Ambulatory Tasks Research Articles

Extended ambulatory assessment of executive function: within-person reliability of working memory and inhibitory control tasks

ABSTRACT Introduction Ambulatory assessment of executive function – particularly in the form working memory (WM) – is increasingly common. Few studies to date, however, have also incorporated ambulatory measures of inhibitory control. Critically, the extended within-person reliability of ambulatory tasks tapping each of these constructs has been largely overlooked. Method Participants (N = 283, M age = 23.74 years, SD = 9.04) received notifications every 3 days (for 4 weeks) to undertake ambulatory assessment versions of the n-Back and Stop-Signal Tasks (SST) via the smartphone application CheckCog. Within-person reliability of these measures was explored. Results Compliance ranged from 66% (for eight sessions) to 89% (for four sessions). Our results reveal significant changes in performance within the first two sessions for both the n-Back and SST, with performance remaining largely consistent across the remaining (two to eight) sessions. In terms of test–retest reliability, the ICC (C, 1) values ranged from .29 to .68 on the n-Back (with overall accuracy being .51) and .31–.73 on the SST (with stop-signal reaction time being .53). Conclusion The results of the current study contribute to the literature by demonstrating the reliability of brief measures of executive function – in the form of inhibitory control and WM – delivered using smartphones in participants’ natural environments. Based on our findings, the CheckCog app reliability tracks baseline systematic changes in WM and response inhibition across multiple time points and for an extended period in healthy individuals.

Load carriage changes tibiofemoral arthrokinematics during ambulatory tasks in recruit-aged women

The introduction of women into U.S. military ground close combat roles requires research into sex-specific effects of military training and operational activities. Knee osteoarthritis is prevalent among military service members; its progression has been linked to occupational tasks such as load carriage. Analyzing tibiofemoral arthrokinematics during load carriage is important to understand potentially injurious motion and osteoarthritis progression. The study purpose was to identify effects of load carriage on knee arthrokinematics during walking and running in recruit-aged women. Twelve healthy recruit-aged women walked and ran while unloaded (bodyweight [BW]) and carrying additional + 25%BW and + 45%BW. Using dynamic biplane radiography and subject-specific bone models, tibiofemoral arthrokinematics, subchondral joint space and center of closest contact location between subchondral bone surfaces were analyzed over 0–30% stance (separate one-way repeated measures analysis of variance, load by locomotion). While walking, medial compartment contact location was 5% (~ 1.6 mm) more medial for BW than + 45%BW at foot strike (p = 0.03). While running, medial compartment contact location was 4% (~ 1.3 mm) more lateral during BW than + 25%BW at 30% stance (p = 0.04). Internal rotation was greater at + 45%BW compared to + 25%BW (p < 0.01) at 30% stance. Carried load affects tibiofemoral arthrokinematics in recruit-aged women. Prolonged load carriage could increase the risk of degenerative joint injury in physically active women.

Continuous A-Mode Ultrasound-Based Prediction of Transfemoral Amputee Prosthesis Kinematics Across Different Ambulation Tasks.

Volitional control systems for powered prostheses require the detection of user intent to operate in real life scenarios. Ambulation mode classification has been proposed to address this issue. However, these approaches introduce discrete labels to the otherwise continuous task that is ambulation. An alternative approach is to provide users with direct, voluntary control of the powered prosthesis motion. Surface electromyography (EMG) sensors have been proposed for this task, but poor signal-to-noise ratios and crosstalk from neighboring muscles limit performance. B-mode ultrasound can address some of these issues at the cost of reduced clinical viability due to the substantial increase in size, weight, and cost. Thus, there is an unmet need for a lightweight, portable neural system that can effectively detect the movement intention of individuals with lower-limb amputation. In this study, we show that a small and lightweight A-mode ultrasound system can continuously predict prosthesis joint kinematics in seven individuals with transfemoral amputation across different ambulation tasks. Features from the A-mode ultrasound signals were mapped to the user's prosthesis kinematics via an artificial neural network. Predictions on testing ambulation circuit trials resulted in a mean normalized RMSE across different ambulation modes of 8.7 ± 3.1%, 4.6 ± 2.5%, 7.2 ± 1.8%, and 4.6 ± 2.4% for knee position, knee velocity, ankle position, and ankle velocity, respectively. This study lays the foundation for future applications of A-mode ultrasound for volitional control of powered prostheses during a variety of daily ambulation tasks.

