Characterizing Bodyweight-Supported Treadmill Walking on Land and Underwater Using Foot-Worn Inertial Measurement Units and Machine Learning for Gait Event Detection.

Gait rehabilitation commonly relies on bodyweight unloading mechanisms, such as overhead mechanical support and underwater buoyancy. Lightweight and wireless inertial measurement unit (IMU) sensors provide a cost-effective tool for quantifying body segment motions without the need for video recordings or ground reaction force measures. Identifying the instant when the foot contacts and leaves the ground from IMU data can be challenging, often requiring scrupulous parameter selection and researcher supervision. We aimed to assess the use of machine learning methods for gait event detection based on features from foot segment rotational velocity using foot-worn IMU sensors during bodyweight-supported treadmill walking on land and underwater. Twelve healthy subjects completed on-land treadmill walking with overhead mechanical bodyweight support, and three subjects completed underwater treadmill walking. We placed IMU sensors on the foot and recorded motion capture and ground reaction force data on land and recorded IMU sensor data from wireless foot pressure insoles underwater. To detect gait events based on IMU data features, we used random forest machine learning classification. We achieved high gait event detection accuracy (95-96%) during on-land bodyweight-supported treadmill walking across a range of gait speeds and bodyweight support levels. Due to biomechanical changes during underwater treadmill walking compared to on land, accurate underwater gait event detection required specific underwater training data. Using single-axis IMU data and machine learning classification, we were able to effectively identify gait events during bodyweight-supported treadmill walking on land and underwater. Robust and automated gait event detection methods can enable advances in gait rehabilitation.

Quantification of Global Myoelectric Spatial Activations to Delineate Normal Hamstring Function at Progressive Running Speeds: A Technical Report.

ABSTRACT: Schlink, BR, Nordin, AD, Diekfuss, JA, and Myer, GD. Quantification of global myoelectric spatial activations to delineate normal hamstring function at progressive running speeds: A technical report. J Strength Cond Res 36(3): 867-870, 2022-Hamstring function is critical to maintain sport performance, and strain injuries to the biceps femoris muscle commonly force an athlete to withdraw from their sport while the muscle heals. Current mechanistic understanding of underlying injury and return-to-play (RTP) guidelines has limited prognostic value because of limitations in technology and nonfunctional assessment strategies to guide clinical care. Integrated structural and functional determinants and dynamic assessment methods are needed to guide advanced rehabilitation strategies for safe and rapid return to sport. A potential solution for assessment of hamstring function is high-density electromyography (EMG), which can noninvasively measure spatial muscle activity in dynamic environments. In this study, we demonstrated the utility of high-density EMG by measuring spatial myoelectric activity from the biceps femoris from a group of recreational athletes running at a range of speeds. The level of significance set for this study was p < 0.05. During the late swing phase of running, we observed increased EMG amplitudes in the central and distal portions of the muscle. There were no changes in this pattern of EMG activation across speed, suggesting that running speed does not affect the general neuromuscular recruitment in the biceps femoris. Applying these methods to athletes with hamstring strains may lead to a more complete understanding of muscle function during rehabilitation and adjunctively support current methods to enhance RTP decision-making.

Mobile Electroencephalography for Studying Neural Control of Human Locomotion.

Walking or running in real-world environments requires dynamic multisensory processing within the brain. Studying supraspinal neural pathways during human locomotion provides opportunities to better understand complex neural circuity that may become compromised due to aging, neurological disorder, or disease. Knowledge gained from studies examining human electrical brain dynamics during gait can also lay foundations for developing locomotor neurotechnologies for rehabilitation or human performance. Technical barriers have largely prohibited neuroimaging during gait, but the portability and precise temporal resolution of non-invasive electroencephalography (EEG) have expanded human neuromotor research into increasingly dynamic tasks. In this narrative mini-review, we provide a (1) brief introduction and overview of modern neuroimaging technologies and then identify considerations for (2) mobile EEG hardware, (3) and data processing, (4) including technical challenges and possible solutions. Finally, we summarize (5) knowledge gained from human locomotor control studies that have used mobile EEG, and (6) discuss future directions for real-world neuroimaging research.

Fatigue induces altered spatial myoelectric activation patterns in the medial gastrocnemius during locomotion.

