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.

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.

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.

Increasing Step Rate Affects Rearfoot Kinematics and Ground Reaction Forces during Running

Relatively high frontal and transverse plane motion in the lower limbs during running have been thought to play a role in the development of some running-related injuries (RRIs). Increasing step rate has been shown to significantly alter lower limb kinematics and kinetics during running. The purpose of this study was to evaluate the effects of increasing step rate on rearfoot kinematics, and to confirm how ground reaction forces (GRFs) are adjusted with increased step rate. Twenty runners ran on a force instrumented treadmill while marker position data were collected under three conditions. Participants ran at their preferred pace and step rate, then +5% and +10% of their preferred step rate while being cued by a metronome for three minutes each. Sagittal and frontal plane angles for the rearfoot segment, tibial rotation, and GRFs were calculated during the stance phase of running. Significant decreases were observed in sagittal and frontal plane rearfoot angles, tibial rotation, vertical GRF, and anteroposterior GRF with increased step rate compared with the preferred step rate. Increasing step rate significantly decreased peak sagittal and frontal plane rearfoot and tibial rotation angles. These findings may have implications for some RRIs and gait retraining.

Compensatory Ground Reaction Forces during Scoliotic Gait in Subjects with and without Right Adolescent Idiopathic Scoliosis

Although the asymmetries of scoliotic gait in adolescent idiopathic scoliosis (AIS) groups have been extensively studied, recent studies indicated conflicting results regarding the ground reaction forces (GRFs) during gait in subjects with spinal deformity. The asymmetry during the stance phase might be clarified with three-dimensional (3D) compensations of GRFs between similar characteristics of subjects with and without AIS. The purpose of this study was to compare the normalized 3D GRF differences during the stance phase of gait while considering age, BMI, and Cobb angle between subjects with and without right AIS. There were 23 subjects with right convexity of thoracic idiopathic scoliosis and 22 age- and gender-matched control subjects. All subjects were right upper/lower limb dominant, and the outcome measures included the Cobb angles, normalized GRF, and KAI. The mediolateral (M/L) third peak force on the dominant limb decreased in the AIS group (t = 2.58, p = 0.01). Both groups demonstrated a significant interaction with the 3D indices (F = 5.41, p = 0.02). The post-hoc analysis identified that the M/L plane of asymmetry was significantly different between groups. The Cobb angles were negatively correlated with the vertical asymmetry index (r = −0.45, p = 0.03); however, there was no significant correlation with age (r = −0.10, p = 0.65) or body mass index (r = −0.28, p = 0.20). The AIS group demonstrated decreased GRF in the dominant limb M/L plane of the terminal stance phase. This compensatory motion was confirmed by a significant group difference on the M/L plane of the KAI. This KAI of vertical asymmetry correlated negatively with the Cobb angle. The asymmetric load transmission with compensatory vertical reactions was evident due to abnormal loading in the stance phase. These kinetic compensatory patterns need to be considered with asymmetry on the dominant limb when developing rehabilitation strategies for patients with AIS.

Machine Learning-Based Estimation of Ground Reaction Forces and Knee Joint Kinetics from Inertial Sensors While Performing a Vertical Drop Jump

Nowadays, the use of wearable inertial-based systems together with machine learning methods opens new pathways to assess athletes’ performance. In this paper, we developed a neural network-based approach for the estimation of the Ground Reaction Forces (GRFs) and the three-dimensional knee joint moments during the first landing phase of the Vertical Drop Jump. Data were simultaneously recorded from three commercial inertial units and an optoelectronic system during the execution of 112 jumps performed by 11 healthy participants. Data were processed and sorted to obtain a time-matched dataset, and a non-linear autoregressive with external input neural network was implemented in Matlab. The network was trained through a train-test split technique, and performance was evaluated in terms of Root Mean Square Error (RMSE). The network was able to estimate the time course of GRFs and joint moments with a mean RMSE of 0.02 N/kg and 0.04 N·m/kg, respectively. Despite the comparatively restricted data set and slight boundary errors, the results supported the use of the developed method to estimate joint kinetics, opening a new perspective for the development of an in-field analysis method.

Ground Reaction Forces during Vertical Hops Are Correlated with the Number of Supervised Physiotherapy Visits after Achilles Tendon Surgery

The objective of this study was to assess the effectiveness of, and the correlation between, an average of 42 supervised physiotherapy (SVPh) visits for the vertical ground reaction forces component (vGRF) using ankle hops during two- and one-legged vertical hops (TLH and OLH, respectively), six months after the surgical suturing of the Achilles tendon using the open method (SSATOM) via Keesler’s technique. Hypothesis: Six months of supervised physiotherapy with a higher number of visits (SPHNVs) was positively correlated with higher vGRF values during TLH and OLH. Group I comprised male patients (n = 23) after SSATOM (SVPh x = 42 visits), and Group II comprised males (n = 23) without Achilles tendon injuries. In the study groups, vGRF was measured during TLH and OLH in the landing phase using two force plates. The vGRF was normalized to the body mass. The limb symmetry index (LSI) of vGRF values was calculated. The ranges of motion of the foot and circumferences of the ankle joint and shin were measured. Then, 10 m unassisted walking, the Thompson test, and pain were assessed. A parametric test for dependent and independent samples, ANOVA and Tukey’s test for between-group comparisons, and linear Pearson’s correlation coefficient calculations were performed. Group I revealed significantly lower vGRF values during TLH and OLH for the operated limb and LSI values compared with the right and left legs in Group II (p ≤ 0.001). A larger number of visits correlates with higher vGRF values for the operated limb during TLH (r = 0.503; p = 0.014) and OLH (r = 0.505; p = 0.014). An average of 42 SVPh visits in 6 months was insufficient to obtain similar values of relative vGRF and their LSI during TLH and OLH, but the hypothesis was confirmed that SPHNVs correlate with higher relative vGRF values during TLH and OLH in the landing phase.