The Impact of the Gestational Period and Weight on Individual, Task and Environmental Constraints as Related to Perceived Confidence when Completing Ambulatory Tasks in Pregnant Women

Background: Pregnancy and the physical, sensory, perceptual, and motor changes that coincide with pregnancy can affect a person’s ability to perform basic activities of daily living (ADL’s) that involve ambulation. Previous research suggested that weight and gestational age (trimester) are factors that impact woman’s ability to ambulate through daily tasks, however, these issues require further investigation. Newell's model of constraints was used as a framework to shape the questionnaire investigating the ability to perform different ambulatory activities across various constraints. Thus, the purpose of this study was to examine whether variables such as pre-pregnancy weight (under vs. over 155 pounds) and trimester (before vs. after 26 weeks) had an impact on the ability and perception to perform different tasks requiring ambulation. Methods: Twenty pregnant or postpartum women (M = 29.7 years; SD = 3.6) were recruited and asked to complete a 15-item online survey exploring their abilities to complete a variety of daily tasks involving balance. Also, open ended questions were posed to gain qualitative insight into their experiences. A series of independent samples T-tests were used to analyse the data. Findings: The results revealed predominantly no statistical differences between the levels of the variables manipulated. At the descriptive level the women indicated that they were relatively comfortable performing the different tasks. However, the qualitative responses suggested that although they were capable, they expended a substantial amount of energy to avoid falling, resulting in fatigue and soreness. Also, they experienced overall mental anguish and feelings of being overwhelmed when performing even simple tasks involving gait. Conclusion: Overall, it was surprising that weight and gestational age, which intuitively should have an impact, did not have a more pronounced effect on the women’s confidence in performing ambulation tasks. Nevertheless, qualitative responses confirmed the challenging effects of pregnancy on the way women engage in activities of daily living.

Plantar somatosensory restoration enhances gait, speed perception, and motor adaptation.

Lower limb loss is a major insult to the body's nervous and musculoskeletal systems. Despite technological advances in prosthesis design, artificial limbs are not yet integrated into the body's physiological systems. Therefore, lower limb amputees (LLAs) experience lower balance confidence, higher fear of falls, and impaired gait compared with their able-bodied peers (ABs). Previous studies have demonstrated that restored sensations perceived as originating directly from the missing limb via neural interfaces improve balance and performance in certain ambulatory tasks; however, the effects of such evoked sensations on neural circuitries involved in the locomotor activity are not well understood. In this work, we investigated the effects of plantar sensation elicited by peripheral nerve stimulation delivered by multicontact nerve cuff electrodes on gait symmetry and stability, speed perception, and motor adaptation. We found that restored plantar sensation increased stance time and propulsive force on the prosthetic side, improved gait symmetry, and yielded an enhanced perception of prosthetic limb movement. Our results show that the locomotor adaptation among LLAs with plantar sensation became similar to that of ABs. These findings suggest that our peripheral nerve-based approach to elicit plantar sensation directly affects central nervous pathways involved in locomotion and motor adaptation during walking. Our neuroprosthesis provided a unique model to investigate the role of somatosensation in the lower limb during walking and its effects on perceptual recalibration after a locomotor adaptation task. Furthermore, we demonstrated how plantar sensation in LLAs could effectively increase mobility, improve walking dynamics, and possibly reduce fall risks.

Improving Amputee Endurance over Activities of Daily Living with a Robotic Knee-Ankle Prosthesis: A Case Study.