This research investigates the effects of muscle fatigue on spatial myoelectric patterns in the lower limb during locomotion. Both spatial and frequency aspects of neuromuscular recruitment in the medial gastrocnemius change in response to fatigue, resulting in altered myoelectric patterns during walking and running. These data may help us better understand the adaptations that occur in lower limb muscles to avoid overuse injuries caused by fatigue.

Human myoelectric spatial patterns differ among lower limb muscles and locomotion speeds.

The spatial distribution of myoelectric activity within lower limb muscles is often nonuniform and can change during different stationary tasks. Recent studies using high-density electromyography (EMG) have suggested that spatial muscle activity may also differ among muscles during locomotion, but contrasting electrode array sizes and experimental designs have limited cross-study comparisons. Here, we sought to determine if spatial EMG patterns differ among lower limb muscles and locomotion speeds. We recorded high-density EMG from the vastus medialis, tibialis anterior, biceps femoris, medial gastrocnemius, and lateral gastrocnemius muscles of 11 healthy subjects while they walked (1.2 and 1.6 m/s) and ran (2.0, 3.0, 4.0, and 5.0 m/s) on a treadmill. To overcome the detrimental effects of cable, electrode, and soft tissue movements on high-density EMG signal quality during locomotion, we applied multivariate signal cleaning methods. From these data, we computed the spatial entropy and center of gravity from the total myoelectric activity within each recording array during the stance or swing phases of the gait cycle. We found heterogeneous spatial EMG patterns evidenced by contrasting spatial entropy among lower limb muscles. As locomotion speed increased, mean entropy values decreased in four of the five recorded muscles, indicating that EMG signal amplitudes were more spatially heterogeneous, or localized, at faster speeds. The EMG center of gravity location also shifted in multiple muscles as locomotion speed increased. Contrasting myoelectric spatial distributions among muscles likely reflect differences in muscle architecture, but increasingly localized activity and spatial shifts in the center of gravity location at faster locomotion speeds could be influenced by preferential recruitment of faster motor units under greater loads.

Motion and Muscle Artifact Removal Validation Using an Electrical Head Phantom, Robotic Motion Platform, and Dual Layer Mobile EEG.

Motion and muscle artifacts can undermine signal quality in electroencephalography (EEG) recordings during locomotion. We evaluated approaches for recovering ground-truth artificial brain signals from noisy EEG recordings. We built an electrical head phantom that broadcast four brain and four muscle sources. Head movements were generated by a robotic motion platform. We recorded 128-channel dual layer EEG and 8-channel neck electromyography (EMG) from the head phantom during motion. We evaluated ground-truth electrocortical source signal recovery from artifact contaminated data using Independent Component Analysis (ICA) to determine: (1) the number of isolated noise sensor recordings needed to capture and remove motion artifacts, (2) the ability of Artifact Subspace Reconstruction to remove motion and muscle artifacts at contrasting artifact detection thresholds, (3) the number of neck EMG sensor recordings needed to capture and remove muscle artifacts, and (4) the ability of Canonical Correlation Analysis to remove muscle artifacts. We also evaluated source signal recovery by combining the best practices identified in aims 1-4. By including isolated noise and EMG recordings in the ICA decomposition, we more effectively recovered ground-truth artificial brain signals. A reduced subset of 32-noise and 6-EMG channels showed equivalent performance compared to including the complete arrays. Artifact Subspace Reconstruction improved source separation, but this was contingent on muscle activity amplitude. Canonical Correlation Analysis also improved source separation. Merging noise and EMG recordings into the ICA decomposition, with Artifact Subspace Reconstruction and Canonical Correlation Analysis preprocessing, improved source signal recovery. This study expands on previous head phantom experiments by including neck muscle source activity and evaluating artificial electrocortical spectral power fluctuations synchronized with gait events.

Footwear and footstrike change loading patterns in running.

