Indices of Loading and Propulsive Ability in the Gait of Patients With Chronic Stroke With Equinus Foot Deviation: A Correlation Study

In literature, indices of overall walking ability that are based on ground reaction forces have been proposed because of their ease of administration with patients. In this study, we analyzed the correlation between the indices of dynamic loading and propulsion ability of 40 chronic hemiparetic post-stroke patients with equinus foot deviation and a set of clinical assessments of ankle joint deviations and walking ability. Ankle passive and active range of motion (ROM) and triceps surae spasticity were considered, along with walking speed and three complementary scales of walking ability focusing respectively on the need for assistance on functional mobility, including balance and transfers, and the limitation in social participation. The correlation between the ground reaction force-based indices and both clinical and functional variables was carried out using the non-parametric Spearman correlation coefficient. Both indices were correlated to 8 of the 10 investigated variables, thus supporting their use. In particular, the dynamic propulsive ability was correlated with all functional scales (rho = 0.5, p < 0.01), and has the advantage of being a continuous variable. Among clinical assessments, limited ankle ROM affected walking ability the most, while spasticity did not. Since the acquisition of ground reaction forces does not require any patient prepping, the derived indices can be used during the rehabilitation period to quickly detect small improvements that, over time, might lead to the broad changes detectable by clinical scales, as well as to immediately highlight the lack of these improvements, thus suggesting adjustments to the ongoing rehabilitation approach.

Effects of Running on Sand vs. Stable Ground on Kinetics and Muscle Activities in Individuals With Over-Pronated Feet

Background: In terms of physiological and biomechanical characteristics, over-pronation of the feet has been associated with distinct muscle recruitment patterns and ground reaction forces during running.Objective: The aim of this study was to evaluate the effects of running on sand vs. stable ground on ground-reaction-forces (GRFs) and electromyographic (EMG) activity of lower limb muscles in individuals with over-pronated feet (OPF) compared with healthy controls.Methods: Thirty-three OPF individuals and 33 controls ran at preferred speed and in randomized-order over level-ground and sand. A force-plate was embedded in an 18-m runway to collect GRFs. Muscle activities were recorded using an EMG-system. Data were adjusted for surface-related differences in running speed.Results: Running on sand resulted in lower speed compared with stable ground running (p < 0.001; d = 0.83). Results demonstrated that running on sand produced higher tibialis anterior activity (p = 0.024; d = 0.28). Also, findings indicated larger loading rates (p = 0.004; d = 0.72) and greater vastus medialis (p < 0.001; d = 0.89) and rectus femoris (p = 0.001; d = 0.61) activities in OPF individuals. Controls but not OPF showed significantly lower gluteus-medius activity (p = 0.022; d = 0.63) when running on sand.Conclusion: Running on sand resulted in lower running speed and higher tibialis anterior activity during the loading phase. This may indicate alterations in neuromuscular demands in the distal part of the lower limbs when running on sand. In OPF individuals, higher loading rates together with greater quadriceps activity may constitute a proximal compensatory mechanism for distal surface instability.

Analysis of Running in Wilson’s Disease

Aim of the study was to analyze the ability of long-term treated patients with Wilson’s disease (WD) to run a distance of 40 m. 30 WD-patients from a single center were consecutively recruited. All patients were able to walk a distance of 40 m without walking aids. Vertical ground reaction forces (GRF-curves) were analyzed by means of an Infotronic® gait analysis system (CDG®) and correlated with clinical and laboratory findings. Results of the WD-patients were compared to those of an age-and sex-matched control group. 25 of the 30 WD-patients were able to run. Patients being unable to run had a significantly (p < 0.03) higher non-motor score. In comparison to the controls speed of running was significantly (p < 0.02) reduced in WD-patients. Their duration of foot contact on the ground lasted significantly (p < 0.05) longer. Running was more irregular in WD and the variability of times to peak of the GRF-curves was significantly (p < 0.05) increased. All running parameters extracted from the GRF-curves of the CDG® did not correlate with severity of WD. Cadence of running was significantly (p < 0.03) negatively correlated with serum liver enzyme levels. Running appears to be rather unimpaired in long-term treated WD, only 16% of the 30 WD-patients were unable to run. This knowledge is highly relevant for the patient management, but because of the missing correlation with severity of WD, analysis of running is of minor importance for monitoring WD-therapy.

Predicting continuous ground reaction forces from accelerometers during uphill and downhill running: a recurrent neural network solution

Background Ground reaction forces (GRFs) are important for understanding human movement, but their measurement is generally limited to a laboratory environment. Previous studies have used neural networks to predict GRF waveforms during running from wearable device data, but these predictions are limited to the stance phase of level-ground running. A method of predicting the normal (perpendicular to running surface) GRF waveform using wearable devices across a range of running speeds and slopes could allow researchers and clinicians to predict kinetic and kinematic variables outside the laboratory environment. Purpose We sought to develop a recurrent neural network capable of predicting continuous normal (perpendicular to surface) GRFs across a range of running speeds and slopes from accelerometer data. Methods Nineteen subjects ran on a force-measuring treadmill at five slopes (0°, ±5°, ±10°) and three speeds (2.5, 3.33, 4.17 m/s) per slope with sacral- and shoe-mounted accelerometers. We then trained a recurrent neural network to predict normal GRF waveforms frame-by-frame. The predicted versus measured GRF waveforms had an average ± SD RMSE of 0.16 ± 0.04 BW and relative RMSE of 6.4 ± 1.5% across all conditions and subjects. Results The recurrent neural network predicted continuous normal GRF waveforms across a range of running speeds and slopes with greater accuracy than neural networks implemented in previous studies. This approach may facilitate predictions of biomechanical variables outside the laboratory in near real-time and improves the accuracy of quantifying and monitoring external forces experienced by the body when running.