Robotic knee-ankle prostheses have often fallen short relative to passive microprocessor prostheses in time-based clinical outcome tests. User ambulation endurance is an alternative clinical outcome metric that may better highlight the benefits of robotic prostheses. However, previous studies were unable to show endurance benefits due to inaccurate high-level classification, discretized mid-level control, and insufficiently difficult ambulation tasks. In this case study, we present a phase-based mid-level prosthesis controller which yields biomimetic joint kinematics and kinetics that adjust to suit a continuum of tasks. We enrolled an individual with an above-knee amputation and challenged him to perform repeated, rapid laps of a circuit comprising activities of daily living with both his passive prosthesis and a robotic prosthesis. The participant demonstrated improved endurance with the robotic prosthesis and our mid-level controller compared to his passive prosthesis, completing over twice as many total laps before fatigue and muscle discomfort required him to stop. We also show that time-based outcome metrics fail to capture this endurance improvement, suggesting that alternative metrics related to endurance and fatigue may better highlight the clinical benefits of robotic prostheses.

Changes in Navigation Controls and Field-of-View Modes Affect Cybersickness Severity and Spatiotemporal Gait Patterns After Exposure to Virtual Environments.

To examine the effects of navigation controls and field-of-view modes on cybersickness severity and gait dynamics after cessation of exposure to a virtual environment (VE). The applications of virtual reality are increasing in various fields; however, whether changes in interaction techniques and visual contents could mitigate the potential gait disturbance following VE exposure remains unclear. Thirty healthy adults wore a head-mounted display to complete six sessions of 12-min run-and-gun tasks using different navigation controls (gamepad, head, natural) and field-of-view modes (full, restricted). Forward and backward walking tasks were performed before and after VE exposure. The degrees of cybersickness and presence were evaluated using questionnaires, along with the in-session task performance. Spatiotemporal gait measures and their variabilities were calculated for each walking task. The participants experienced less cybersickness with the head and natural controls than with the gamepad. Natural control, based on matching body movements, was associated with the highest degree of presence and best performance. VE navigation using the gamepad showed reduced cadences and increased stride times during postexposure forward-walking tasks. When the VE was presented via the restricted field-of-view mode, increased gait variabilities were observed from backward-walking tasks after VE exposure. Body movement-based navigation controls may alleviate cybersickness. We observed gait adaptation during both ambulation tasks, which was influenced by the navigation control method and field-of-view mode. This study provides the first evidence for gait adaptation during balance-demanding tasks after VE exposure, which is valuable for designing guidelines for virtual reality interactions.

BioMAT: An Open-Source Biomechanics Multi-Activity Transformer for Joint Kinematic Predictions Using Wearable Sensors.

Through wearable sensors and deep learning techniques, biomechanical analysis can reach beyond the lab for clinical and sporting applications. Transformers, a class of recent deep learning models, have become widely used in state-of-the-art artificial intelligence research due to their superior performance in various natural language processing and computer vision tasks. The performance of transformer models has not yet been investigated in biomechanics applications. In this study, we introduce a Biomechanical Multi-activity Transformer-based model, BioMAT, for the estimation of joint kinematics from streaming signals of multiple inertia measurement units (IMUs) using a publicly available dataset. This dataset includes IMU signals and the corresponding sagittal plane kinematics of the hip, knee, and ankle joints during multiple activities of daily living. We evaluated the model's performance and generalizability and compared it against a convolutional neural network long short-term model, a bidirectional long short-term model, and multi-linear regression across different ambulation tasks including level ground walking (LW), ramp ascent (RA), ramp descent (RD), stair ascent (SA), and stair descent (SD). To investigate the effect of different activity datasets on prediction accuracy, we compared the performance of a universal model trained on all activities against task-specific models trained on individual tasks. When the models were tested on three unseen subjects' data, BioMAT outperformed the benchmark models with an average root mean square error (RMSE) of 5.5 ± 0.5°, and normalized RMSE of 6.8 ± 0.3° across all three joints and all activities. A unified BioMAT model demonstrated superior performance compared to individual task-specific models across four of five activities. The RMSE values from the universal model for LW, RA, RD, SA, and SD activities were 5.0 ± 1.5°, 6.2 ± 1.1°, 5.8 ± 1.1°, 5.3 ± 1.6°, and 5.2 ± 0.7° while these values for task-specific models were, 5.3 ± 2.1°, 6.7 ± 2.0°, 6.9 ± 2.2°, 4.9 ± 1.4°, and 5.6 ± 1.3°, respectively. Overall, BioMAT accurately estimated joint kinematics relative to previous machine learning algorithms across different activities directly from the sequence of IMUs signals instead of time-normalized gait cycle data.