Loading rates have been linked to running injuries, revealing persistent impact features that change direction among three-dimensional axes in different footwear and footstrike patterns. Extracting peak loads from ground reaction forces, however, can neglect the time-varying loading patterns experienced by the runner in each footfall. Following footwear and footstrike manipulations during laboratory-based overground running, we examined three-dimensional loading rate-time features in each direction (X, Y, Z) using principal component analysis. Twenty participants (9 M, 11 F, age: 25.3 ± 3.6 y) were analysed during 14 running trials in each of two footwear (cushioned and minimalist) and three footstrike conditions (forefoot, midfoot, rearfoot). Two principal components (PC) captured the primary loading rate-time features (PC1: 42.5% and PC2: 22.8% explained variance) and revealed interaction among axes, footwear, and footstrike conditions (PC1: F (2.1, 40.1) = 5.6, p = 0.007, η 2 = 0.23; PC2: F (2.0, 38.4) = 62.3, p < 0.001, η 2 = 0.77). Rearfoot running in cushioned footwear attenuated impact loads in the vertical direction, and forefoot running in minimalist footwear attenuated impact loads in the anterior-posterior and medial-lateral directions relative to forefoot running in cushioned shoes. Loading patterns depend on footwear and footstrike interactions, which require shoes that match the runner's footstrike pattern.

Comparison of Signal Processing Methods for Reducing Motion Artifacts in High-Density Electromyography During Human Locomotion.

Objective: High-density electromyography (EMG) is useful for studying changes in myoelectric activity within a muscle during human movement, but it is prone to motion artifacts during locomotion. We compared canonical correlation analysis and principal component analysis methods for signal decomposition and component filtering with a traditional EMG high-pass filtering approach to quantify their relative performance at removing motion artifacts from high-density EMG of the gastrocnemius and tibialis anterior muscles during human walking and running. Results: Canonical correlation analysis filtering provided a greater reduction in signal content at frequency bands associated with motion artifacts than either traditional high-pass filtering or principal component analysis filtering. Canonical correlation analysis filtering also minimized signal reduction at frequency bands expected to consist of true myoelectric signal. Conclusions: Canonical correlation analysis filtering appears to outperform a standard high-pass filter and principal component analysis filter in cleaning high-density EMG collected during fast walking or running.

Faster Gait Speeds Reduce Alpha and Beta EEG Spectral Power From Human Sensorimotor Cortex.

OBJECTIVE: Our aim was to determine if walking speed affected human sensorimotor electrocortical dynamics using mobile high-density electroencephalography (EEG). METHODS: To overcome limitations associated with motion and muscle artifact contamination in EEG recordings, we compared solutions for artifact removal using novel dual-layer EEG electrodes and alternative signal processing methods. Dual-layer EEG simultaneously recorded human electrocortical signals and isolated motion artifacts using pairs of mechanically coupled and electrically independent electrodes. For electrical muscle activity removal, we incorporated electromyographic (EMG) recordings from the neck into our mobile EEG data processing pipeline. We compared artifact removal methods during treadmill walking at four speeds (0.5, 1.0, 1.5, and 2.0 m/s). RESULTS: Left and right sensorimotor alpha and beta spectral power increased in contralateral limb single support and push off, and decreased during contralateral limb swing at each speed. At faster walking speeds, sensorimotor spectral power fluctuations were less pronounced across the gait cycle with reduced alpha and beta power (p < 0.05) compared to slower speeds. Isolated noise recordings and neck EMG spectral power fluctuations matched gait events and showed broadband spectral power increases at faster speeds. CONCLUSION AND SIGNIFICANCE: Dual-layer EEG enabled us to isolate changes in human sensorimotor electrocortical dynamics across walking speeds. A comparison of signal processing approaches revealed similar intrastride cortical fluctuations when applying common (e.g., artifact subspace reconstruction) and novel artifact rejection methods. Dual-layer EEG, however, allowed us to document and rule out residual artifacts, which exposed sensorimotor spectral power changes across gait speeds.

Human electrocortical dynamics while stepping over obstacles.

