Design of a sensor based data collection system for lower limb prosthetic gait analysis

The primary function of a lower limb prosthetic device is restoration of ambulation. Proper alignment – the correct spatial relationship between artificial sockets and the natural limb – is paramount to attain an efficient, comfortable gait with a desired loading pattern on the residual leg. Despite advances in prosthetic device design, the clinical alignment process remains subjective and nonsystematic due to a lack of an inexpensive, effective method for quantification of the amputee gait. Gait laboratories provide accurate data for gait monitoring; however cost and lab availability prohibit most patients from this benefit. Economic concerns aside, gait labs do not fill the void of information needed to quantify the alignment process. Observation time and environment are too limited to amass useful information for prosthetic alignment improvement. A more logical and systematic approach to clinical alignment requires the quantification of amputee gait before and after adjustments made by the prosthetist. To be complete this quantification must span extended periods of time and terrain. Thus, there is a patent need for a portable, reliable, and cost effective motion capture system. This project proposes a design for such a system. Comprised of body (prosthetic) mounted inertial sensors – accelerometers and gyroscopes – the system is designed to track the kinematics of the limbs during a walking cycle. The goal of this work is to prove the feasibility of motion capture system using these body mounted sensors. The effectiveness of the system will be judged as its ability to capture planar motion using two (2) accelerometers and one (1) gyroscope mounted on an aluminum bar (simulating a prosthetic device). The design of the system was formulated based on an extensive literature review pertaining to body mounted sensor systems. The rigid structure of the prostheses gives a prosthetic mounted sensor system a distinct advantage over a body mounted system in terms of inverse kinematic calculations

Editorial: Next Generation User-Adaptive Wearable Robots

Wearable robots, including powered exoskeletons and robotic prostheses, have created new possibilities for mobility augmentation and restoration among individuals with a variety of movement disorders, including spinal cord injury, stroke, amputation, and other neurological conditions (Esquenazi et al., 2017; Rodríguez-Fernández et al., 2021). Exoskeleton technology has progressed to create utility for unimpaired individuals by supporting load carriage, reducing joint loading or improving metabolic efficiency (Sawicki et al., 2020). Despite this progress, exoskeleton deployment in real-world, community environments remains limited. While there are multiple barriers to ubiquitous exoskeleton use, a key lynchpin is development of robust control systems that adapt to user intent, support the variety of mobility tasks that may be encountered, and account for variation in the user’s voluntary effort across such tasks

A Pediatric Knee Exoskeleton With Real-Time Adaptive Control for Overground Walking in Ambulatory Individuals With Cerebral Palsy

Gait training via a wearable device in children with cerebral palsy (CP) offers the potential to increase therapy dosage and intensity compared to current approaches. Here, we report the design and characterization of a pediatric knee exoskeleton (P.REX) with a microcontroller based multi-layered closed loop control system to provide individualized control capability. Exoskeleton performance was evaluated through benchtop and human subject testing. Step response tests show the averaged 90% rise was 26 ± 0.2 ms for 5 Nm, 22 ± 0.2 ms for 10 Nm, 32 ± 0.4 ms for 15 Nm. Torque bandwidth of P.REX was 12 Hz and output impedance was less than 1.8 Nm with control on (Zero mode). Three different control strategies can be deployed to apply assistance to knee extension: state-based assistance, impedance-based trajectory tracking, and real-time adaptive control. One participant with typical development (TD) and one participant with crouch gait from CP were recruited to evaluate P.REX in overground walking tests. Data from the participant with TD were used to validate control system performance. Kinematic and kinetic data were collected by motion capture and compared to exoskeleton on-board sensors to evaluate control system performance with results demonstrating that the control system functioned as intended. The data from the participant with CP are part of a larger ongoing study. Results for this participant compare walking with P.REX in two control modes: a state-based approach that provided constant knee extension assistance during early stance, mid-stance and late swing (Est+Mst+Lsw mode) and an Adaptive mode providing knee extension assistance proportional to estimated knee moment during stance. Both were well tolerated and significantly improved knee extension compared to walking without extension assistance (Zero mode). There was less reduction in gait speed during use of the adaptive controller, suggesting that it may be more intuitive than state-based constant assistance for this individual. Future work will investigate the effects of exoskeleton assistance during overground gait training in children with neurological disorders and will aim to identify the optimal individualized control strategy for exoskeleton prescription

