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Patients with peripheral artery disease with intermittent claudication (PAD-IC) have altered gait variability from the first step they take, well before the onset of claudication pain. The mechanisms underlying these gait alterations are poorly understood. To determine the effect of reduced blood flow on gait variability by comparing healthy older controls and patients with PAD-IC. We also determined the diagnostic value of gait variability parameters to identify the presence of PAD. A cross-sectional cohort design was used. Thirty healthy older controls and thirty patients with PAD-IC walked on a treadmill at their self-selected speed in pain free walking (normal walking for healthy older controls; prior to claudication onset for PAD) and reduced blood flow (post vascular occlusion with thigh tourniquet for healthy older controls; pain for PAD) conditions. Gait variability was assessed using the largest Lyapunov exponent, approximate entropy, standard deviation, and coefficient of variation of ankle, knee, and hip joints range of motion. Receiver operating characteristics curve analyses of the pain free walking condition were performed to determine the optimal cut-off values for separating individuals with PAD-IC from those without PAD-IC. Patients with PAD-IC have increased amount of variability for knee and hip ranges of motion compared with the healthy older control group. Regarding the main effect of condition, reduced blood flow demonstrated increased amount of variability compared with pain free walking. Significant interactions between group and condition at the ankle show increased values for temporal structure of variability, but a similar amount of variability in the reduced blood flow condition. This demonstrates subtle interactions in the movement patterns remain distinct between PAD-IC versus healthy older controls during the reduced blood flow condition. A combination of gait variability parameters correctly identifies PAD-IC disease 70% of the time or more. Gait variability is affected both by PAD and by the mechanical induction of reduced blood flow. Gait variability parameters have potential diagnostic ability, as some measures had 90.0% probability of correctly identifying patients with PAD-IC.

Peripheral artery disease (PAD) is characterized by reduced blood flow to the extremities due to atherosclerosis. Studies report impaired gait mechanics in patients with lower extremity PAD. We hypothesized that revascularization surgery would improve gait mechanics when quantified by net lower limb joint work across the stance phase of walking. We performed gait analyses in 35 patients with PAD and 35 healthy, older adults. Patients with PAD performed a walking protocol prior to and six months following revascularization surgery. Healthy adults only took part in a single walking session. Lower limb joint powers were calculated using inverse dynamics and were integrated across early, middle, and late stance phases to determine the work performed during each phase (J kg−1). The work mechanical ratio between positive-producing and negative-producing phases of stance was calculated for each lower-limb joint. Self-selected walking speed significantly increased from 1.13 ± 0.2 ms−1 to 1.26 ± 0.18 ms−1 in patients following revascularization (p < 0.001). We observed a significant decrease in positive late stance work (p < 0.001) in conjunction with more negative work during early stance (p < 0.001) in patients following revascularization. Revascularization surgery led to faster walking without an increase in the ankle joint’s mechanical ratio. Our results suggest faster walking was achieved via work done at the hip rather than the ankle. These findings suggest that additional therapies that facilitate the restoration of muscle, tissue, and nervous system damage caused by years of having reduced blood flow to the limbs might still be beneficial following revascularization.

(1) Background: Peripheral artery disease (PAD) and related conditions significantly impair walking ability. Previous studies demonstrated that passive lightweight exosuits can improve walking biomechanics. However, most of these devices focus on assisting hip flexion. The aim of this study was to investigate the effects of flexion and extension assistance on joint kinetics and muscle activation. We hypothesized that there would be an optimal combination of flexion and extension assistance for measured parameters. (2) Methods: Four patients with PAD and six healthy individuals walked on a treadmill while wearing a passive exosuit with adjustable hip flexion and extension assistance. Lower limbs’ power, moment, and muscle activation were recorded. (3) Results: We found that passive hip assistance effectively reduced hip kinetics in both healthy and PAD participants. We also found different effects between the groups, with the PAD group utilizing the exosuit to reduce plantarflexion kinetics and gastrocnemius activity. (4) Conclusions: These findings suggest that patients with PAD can leverage the exosuit to ameliorate impairment-specific deficits. Future research should explore more real-world applicability of passive exosuits.