There are unique kinematics during locomotor transitions between level ground and stair ambulation that persist with increasing stair grade

Human ambulation is typically characterized during steady-state isolated tasks (e.g., walking, running, stair ambulation). However, general human locomotion comprises continuous adaptation to the varied terrains encountered during activities of daily life. To fill an important gap in knowledge that may lead to improved therapeutic and device interventions for mobility-impaired individuals, it is vital to identify how the mechanics of individuals change as they transition between different ambulatory tasks, and as they encounter terrains of differing severity. In this work, we study lower-limb joint kinematics during the transitions between level walking and stair ascent and descent over a range of stair inclination angles. Using statistical parametric mapping, we identify where and when the kinematics of transitions are unique from the adjacent steady-state tasks. Results show unique transition kinematics primarily in the swing phase, which are sensitive to stair inclination. We also train Gaussian process regression models for each joint to predict joint angles given the gait phase, stair inclination, and ambulation context (transition type, ascent/descent), demonstrating a mathematical modeling approach that successfully incorporates terrain transitions and severity. The results of this work further our understanding of transitory human biomechanics and motivate the incorporation of transition-specific control models into mobility-assistive technology.

Accurate COP Trajectory Estimation in Healthy and Pathological Gait Using Multimodal Instrumented Insoles and Deep Learning Models.

Measuring center-of-pressure (COP) trajectories in out-of-the-lab environments may provide valuable information about changes in gait and balance function related to natural disease progression or treatment in neurological disorders. Traditional equipment to acquire COP trajectories includes stationary force plates, instrumented treadmills, electronic walkways, and insoles featuring high-density force sensing arrays, all of which are expensive and not widely accessible. This study introduces novel deep recurrent neural networks that can accurately estimate dynamic COP trajectories by fusing data from affordable and heterogeneous insole-embedded sensors (namely, an eight-cell array of force sensitive resistors (FSRs) and an inertial measurement unit (IMU)). The method was validated against gold-standard equipment during out-of-the-lab ambulatory tasks that simulated real-world walking. Root-mean-square errors (RMSE) in the mediolateral (ML) and anteroposterior (AP) directions obtained from healthy individuals (ML: 0.51 cm, AP: 1.44 cm) and individuals with neuromuscular conditions (ML: 0.59 cm, AP: 1.53 cm) indicated technical validity. In individuals with neuromuscular conditions, COP-derived metrics showed significant correlations with validated clinical measures of ambulatory function and lower-extremity muscle strength, providing proof-of-concept evidence of the convergent validity of the proposed method for clinical applications.

Evidence That Rating of Perceived Exertion Growth During Fatiguing Tasks is Scalar and Independent of Exercise Mode.

The relationship between the percentage of a fatiguing ambulatory task completed and rating of perceived exertion (RPE) appears to be linear and scalar, with a relatively narrow "window." Recent evidence has suggested that a similar relationship may exist for muscularly demanding tasks. To determine whether muscularly demanding tasks fit within this "ambulatory window," we tested resistance-trained athletes performing bench press and leg press with different loadings predicted to allow 5, 10, 20, and 30 repetitions and measured RPE (category ratio scale) at the end of the concentric action for each repetition. There was a regular, and strongly linear, pattern of growth of RPE for both bench press (r = .89) and leg press (r = .90) during the tasks that allowed 5.2 (1.2), 11.6 (1.9), 22.7 (2.0), and 30.8 (3.2) repetitions for bench press and 5.5 (1.5), 11.4 (1.6), 20.2 (3.0), and 32.4 (4.2) repetitions for leg press, respectively. The path of the RPE growth versus percentage task fit within the window evident for ambulatory tasks. The results suggest that the RPE versus percentage task completed relationship is scalar, relatively linear, and apparently independent of exercise mode.