Sitting and Standing Intention Can be Decoded from Scalp EEG Recorded Prior to Movement Execution

Low frequency signals recorded from non-invasive electroencephalography (EEG), in particular movement-related cortical potentials (MRPs), are associated with preparation and execution of movement and thus present a target for use in brain-machine interfaces. We investigated the ability to decode movement intent from delta-band (0.1-4 Hz) EEG recorded immediately before movement execution in healthy volunteers. We used data from epochs starting 1.5 sec before movement onset to classify future movements into one of three classes: stand-up, sit-down, or quiet. We assessed classification accuracy in both externally triggered and self-paced paradigms. Movement onset was determined from electromyography (EMG) recordings synchronized with EEG signals. We employed an artifact subspace reconstruction (ASR) algorithm to eliminate high amplitude noise before building our time-embedded EEG features. We applied local Fisher’s discriminant analysis to reduce the dimensionality of our spatio-temporal features and subsequently used a Gaussian mixture model classifier for our three class problem. Our results demonstrate significantly better than chance classification accuracy (chance level = 33.3%) for the self-initiated (78.0 ± 2.6%) and triggered (74.7 ± 5.7%) paradigms. Surprisingly, we found no significant difference in classification accuracy between the self-paced and cued paradigms when using the full set of non-peripheral electrodes. However, accuracy was significantly increased for self-paced movements when only electrodes over the primary motor area were used. Overall, this study demonstrates that delta-band EEG recorded immediately before movement carries discriminative information regarding movement type. Our results suggest that EEG-based classifiers could improve lower-limb neuroprostheses and neurorehabilitation techniques by providing earlier detection of movement intent, which could be used in robot-assisted strategies for motor training and recovery of function

Novel methods to enhance precision and reliability in muscle synergy identification during walking

Muscle synergies are hypothesized to reflect modular control of muscle groups via descending commands sent through multiple neural pathways. Recently, the number of synergies has been reported as a functionally relevant indicator of motor control complexity in individuals with neurological movement disorders. Yet the number of synergies extracted during a given activity, e.g. gait, varies within and across studies, even for unimpaired individuals. With no standardized methods for precise determination, this variability remains unexplained making comparisons across studies and cohorts difficult. Here, we utilize k-means clustering and intra-class and between-level correlation coefficients to precisely discriminate reliable from unreliable synergies. EMG was recorded bilaterally from eight leg muscles during treadmill walking at self-selected speed. Muscle synergies were extracted from 20 consecutive gait cycles using non-negative matrix factorization. We demonstrate that the number of synergies is highly dependent on the threshold when using the variance accounted for by reconstructed EMG. In contrast, our method utilized a quantitative metric to reliably identify four or five synergies underpinning walking in unimpaired adults and revealed synergies having poor reproducibility that should not be considered as true synergies. We show that robust and unreliable synergies emerge similarly, emphasizing the need for careful analysis in those with pathology

Prefrontal, posterior parietal and sensorimotor network activity underlying speed control during walking

Accumulating evidence suggests cortical circuits may contribute to control of human locomotion. Here, noninvasive electroencephalography (EEG) recorded from able-bodied volunteers during a novel treadmill walking paradigm was used to assess neural correlates of walking. A systematic processing method, including a recently developed subspace reconstruction algorithm, reduced movement-related EEG artifact prior to independent component analysis and dipole source localization. We quantified cortical activity while participants tracked slow and fast target speeds across two treadmill conditions: an active mode that adjusted belt speed based on user movements and a passive mode reflecting a typical treadmill. Our results reveal frequency specific, multi-focal task related changes in cortical oscillations elicited by active walking. Low γ band power, localized to the prefrontal and posterior parietal cortices, was significantly increased during double support and early swing phases, critical points in the gait cycle since the active controller adjusted speed based on pelvis position and swing foot velocity. These phasic γ band synchronizations provide evidence that prefrontal and posterior parietal networks, previously implicated in visuo-spatial and somotosensory integration, are engaged to enhance lower limb control during gait. Sustained μ and β band desynchronization within sensorimotor cortex, a neural correlate for movement, was observed during walking thereby validating our methods for isolating cortical activity. Our results also demonstrate the utility of EEG recorded during locomotion for probing the multi-regional cortical networks which underpin its execution. For example, the cortical network engagement elicited by the active treadmill suggests that it may enhance neuroplasticity for more effective motor training