Different levels of arterial occlusive disease (aortoiliac, femoropopliteal, multi-level disease) can produce claudication symptoms in different leg muscle groups (buttocks, thighs, calves) in patients with peripheral artery disease (PAD). We tested the hypothesis that different locations of occlusive disease uniquely affect the muscles of PAD legs and produce distinctive patterns in the way claudicating patients walk. Ninety-seven PAD patients and 35 healthy controls were recruited. PAD patients were categorized to aortoiliac, femoropopliteal and multi-level disease groups using computerized tomographic angiography. Subjects performed walking trials both pain-free and during claudication pain and joint kinematics, kinetics, and spatiotemporal parameters were calculated to evaluate the net contribution of the calf, thigh and buttock muscles. PAD patients with occlusive disease affecting different segments of the arterial tree (aortoiliac, femoropopliteal, multi-level disease) presented with symptoms affecting different muscle groups of the lower extremity (calves, thighs and buttocks alone or in combination). However, no significant biomechanical differences were found between PAD groups during the pain-free conditions with minimal differences between PAD groups in the claudicating state. All statistical differences in the pain-free condition occurred between healthy controls and one or more PAD groups. A discriminant analysis function was able to adequately predict if a subject was a control with over 70% accuracy, but the function was unable to differentiate between PAD groups. In-depth gait analyses of claudicating PAD patients indicate that different locations of arterial disease produce claudication symptoms that affect different muscle groups across the lower extremity but impact the function of the leg muscles in a diffuse manner generating similar walking impairments.

Peripheral artery disease (PAD) manifests from atherosclerosis, which limits blood flow to the legs and causes changes in muscle structure and function, and in gait performance. PAD is underdiagnosed, which delays treatment and worsens clinical outcomes. To overcome this challenge, the purpose of this study is to develop machine learning (ML) models that distinguish individuals with and without PAD. This is the first step to using ML to identify those with PAD risk early. We built ML models based on previously acquired overground walking biomechanics data from patients with PAD and healthy controls. Gait signatures were characterized using ankle, knee, and hip joint angles, torques, and powers, as well as ground reaction forces (GRF). ML was able to classify those with and without PAD using Neural Networks or Random Forest algorithms with 89% accuracy (0.64 Matthew’s Correlation Coefficient) using all laboratory-based gait variables. Moreover, models using only GRF variables provided up to 87% accuracy (0.64 Matthew’s Correlation Coefficient). These results indicate that ML models can classify those with and without PAD using gait signatures with acceptable performance. Results also show that an ML gait signature model that uses GRF features delivers the most informative data for PAD classification.

This paper demonstrates the value of a framework for processing data on body acceleration as a uniquely valuable tool for diagnosing diseases that affect gait early. As a case study, we used this model to identify individuals with peripheral artery disease (PAD) and distinguish them from those without PAD. The framework uses acceleration data extracted from anatomical reflective markers placed in different body locations to train the diagnostic models and a wearable accelerometer carried at the waist for validation. Reflective marker data have been used for decades in studies evaluating and monitoring human gait. They are widely available for many body parts but are obtained in specialized laboratories. On the other hand, wearable accelerometers enable diagnostics outside lab conditions. Models trained by raw marker data at the sacrum achieve an accuracy of 92% in distinguishing PAD patients from non-PAD controls. This accuracy drops to 28% when data from a wearable accelerometer at the waist validate the model. This model was enhanced by using features extracted from the acceleration rather than the raw acceleration, with the marker model accuracy only dropping from 86 to 60% when validated by the wearable accelerometer data.

Understanding metabolic cost through biomechanical data, including ground reaction forces (GRFs) and joint moments, is vital for health, sports, and rehabilitation. The long stabilization time (2–5 min) of indirect calorimetry poses challenges in prolonged tests. This study investigated using artificial neural networks (ANNs) to predict metabolic costs from the GRF and joint moment time series. Data from 20 participants collected over 270 walking trials, including the GRF and joint moments, formed a detailed dataset. Two ANN models were crafted, netGRF for the GRF and netMoment for joint moments, and both underwent training, validation, and testing to validate their predictive accuracy for metabolic cost. NetGRF (six hidden layers, two input delays) showed significant correlations: 0.963 (training), 0.927 (validation), 0.883 (testing), p < 0.001. NetMoment (three hidden layers, one input delay) had correlations of 0.920 (training), 0.956 (validation), 0.874 (testing), p < 0.001. The models’ low mean squared errors reflect their precision. Using Partial Dependence Plots, we demonstrated how gait cycle phases affect metabolic cost predictions, pinpointing key phases. Our findings show that the GRF and joint moments data can accurately predict metabolic costs via ANN models, with netGRF being notably consistent. This emphasizes ANNs’ role in biomechanics as a crucial method for estimating metabolic costs, impacting sports science, rehabilitation, assistive technology development, and fostering personalized advancements.

