recordings and produce ground-truth for validation, a quasi-simulation framework is proposed to incrementally synthesize signals from physiological recordings. A detection threshold was optimized on the spectral features of simulated signals and performance evaluation was done using an independent simulated data set. Results show that the STFT based technique, compared to the simple thresholding, reduces the false detection rate even in recordings with moderately low SNR.

Verification of a near-field error sensor placement method in active control of compact noise sources.

Recent experiments in active noise control (ANC) have used near-field error sensors whose locations are determined according to the minimization of sound power. Sensors should be placed in regions where the sound pressure reductions are the greatest during sound power minimization of the ANC system. Near-field pressure measurements of noise sources with and without ANC were made. With the error sensors in theoretically ideal locations, the measured near-field pressure map approximates the theoretical map created under the condition of minimized radiated power. Moving the error sensors to theoretically nonideal locations greatly reduces the attenuation of radiated sound power.