Evaluating Electromyography and Sonomyography Sensor Fusion to Estimate Lower-Limb Kinematics Using Gaussian Process Regression

Research on robotic lower-limb assistive devices over the past decade has generated autonomous, multiple degree-of-freedom devices to augment human performance during a variety of scenarios. However, the increase in capabilities of these devices is met with an increase in the complexity of the overall control problem and requirement for an accurate and robust sensing modality for intent recognition. Due to its ability to precede changes in motion, surface electromyography (EMG) is widely studied as a peripheral sensing modality for capturing features of muscle activity as an input for control of powered assistive devices. In order to capture features that contribute to muscle contraction and joint motion beyond muscle activity of superficial muscles, researchers have introduced sonomyography, or real-time dynamic ultrasound imaging of skeletal muscle. However, the ability of these sonomyography features to continuously predict multiple lower-limb joint kinematics during widely varying ambulation tasks, and their potential as an input for powered multiple degree-of-freedom lower-limb assistive devices is unknown. The objective of this research is to evaluate surface EMG and sonomyography, as well as the fusion of features from both sensing modalities, as inputs to Gaussian process regression models for the continuous estimation of hip, knee and ankle angle and velocity during level walking, stair ascent/descent and ramp ascent/descent ambulation. Gaussian process regression is a Bayesian nonlinear regression model that has been introduced as an alternative to musculoskeletal model-based techniques. In this study, time-intensity features of sonomyography on both the anterior and posterior thigh along with time-domain features of surface EMG from eight muscles on the lower-limb were used to train and test subject-dependent and task-invariant Gaussian process regression models for the continuous estimation of hip, knee and ankle motion. Overall, anterior sonomyography sensor fusion with surface EMG significantly improved estimation of hip, knee and ankle motion for all ambulation tasks (level ground, stair and ramp ambulation) in comparison to surface EMG alone. Additionally, anterior sonomyography alone significantly improved errors at the hip and knee for most tasks compared to surface EMG. These findings help inform the implementation and integration of volitional control strategies for robotic assistive technologies.

Prolonged unloading of the cardiovascular system during bedrest and spaceflight weakens neural coupling between blood pressure and heart rate

Impaired baroreflex control following bedrest and spaceflight severely affects rate of recovery and return to ambulatory tasks. Post-bedrest/spaceflight orthostatic intolerance remains a major concern for bedrest confined older patients and for astronauts on long-duration missions. Here, we investigated how baroreflex changes following cardiovascular unloading were associated with autonomic neural coupling between blood pressure and heart rate in returning astronauts and in healthy participants who had undergone prolonged bedrest. Cardiovascular data collected from 27 shuttle astronauts (8–16 days) were compared with 19 head-down tilt bedrest (HDBR, 60 days) volunteers. In each group, beat-by-beat systolic blood pressure (SBP) and heart period (R-to-R intervals, RR) were collected simultaneously during a supine-to-stand test (5–10 min) conducted around 10 days before launch or HDBR and within 120 min of landing or exiting HDBR. Indices characterizing interaction time (fraction time active), response gain (gain), and control directionality (causality) between SBP and RR (SBP→RR: neural reflex, and RR→SBP: mechanical coupling) during standing were derived from the beat-by-beat data. Following spaceflight, cardiac baroreflex fraction time active was reduced along with neural reflex causality, with much larger reductions seen with HDBR. No change in mechanical causality pre-post flight or HDBR was observed; however, comparison of male and female astronauts indicated that neural and mechanical causality were both lower in females. Altogether, these findings suggest that changes in blood pressure regulation after bedrest and spaceflight are primarily reflex/neurally mediated and provides a target for therapeutic approaches to cardiovascular deconditioning.