To better understand human brain dynamics during visually guided locomotion, we developed a method of removing motion artifacts from mobile electroencephalography (EEG) and studied human subjects walking and running over obstacles on a treadmill. We constructed a novel dual-layer EEG electrode system to isolate electrocortical signals, and then validated the system using an electrical head phantom and robotic motion platform. We collected data from young healthy subjects walking and running on a treadmill while they encountered unexpected obstacles to step over. Supplementary motor area and premotor cortex had spectral power increases within ~200 ms after object appearance in delta, theta, and alpha frequency bands (3-13 Hz). That activity was followed by similar posterior parietal cortex spectral power increase that decreased in lag time with increasing locomotion speed. The sequence of activation suggests that supplementary motor area and premotor cortex interrupted the gait cycle, while posterior parietal cortex tracked obstacle location for planning foot placement nearly two steps ahead of reaching the obstacle. Together, these results highlight advantages of adopting dual-layer mobile EEG, which should greatly facilitate the study of human brain dynamics in physically active real-world settings and tasks.

Reviewing the Variability-Overuse Injury Hypothesis: Does Movement Variability Relate to Landing Injuries?

PURPOSE: Overuse injuries are common in sport, but complete understanding of injury risk factors remains incomplete. Although biomechanical studies frequently examine musculoskeletal injury mechanisms, human movement variability studies aim to better understand neuromotor functioning, with proposed connections between overuse injury mechanisms and changes in motor variability. METHOD: In a narrative review, we discuss the variability-overuse injury hypothesis, which suggests repeated load application leads to mechanical tissue breakdown and subsequent injury when exceeding the rate of physiological adaptation. Due to the multidisciplinary nature of this hypothesis, we incorporate concepts from motor control, neurophysiology, biomechanics, as well as research design and data analysis. We therefore summarize multiple perspectives while proposing theoretical relationships between movement variability and lower extremity overuse injuries. RESULTS: Experimental data are presented and summarized from published experiments examining interactions between experimental task demands and movement variability in the context of drop landing movements, along with comparisons to previous movement variability studies. CONCLUSION: We provide a conceptual framework for sports medicine researchers interested in predicting and preventing sports injuries. Under performance conditions with greater task demands, we predict reduced trial-to-trial movement variability that could increase the likelihood of overuse injuries.

Dual-electrode motion artifact cancellation for mobile electroencephalography.

OBJECTIVE: Our purpose was to evaluate the ability of a dual electrode approach to remove motion artifact from electroencephalography (EEG) measurements. APPROACH: We used a phantom human head model and robotic motion platform to induce motion while collecting scalp EEG. We assembled a dual electrode array capturing (a) artificial neural signals plus noise from scalp EEG electrodes, and (b) electrically isolated motion artifact noise. We recorded artificial neural signals broadcast from antennae in the phantom head during continuous vertical sinusoidal movements (stationary, 1.00, 1.25, 1.50, 1.75, 2.00 Hz movement frequencies). We evaluated signal quality using signal-to-noise ratio (SNR), cross-correlation, and root mean square error (RMSE) between the ground truth broadcast signals and the recovered EEG signals. MAIN RESULTS: Signal quality was restored following noise cancellation when compared to single electrode EEG measurements collected with no phantom head motion. SIGNIFICANCE: We achieved substantial motion artifact attenuation using secondary electrodes for noise cancellation. These methods can be applied to studying electrocortical signals during human locomotion to improve real-world neuroimaging using EEG.

Effects of Cable Sway, Electrode Surface Area, and Electrode Mass on Electroencephalography Signal Quality during Motion.

More neuroscience researchers are using scalp electroencephalography (EEG) to measure electrocortical dynamics during human locomotion and other types of movement. Motion artifacts corrupt the EEG and mask underlying neural signals of interest. The cause of motion artifacts in EEG is often attributed to electrode motion relative to the skin, but few studies have examined EEG signals under head motion. In the current study, we tested how motion artifacts are affected by the overall mass and surface area of commercially available electrodes, as well as how cable sway contributes to motion artifacts. To provide a ground-truth signal, we used a gelatin head phantom with embedded antennas broadcasting electrical signals, and recorded EEG with a commercially available electrode system. A robotic platform moved the phantom head through sinusoidal displacements at different frequencies (0-2 Hz). Results showed that a larger electrode surface area can have a small but significant effect on improving EEG signal quality during motion and that cable sway is a major contributor to motion artifacts. These results have implications in the development of future hardware for mobile brain imaging with EEG.

Biomechanics and neural control of movement, 20 years later: what have we learned and what has changed?