We hypothesized that slow crystalloid resuscitation would result in less blood loss and a smaller hemoglobin decrease compared to a rapid resuscitation during uncontrolled hemorrhage. Anesthetized, splenectomized domestic swine underwent hepatic lobar hemitransection. Lactated Ringers was given at 150 or 20 mL/min IV (rapid vs. slow, respectively, N = 12 per group; limit of 100 mL/kg). Primary endpoints were blood loss and serum hemoglobin; secondary endpoints included survival, vital signs, coagulation parameters, and blood gases. The slow group had a less blood loss (1.6 vs. 2.7 L, respectively) and a higher final hemoglobin concentration (6.0 vs. 3.4 g/dL). Using a fixed volume of crystalloid resuscitation in this porcine model of uncontrolled intraabdominal hemorrhage, a slow IV infusion rate produced less blood loss and a smaller hemoglobin decrease compared to rapid infusion.

Patients with peripheral artery disease (PAD) have significantly reduced lower extremity muscle strength compared with healthy individuals as measured during isolated, single plane joint motion by isometric and isokinetic strength dynamometers. Alterations to the force contribution of muscles during walking caused by PAD are not well understood. Therefore, this study used simulations with PAD biomechanics data to understand lower extremity muscle functions in patients with PAD during walking and to compare that with healthy older individuals. A total of 12 patients with PAD and 10 age-matched healthy older controls walked across a 10-meter pathway with reflective markers on their lower limbs. Marker coordinates and ground reaction forces were recorded and exported to OpenSim software to perform gait simulations. Walking velocity, joint angles, muscle force, muscle power, and metabolic rate were calculated and compared between patients with PAD and healthy older controls. Our results suggest that patients with PAD walked slower with less hip extension during propulsion. Significant force and power reductions were observed in knee extensors during weight acceptance and in plantar flexors and hip flexors during propulsion in patients with PAD. The estimated metabolic rate of walking during stance was not different between patients with PAD and controls. This study is the first to analyze lower limb muscular responses during walking in patients with PAD using the OpenSim simulation software. The simulation results of this study identified important information about alterations to muscle force and power during walking in those with PAD.

Blocked or narrowed arteries restrict blood flow to the lower limbs, commonly leading to peripheral artery disease (PAD). Patients with PAD have been shown to have increased gait variability, which may contribute to higher rates of falls and worsen functional outcomes. Surgical revascularization seeks to restore blood flow to the legs, but it is unknown if this restoration enhances limb function. This study investigated whether gait variability changes in patients with PAD after revascularization surgery. Thirty-three patients with PAD exhibiting claudication symptoms were recruited for the study. Kinematic data were recorded using a motion capture system while the patients walked on a treadmill following a progressive treadmill protocol, both before and after undergoing revascularization surgery. Angular sagittal movements’ linear and nonlinear variability in the lower limbs were measured and compared before and after surgery across the ankle, knee, and hip joints. Following revascularization surgery, knee joint sample entropy (SampEn) decreased, suggesting improved gait regularity. Furthermore, the hip range of motion (ROM) significantly decreased, whereas the knee ROM significantly increased. The ankle joint showed significantly greater changes in the Lyapunov Exponent (LyE) relative to the pre-exercise condition compared with the hip and knee joints. No significant differences existed in the linear variability (standard deviation) of the ROM between joints. In individuals with PAD, revascularization surgery considerably increased knee ROM and gait regularity, indicating improved limb function and motor control. However, the ankle ROM remained unchanged, indicating the need for targeted strengthening exercises post-surgery.