Predicting biological joint moment during multiple ambulation tasks

Combining machine learning models with wearable sensing provides a key technique for understanding the biological effort, creating an alternative to inverse dynamics based on motion capture. In this study, we demonstrate a novel approach to not only estimate but predict the joint moment in advance for multiple ambulation modes. By combining electromyography (EMG), inertial measurement units (IMU), and electrogoniometers, we enable the prediction of the joint moment only from wearable sensors. We performed a forward feature selection to determine the best feature sets for different anticipation times of the intended moment generated at the hip, knee, and ankle, encompassing level walking on a treadmill and ascent/descent of stairs and ramps. We show that wearable sensors can predict the joint moment with an MAE of 0.06 ± 0.02 Nm/kg for direct estimation and an MAE of 0.10 ± 0.04 Nm/kg when predicting 150 ms in advance, corresponding to an MAE within 9.2% of the joint moment range. We found that the hip moment had a significantly lower error than the knee and ankle when anticipating the joint moment (Bonferroni test, p < 0.05). The accurate estimation of the joint moment could monitor user activity to reduce risk factors and inform the control of exoskeletons.

Cognitive correlates of dual tasking costs on the timed up and go test in Parkinson disease

IntroductionDual tasking impairments are an increasingly recognized contributor to falls in Parkinson disease (PD) and may be a promising therapeutic target for PD fall prevention trials. Depending on the context, ambulatory dual tasking difficulties may be caused by different types of neurocognitive impairments. MethodsWe performed a cross-sectional analysis of 21 participants with PD. All participants underwent detailed neuropsychological testing that was quantified using normative z-scores. All participants completed the 3-meter timed up and go test (TUG), with and without a dual tasking assignment. Biomechanistic properties of the TUG were quantified using APDM wearable OPAL sensors. We explored correlations between dual tasking cost (DTC) in 1) total TUG duration, 2) Sit-to-stand duration, 3) Stand-to-sit duration, and 4) turn velocity. ResultsImpaired total DTC in the TUG correlated inversely with global cognitive performance measured using the Montreal Cognitive Assessment (MoCA) (r = −0.4649, p = 0.0337). Sit-to-stand DTC impairments correlated inversely with processing speed on the WAIS-IV Coding (r = −0.5762, p = 0.0063), semantic fluency (r = −0.5100, p = 0.0182) and learning and memory on the Hopkins Verbal Learning Test-Revised total recall (r = −0.5502, p = 0.0098). Impaired stand-to-sit DTC function corelated inversely with visuospatial cognitive function on the Benton Judgement of Line Orientation (JOLO) test (r = −0.5181, p = 0.0161). ConclusionsThe link between dual tasking and fall risk in PD may be caused by cognitive features other than executive dysfunction and may vary based on the ambulatory task in question. These findings shed light on the cognitive contributions to falls in PD.

Is Wrist Accelerometry Suitable for Threshold Scoring? A Comparison of Hip-Worn and Wrist-Worn ActiGraph Data in Low-Active Older Adults With Obesity.

Hip- and wrist-worn ActiGraph accelerometers are widely used in research on physical activity as they offer an objective assessment of movement intensity across the day. Herein we characterize and contrast key structured physical activities and common activities of daily living via accelerometry data collected at the hip and wrist from a sample of community-dwelling older adults. Low-active, older adults with obesity (age 60+ years) were fit with an ActiGraph GT3X+ accelerometer on their nondominant wrist and hip before completing a series of tasks in a randomized order, including sitting/standing, sweeping, folding laundry, stair climbing, ambulation at different intensities, and cycling at different intensities. Participants returned a week later and completed the tasks once again. Vector magnitude counts/second were time-matched during each task and then summarized into counts/minute (CPM). Monitors at both wear locations similarly characterized standing, sitting, and ambulatory tasks. A key finding was that light home chores (sweeping, folding laundry) produced higher and more variable CPM values than fast walking via wrist ActiGraph. Regression analyses revealed wrist CPM values were poor predictors of hip CPM values, with devices aligning best during fast walking (R2 = 0.25) and stair climbing (R2 = 0.35). As older adults spend a considerable portion of their day in nonexercise activities of daily living, researchers should be cautious in the use of simply acceleration thresholds for scoring wrist-worn accelerometer data. Methods for better classifying wrist-worn activity monitor data in older adults are needed.

The Incremental Shuttle Walk Test

The Incremental Shuttle Walk Test

Hamstrings Neuromuscular Function After Anterior Cruciate Ligament Reconstruction: A Systematic Review and Meta-Analysis.