We summarize content from the opening thematic session of the 20th anniversary meeting for Biomechanics and Neural Control of Movement (BANCOM). Scientific discoveries from the past 20 years of research are covered, highlighting the impacts of rapid technological, computational, and financial growth on motor control research. We discuss spinal-level communication mechanisms, relationships between muscle structure and function, and direct cortical movement representations that can be decoded in the control of neuroprostheses. In addition to summarizing the rich scientific ideas shared during the session, we reflect on research infrastructure and capacity that contributed to progress in the field, and outline unresolved issues and remaining open questions.

Load Accommodation Strategies and Movement Variability in Single-Leg Landing.

Our purpose was to examine changes in participant-specific single-leg landing strategies and intra-individual movement variability following alterations in mechanical task demands via external load and landing height. Nineteen healthy volunteers (15M, 4 F, age: 24.3 ± 4.9 y, mass: 78.5 ± 14.7 kg, height: 1.73 ± 0.08 m) were analyzed among 9 single-leg drop landing trials in each of 6 experimental conditions (3 load and 2 landing height) computed as percentages of participant bodyweight (BW, BW + 12.5%, BW + 25%) and height (H12.5% & H25%). Lower-extremity sagittal joint angles and moments (hip, knee, and ankle), vertical ground reaction forces (GRFz), and electrical muscle activities (gluteus maximus, biceps femoris, vastus medialis, medial gastrocnemius, and tibialis anterior muscles) were analyzed. Individual single-leg drop landing strategies were identified using landing impulse predictions and the Load Accommodation Strategies Model (James et al., 2014). Intra-individual movement variability was assessed from neuromechanical synergies extracted using single-case principal component analyses (PCA). Fewer contrasting single-leg landing strategies were identified among participants under greater mechanical task demands (p < .001) alongside lesser intra-individual movement variability (p < .001). These results reveal changes in movement control under greater mechanical task demands, which may have implications for understanding overuse injury mechanisms in landing.

Lower extremity variability changes with drop-landing height manipulations.

Landing is a common lower extremity injury mechanism in sport, with potential connections to movement control accessed through variability measures. We investigated intra-subject lower extremity variability changes following drop-landing height manipulations using standard deviation (SD) and coefficient of variation (CV) among lower extremity peak sagittal joint angles and moments. Fourteen healthy participants completed five drop-landing trials from five heights 20%, 60%, 100%, 140% and 180% maximum vertical jump height (MVJH). Peak joint angles and moments increased with greater landing height (p < 0.001), highlighting inter-joint differences (Flexion: Knee > Hip > Ankle, p < 0.001; Extensor Moment: Hip > Knee > Ankle, in excess of 60% MVJH, p < 0.05). Kinematic and kinetic SD increased with variable magnitudes, while CV decreased at greater landing heights (p ≤ 0.016). Decreased relative variability under greater task demands may underscore non-contact injury mechanisms from repetitive loading of identical structures.

Classifying performer strategies in drop landing activities.

Our purpose was to use group and single-case methods to examine inter-individual variability in the context of factors related to landing injuries. We tested the load accommodation strategies model (An exploration of load accommodation strategies during walking with extremity-carried weights. Human Movement Science, 35, 17-29) using landing impulse, revealing pre-landing strategies following height and external load manipulations. Ten healthy volunteers (8 male, 2 female, 24.0 ± 1.4 years, 1.72 ± 0.06 m, 73.5 ± 8.7 kg) were analysed across 12 trials in each of three load conditions (100% body weight [BW], 110% BW, 120% BW) from two landing heights (30 cm, 60 cm). Landing impulse (BW ∙ s) was computed for each participant-condition-trial, using impulse ratios (unit-less; BW ∙ s/BW ∙ s) to evaluate load accommodation strategies between adjacent load conditions (110%/100%, 120%/110%) at each landing height. Load accommodation strategy classifications were based on 95% confidence intervals (CIs) containing mechanically predicted impulse ratios (1.10 and 1.09 for 110/100% BW and 120/110% BW, respectively; α = 0.05). Mean group impulse ratios matched and exceeded predicted impulse ratios. Single-case analyses revealed a range of individual landing strategies that might be overlooked during group analyses, possibly uncovering individuals at greater risk of injury during landing activities.