Static analysis of multi-support spindle shafts with nonlinear elastic bearings

In this paper a mathematical model and computational tool are developed for the static analysis of multi-bearing spindle shafts with nonlinear elastic supports. Based on the Timoshenko beam theory a resolving system of equations is obtained that takes into account the nonlinear dependence of the bearing stiffness on the reaction forces acting upon them. A solution method is proposed and appropriate software is developed that implements the static analysis of multi-support spindle shafts with non-linearly elastic bearings in MATLAB environment. Key words: spindle, shaft, nonlinear elastic support, multi-bearing, nonlinear elastic stiffness, Timoshenko beam.

Foot Insole Pressure Distribution during the Golf Swing in Professionals and Amateur Players

There are numerous articles that study the ground reaction forces during the golf swing, among which only a few analyze the pressure pattern distributed on the entire surface of the foot. The current study compares the pressure patterns on the foot insoles of fifty-five golfers, from three different performance levels, playing swings with driver and 5-iron clubs in the driving range. Five swings were selected for each club. During each swing, ultra-thin insole sensors (4 sensors/cm^2) measure foot pressure at the frequency of 100 Hz. To perform statistical analysis, insole sensors are clustered to form seven areas, with the normalized pressure of each area being our dependent variable. A video camera was used to label the five key instants of the swing. Statistical analysis demonstrates a significant difference between the pressure distribution pattern of the left and right feet for both driver and 5-iron. However, the pressure distribution pattern remains almost the same when switching the club type from 5-iron to driver. We have also observed that there are significant differences between the pattern of professionals and players with medium and high handicap. The obtained pattern agrees with the principle of weight transfer with a different behavior between the medial and lateral areas of the foot.

Using Ground Reaction Forces To Predict Fatigue In Treadmill Running: A Machine Learning Approach

The analysis of running patterns, especially those associated with fatigue, can help specialists in designing more efficient workouts and preventing injuries in high-performance sports. However, classifying running patterns is not trivial for humans. An interesting alternative is to use Machine Learning methods, such as Artificial Neural Networks (ANNs), to classify running patterns. In this work, ground reaction forces are measured by sensors coupled to the base of a low-cost open-source treadmill. ANNs are used to classify the force signals and to indicate the occurrence of fatigue. Different features, extracted from the force signals, are proposed and investigated. A Genetic Algorithm (GA) is used to select the best features. The experimental results indicate that the ANN is able to classify the running patterns with good accuracy. In addition, some features selected by the GA provide important information regarding the identification of fatigue in treadmill running.

Are All Unloader Braces Created Equal?: Recommendations for Evidence-Based Implementation of Unloader Braces for Patients with Unicompartmental Knee OA

Unicompartmental knee osteoarthritis (UKOA) is a complex issue that is estimated to affect roughly 28% of patients with knee OA, and can result in severe cartilage degeneration, meniscus deficiency, and concomitant varus or valgus malalignment. This malalignment results in abnormally high joint reaction forces in the affected compartment, which can elicit pain, cause dysfunction, and exacerbate joint degradation. For more than two decades, the use of knee unloader braces has been advocated as a cost-effective option for symptomatic management of UKOA.During bipedal ambulation with a normal lower extremity mechanical axis, ground reaction forces create a knee adduction moment (KAM) such that the medial compartment of the knee experiences approximately 60% of joint loading and the lateral compartment experiences approximately 40% of joint loading. UKOA disrupts the mechanical axis, altering KAM and joint loading and causing pain, dysfunction, and disease progression. In theory, knee unloader braces were designed to mitigate the symptoms of UKOA by normalizing KAM via shifts in the lever arms about the knee. However, studies vary, and suggest that push-mechanism knee unloaders do not consistently provide significant biomechanical benefits for medial or lateral UKOA. Current evidence suggests that pull-mechanism unloaders may be more effective, though contrasting data have also been reported, such that further validation is necessary. The purpose of our study was to synthesize current best evidence for use of knee unloader braces for management of UKOA to suggest evidence-based best practices as well as gaps in knowledge to target for future studies. Unloader bracing for patients with UKOA appears to be a cost-effective treatment option for patients with medial UKOA who have insurance coverage. Pull-mechanism unloader bracing should be considered in conjunction with other nonoperative management therapies for those who are willing to adhere to consistent brace use for weight-bearing activities.

Overcoming a 'forbidden phenotype': The parrot's head supports, propels, and powers tripedal locomotion

No vertebrate, living or extinct, is known to have possessed an odd number of limbs. Despite this ″forbidden phenotype″, gaits that utilize odd numbers of limbs (e.g., tripedalism or pentapedalism) have evolved in both avian and mammalian lineages. Tripedal locomotion is commonly employed by parrots during climbing, who utilize their beaks as an additional support. However, it is unclear whether the beak functions simply as a stabilizing hook, or as a propulsive limb. Here, we present data on kinetics of tripedal climbing in six rosy –faced lovebirds (Agapornis rosiecollis). Our findings demonstrate that parrots utilize cyclical tripedal gaits when climbing and the beak and hindlimbs generate comparable propulsive and tangential substrate reaction forces and power. Propulsive and tangential forces generated by the beak are of equal or greater magnitudes to those forces generated by the forelimbs of humans and non –human primates during vertical climbing. We conclude that the feeding apparatus and neck musculature of parrots has been co–opted to function biomechanically as a third limb during vertical climbing. We hypothesize that this exaptation required substantive alterations to the neuromuscular system including enhanced force–generating capabilities of the neck musculature and modifications to limb central pattern generators.