Hamstrings neuromuscular function is a crucial component of functional movement, and changes after anterior cruciate ligament (ACL) injury contribute to risk factors for secondary injury and long-term sequelae. To effectively treat muscular impairments, an accurate understanding of hamstrings neuromuscular function in patients with ACL reconstruction (ACLR) is needed. A systematic review and meta-analysis were undertaken to describe and quantify hamstrings neuromuscular function in individuals with ACLR compared to controls. We searched PubMed, Web of Science, SPORTDiscus, CINAHL, and EBSCOhost databases in October of 2020 for studies evaluating the difference between hamstrings electromyography (EMG) between individuals with ACLR and controls. Two independent reviewers assessed each paper for inclusion and quality. Means and standard deviations were extracted from each included study to allow random-effect size (ES) meta-analysis calculations for comparison of results. Thirty-four studies were included for final review. From these, 5 categories of neuromuscular outcomes were identified, and studies were grouped accordingly: (1) muscle activation levels (EMG amplitude), (2) co-activation, (3) onset timing, (4) electromechanical delay, and (5) time-to-peak activity. Moderate to strong evidence indicates that individuals with ACLR demonstrate higher hamstrings EMG amplitude (normalized to % maximum voluntary isometric contraction) and hamstrings-to-quadriceps co-activation during gait and stair ambulation compared to controls. In addition, there was moderate evidence of longer electromechanical delay during knee flexion and greater hamstrings-to-quadriceps co-activation during knee extension compared to controls. Greater hamstrings EMG amplitude and co-activation during gait and ambulation tasks and longer electromechanical delay of the hamstrings in individuals with ACLR align with clinical impairments following ACLR and have implications for re-injury risk and long-term joint health, thus warranting attention in rehabilitation.

Speed Adaptable Prosthetic Foot: Concept Description, Prototyping and Initial User Testing.

This article presents a novel design of a prosthetic foot that features adaptable stiffness that changes according to the speed of ankle motion. The motivation is the natural graduation in stiffness of a biological ankle over a range of ambulation tasks. The device stiffness depends on rate of movement, ranging from a dissipating support at very slow walking speed, to efficient energy storage and return at normal walking speed. The objective here is to design a prosthetic foot that provides a compliant support for slow ambulation, without sacrificing the spring-like energy return beneficial in normal walking. The design is a modification of a commercially available foot and employs material properties to provide a change in stiffness. The velocity dependent properties of a non-Newtonian working fluid provide the rate adaptability. Material properties of components allow for a geometry shift that results in a coupling action, affecting the stiffness of the overall system. The function of an adaptive coupling was tested in linear motion. A prototype prosthetic foot was built, and the speed dependent stiffness measured mechanically. Furthermore, the prototype was tested by a user and body kinematics measured in gait analysis for varying walking speed, comparing the prototype to the original foot model (non-modified). Mechanical evaluation of stiffness shows increase in stiffness of about 60% over the test range and 10% increase between slow and normal walking speed in user testing.

Continuous locomotion mode classification using a robotic hip exoskeleton.

Human augmentation through robotic exoskeleton technology can enhance the user's mobility for a wide range of ambulation tasks. This is done by providing assistance that is in line with the user's movement during different locomotion modes (e.g., ramps and stairs). Several machine learning techniques have been applied to classify such tasks on lower limb prostheses, but these strategies have not been applied extensively to exoskeleton systems which often rely on similar control inputs. Additionally, conventional methods often identify modes at a discrete time during the gait cycle which can delay the corresponding assistance to the user and potentially reduce overall exoskeleton benefit. We developed a gait phase-based Bayesian classifier that can classify five ambulation modes continuously throughout the gait cycle using only mechanical sensors on the device. From our five able-bodied subject experiment with a robotic hip exoskeleton, we found that implementing multiple models within the gait cycle can reduce the classification error rate by 35% compared to using a single model (p < 0.05). Furthermore, we found that utilizing bilateral sensor information can reduce the error by 43% compared to using a unilateral information (p < 0.05). Our study findings provide valuable information for future exoskeleton developers to utilize different on-board mechanical sensors to enhance mode classification for a faster update rate in the controller and provide more natural and seamless exoskeleton assistance between locomotion modes.