Three-dimensional impact kinetics with foot-strike manipulations during running.

BACKGROUND: Lack of an observable vertical impact peak in fore/mid-foot running has been suggested as a means of reducing lower extremity impact forces, although it is unclear if impact characteristics exist in other axes. The purpose of the investigation was to compare three-dimensional (3D) impact kinetics among foot-strike conditions in over-ground running using instantaneous loading rate-time profiles. METHODS: Impact characteristics were assessed by identifying peak loading rates in each direction (medial-lateral (ML), anterior-posterior (AP), vertical, and 3D resultant) following foot-strike instructions (fore-foot, mid-foot, subtle heel, and obvious heel strike). Kinematic and kinetic data were analyzed among 9 male participants in each foot-strike condition. RESULTS: Loading rate peaks were observed in each direction and foot-strike condition, differing in magnitude by direction (3D resultant and vertical > AP > ML, p ≤ 0.031) and foot-strike: ML (fore-foot and mid-foot strike > obvious heel strike, p ≤ 0.032), AP (fore-foot and mid-foot strikes > subtle-heel and obvious heel strikes, p ≤ 0.023). In each direction, the first loading rate peak occurred later during heel strike running relative to fore-foot (p ≤ 0.019), with vertical and 3D resultant impact durations exceeding shear (ML and AP, p ≤ 0.007) in each condition. CONCLUSION: Loading rate-time assessment identified contrasting impact characteristics in each direction and the 3D resultant following foot-strike manipulations, with potential implications for lower extremity structures in running.

Single-leg landing neuromechanical data following load and land height manipulations.

Lower extremity sagittal kinematic and kinetic data are summarized alongside electrical muscle activities during single-leg landing trials completed in contrasting external load and landing height conditions. Nineteen subjects were analyzed during 9 landing trials in each of 6 experimental conditions computed as percentages of subject anthropometrics (bodyweight: BW and subject height: H; BW, BW+12.5%, BW+25%, and H12.5%, H25%). Twelve lower extremity variables (sagittal hip, knee, ankle angles and moments, vertical ground reaction force (GRFz), gluteus maximus, biceps femoris, vastus medials, medial gastrocnemius, and tibialis anterior muscles) were assessed using separate principal component analyses (PCA). Variable trends across conditions were summarized in "Neuromechanical synergies in single-leg landing reveal changes in movement control. Human Movement Science" (Nordin and Dufek, 2016) [1], revealing changes in landing biomechanics and movement control.

Neuromechanical synergies in single-leg landing reveal changes in movement control.

Our purpose was to examine changes in single-leg landing biomechanics and movement control following alterations in mechanical task demands via external load and landing height. We examined lower-extremity kinematic, kinetic, and electromyographic (EMG) adjustments, as well as changes in movement control from neuromechanical synergies using separate principal component analyses (PCA). Nineteen healthy volunteers (15M, 4F, age: 24.3±4.9y, mass: 78.5±14.7kg, height: 1.73±0.08m) were analyzed among 9 single-leg drop landing trials in each of 6 experimental conditions (3 load and 2 landing height) computed as percentages of subject bodyweight (BW, BW+12.5%, BW+25%) and height (H12.5% & H25%). Condition order was counterbalanced, including: 1.) BW·H12.5, 2.) BW+12.5·H12.5, 3.) BW+25·H12.5, 4.) BW·H25, 5.) BW+12.5·H25, 6.) BW+25·H25. Lower-extremity sagittal joint angles and moments (hip, knee, & ankle), vertical ground reaction force (GRFz), and electrical muscle activity (gluteus maximus, biceps femoris, vastus medialis, medial gastrocnemius, & tibialis anterior muscles), were analyzed in each trial. Biomechanical adjustments and neuromechanical synergies were assessed using PCA. Subjects reduced effective landing height through segmental configuration adjustments at ground contact, extending at the hip and ankle joints with greater load and landing height (p⩽0.028 and p⩽0.013, respectively), while using greater medial gastrocnemius pre-activation with greater load (p⩽0.006). Dimension reduction was observed under greater mechanical task demands, compressing and restructuring synergies among patterns of muscle activation, applied loads, and segmental configurations. These results provide insight into movement control and potential injury mechanisms in landing activities.